DETAILED ACTION
Upon review of the last Office action (OA) of June 3, 2026, as per Applicant’s request, it has been determined that claims 3-4, 6-8, 10, and 12 were inadvertently excluded from the last OA. Therefore, a new ground of rejection is warranted to include all pending claims, as indicated below. The previous Office action has been withdrawn. Claims 1-4, 6-8,10, and 12-19 are pending.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 6-7, 10, 12-14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over et al. (US PG Publication No. 20150/036904 A1), hereafter referred to as JEREBKO, in view of Oosawa et al. (US PG Publication No. 2005/0105828 A1), hereafter referred to as Oosawa.
Regarding claim 1, JEREBKO discloses an image processing apparatus (Par. [0009-19]: a reconstruction method and system which considers non-uniformly distribution of lesions in the volume and reconstruction methods… a reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices… invention refers to reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density… apply different reconstruction algorithms for different regions or areas in the volume, depending on the distribution of diagnostically relevant information in the volume to be examined… a method for image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness)… a control unit system for image reconstruction of a three-dimensional digital breast tomosynthesis volume in a plurality of slabs, having a pre-configurable thickness, wherein the control unit is adapted for executing the method mentioned above… a computer program for executing the method mentioned above, when being implemented and running on the control unit system of a three-dimensional tomosynthesis scanner apparatus. The present invention is also directed to a computer program product, comprising code means adapted to execute the method steps according to the method, described above, when loaded into a computer processor) comprising:
a region obtaining unit configured to obtain a first region in a tomographic image included in a three-dimensional image, and a second region [in the tomographic image included in the three-dimensional image] different from the first region (Par. [0010-20]: reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density. It should be possible to apply different reconstruction algorithms for different regions or areas in the volume, depending on the distribution of diagnostically relevant information in the volume to be examined… a method for image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness), comprising the steps of… analyzing a volume in order to detect diagnostically relevant data by determining a type of lesion (like masses, calcifications etc.)… clustering the analyzed volume based on the determined lesion type in at least a first and a second region, wherein the first region has a high degree of diagnostically relevant data and the second region has a low degree of diagnostically relevant data… determining a first reconstruction algorithm for the first region and a second reconstruction algorithm for the second region… system for image reconstruction of a three-dimensional digital breast tomosynthesis volume in a plurality of slabs, having a pre-configurable thickness, wherein the control unit is adapted for executing the method mentioned above… a computer program for executing the method mentioned above, when being implemented and running on the control unit system of a three-dimensional tomosynthesis scanner apparatus. The present invention is also directed to a computer program product, comprising code means adapted to execute the method steps according to the method, described above, when loaded into a computer processor… invention is implemented in hardware or in hardware modules, which may be combined with software modules. The hardware modules are then adapted to perform the functionality of the steps of the method, described above. Accordingly, it is also possible to have a combination of hardware and software modules. The modules are preferably integrated into an existing medical environment, for example into an image acquisition device (CT, x-ray, tomosynthesis apparatus) or in a control unit of such an apparatus; Par. [0024-43]: FIG. 1 is a schematic illustration of a medical tomosynthesis system according to an embodiment of present invention… FIG. 3 is a flowchart of a reconstruction method according to a preferred embodiment of the present invention for reconstructing different volume regions differently… analysis phase is executed before reconstruction of the volume… present invention refers to an image reconstruction method for a three-dimensional digital breast tomosynthesis volume with a plurality of pre-configurable slabs … in the analysis phase the analyzed volume is clustered in different regions, particularly in a first region and in a second region, wherein the first region has a high degree of diagnostically relevant data and wherein the second region has a low degree of diagnostically relevant data… it is possible to determine more than two different regions in the volume, which have (and consist of) different information and which are to be reconstructed differently (for instance with MIP or AIP techniques)… determining and configuring the reconstruction algorithms, wherein different reconstruction algorithms are determined for the first region and for the second region… The regions or the clusters of high and low diagnostically relevant information density may refer to two-dimensional regions or three-dimensional regions in the volume… different reconstruction algorithms may be applied. Particularly, different reconstruction algorithms are executed on the same volume. A first reconstruction algorithm is determined for a first region and a second reconstruction algorithm is determined for the second region (or vice versa); Par. [0061-66]: the analyzed volume is clustered. This means that the three-dimensional volume is classified in different types of clusters or classes. Preferably two clusters are differentiated: a first region or cluster and a second region or cluster. The first region may be defined as having a high degree of diagnostically relevant data (a high density referring to lesions) and wherein the second region has a low degree of diagnostically relevant data (and thus referring to healthy tissue). In other embodiments it is also possible to define more than two regions or clusters, which are going to be distinguished with respect to determining the reconstruction algorithm. With other words, reconstruction is region-specific and lesion-type specific… a first reconstruction algorithm is determined for the first region and a second reconstruction algorithm is going to be determined for the second region or cluster, which has been identified in step 14. As mentioned before, different reconstruction algorithms are executed for the different clusters. For example a maximum intensity projection (MIP technique) could be applied to reconstruct slabs containing calcification clusters and an average intensity projection (AIP technique) may be used to reconstruct slabs through the masses… the identified and determined reconstruction algorithm is applied to the volume clusters according to the before mentioned method steps such that the first region is reconstructed with high resolution in thin slabs and the second region is reconstructed with low resolution and thicker slabs; obtain a first region in a tomographic image included in a three-dimensional image, and a second region [in the tomographic image included in the three-dimensional image] different from the first region (e.g. method and system which considers non-uniformly distribution of lesions in a volume (i.e. a three-dimensional image) acquired by using an image acquisition device, including a three-dimensional tomosynthesis scanner apparatus (i.e. an image processing apparatus), for example, include tomographic image reconstruction methods that apply different reconstruction algorithms for different regions (i.e. first, second, third… Nth different regions, sites, areas, clusters, etc.) in the volume depending on the distribution of diagnostically relevant information in the volume to be examined (i.e. obtain a first region in a tomographic image included in a three-dimensional image, and a second region in the tomographic image included in the three-dimensional image different from the first region), for example, including determining a first reconstruction algorithm for a first region and a second reconstruction algorithm for a second region, in which the first region has a high degree of diagnostically relevant data and the second region has a low degree of diagnostically relevant data, respectively, as indicated above), for example);
a setting unit configured to set a first slab having a first slab thickness for the first region, and a second slab having a second slab thickness, which is different from the first slab thickness, for the second region; and
a generation unit configured to generate a projection image corresponding to the tomographic image using the first slab corresponding to the first region and the second slab corresponding to the second region (Par. [0004-20]: tomosynthesis methods have been developed, which acquire several projections of an object… and thereafter reconstruct the three-dimensional distribution of the detected grey values in a detector by means of a tomography reconstruction algorithm… A sequence of projection views is acquired by the digital detector as the X-ray source is rotated to different angular positions… Anatomical structures or objects at different heights (or depths in the breast) are projected differently at different angles. The subsequent image reconstruction algorithm leads to a stack or a slab of slice images of the different depth layers… One of the usual ways of reducing the amount of data for read and for storage in regular computer tomography is the reconstruction of the volume in thick slices. While the modern computer tomographs are capable of producing images of less than 0.5 mm slice thickness (for example in thoracic or in abdominal images), radiologists often read and analyze three-dimensional images reconstructed as thick slices or thick slabs (for example 2 to 5 mm)… provide a reconstruction method and system which considers non-uniformly distribution of lesions in the volume and reconstruction methods, considering these distribution differences and taking into account that different reconstruction algorithms may be applied to different anatomical structures (lesions) in the same volume to be examined… a reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices… reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density… apply different reconstruction algorithms for different regions or areas in the volume, depending on the distribution of diagnostically relevant information in the volume to be examine… a method for image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness)… clustering the analyzed volume based on the determined lesion type in at least a first and a second region, wherein the first region has a high degree of diagnostically relevant data and the second region has a low degree of diagnostically relevant data… determining a first reconstruction algorithm for the first region and a second reconstruction algorithm for the second region… determining a first slab thickness for the first and a second slab thickness for the second region… reconstructing the volume such that the first region is reconstructed with the first reconstruction algorithm (preferably with high resolution and in thin slabs) and the second region is reconstructed with the second reconstruction algorithm (preferably with low resolution and thick slabs)… (different) clusters are reconstructed differently (with different reconstruction algorithms), depending on their information density for diagnostic information content. The reconstruction algorithms differ in resolution and slab thickness… a computer program for executing the method mentioned above, when being implemented and running on the control unit system of a three-dimensional tomosynthesis scanner apparatus. The present invention is also directed to a computer program product, comprising code means adapted to execute the method steps according to the method, described above, when loaded into a computer processor… the invention is implemented in hardware or in hardware modules, which may be combined with software modules. The hardware modules are then adapted to perform the functionality of the steps of the method, described above. Accordingly, it is also possible to have a combination of hardware and software modules. The modules are preferably integrated into an existing medical environment, for example into an image acquisition device (CT, x-ray, tomosynthesis apparatus) or in a control unit of such an apparatus; Par. [0027-33]: present invention refers to an image reconstruction method for a three-dimensional digital breast tomosynthesis volume with a plurality of pre-configurable slabs. The method consists of three phases:… analysis phase is executed before reconstruction of the volume… in the analysis phase the analyzed volume is clustered in different regions, particularly in a first region and in a second region, wherein the first region has a high degree of diagnostically relevant data and wherein the second region has a low degree of diagnostically relevant data (and for example mainly consists of healthy tissue). It should be noted that it is possible to determine more than two different regions in the volume, which have (and consist of) different information and which are to be reconstructed differently (for instance with MIP or AIP techniques)… configuration phase relates to the determination of reconstruction relevant parameters for selecting an appropriate reconstruction algorithm, which best fits, the respective type of image information in that region. Particularly, a type of the region clusters is determined. More specifically, a type of information is determined which is present in the regions which a high degree of diagnostic relevant data. Further, the configuration phase refers to determining and configuring the reconstruction algorithms, wherein different reconstruction algorithms are determined for the first region and for the second region. Further, the slab thickness is configured for the reconstruction algorithm. Here again, the slab thickness may differ for the first and the second region… the first region with a high degree of diagnostically relevant data is reconstructed with high resolution in thin slabs and the second region with low degree of diagnostically relevant data is reconstructed with low resolution and thicker slabs than the first region… In the following, a short explanation and definition of terms, used within this disclosure is given… The term "slab" refers to a stack or a sequence of slices in a tomosynthesis volume. A slab has a given thickness. A slab may be construed as consisting of an amount of slices. The slices may be computed with average or maximum intensity projection techniques. A slice may refer to a two-dimensional image plane of the three-dimensional volume stack. A slice typically has a thickness of 1 Voxel. A slab, usually, comprises a plurality of slices, wherein the amount of slices (and thus the thickness of the slab) is configurable… A typical slab thickness for the first region with high degree of diagnostically relevant information lies in the range of 0.5 mm to 2 mm and a typical second slab thickness for the second region with low information density of diagnostically relevant data is in the range of 2 mm to 10 mm; Par. [0057-71]: the provided volume is analyzed in order to detect and identify diagnostically relevant data, like lesions, suspicious structures and/or clusters of calcifications etc.… Preferably two clusters are differentiated: a first region or cluster and a second region or cluster. The first region may be defined as having a high degree of diagnostically relevant data (a high density referring to lesions) and wherein the second region has a low degree of diagnostically relevant data (and thus referring to healthy tissue)… reconstruction is region-specific and lesion-type specific. Thus a first lesion type is determined as a first cluster and reconstructed with a first reconstruction algorithm, whereas a second lesion type is determined as a second cluster and reconstructed with a second reconstruction algorithm… a first reconstruction algorithm is determined for the first region and a second reconstruction algorithm is going to be determined for the second region or cluster, which has been identified in step 14. As mentioned before, different reconstruction algorithms are executed for the different clusters. For example a maximum intensity projection (MIP technique) could be applied to reconstruct slabs containing calcification clusters and an average intensity projection (AIP technique) may be used to reconstruct slabs through the masses. Moreover, a memory and computationally expensive iterative reconstruction could be used to reconstruct slabs corresponding to micro-calcifications and a less resource intensive ("cheaper") filtered back projection method could be used for the other slices…This has the advantage that different reconstruction techniques may be applied for slices and/or for slabs of the same volume but corresponding to different types of lesions or different types of information density clusters… a slab thickness for the respective region is determined within the same volume. This means that different slabbing methods for slices and slabs of the same volume are applied, which correspond to the respective different types of information density clusters, which have been identified in step 14… the identified and determined reconstruction algorithm is applied to the volume clusters according to the before mentioned method steps such that the first region is reconstructed with high resolution in thin slabs and the second region is reconstructed with low resolution and thicker slabs… the reconstruction result is displayed on a monitor or on another user interface for the purpose of diagnosis… selecting or determining different reconstruction algorithms for the volume clusters, which have been identified by the clustering unit C. Further, the determination unit D is adapted for defining a slab thickness for the respective cluster or region, which has been identified by the clustering unit C. With the determination unit D it is possible to use different reconstruction algorithms and different slabbing methods for the different volume clusters, which have been identified by the clustering unit C. It is possible to apply different reconstruction algorithms and slabbing methods for slices within the slabs and for slabs within the volume, corresponding to the specific type of information density of the respective cluster… The reconstruction unit R is adapted for reconstructing the volume according to the determined parameters, as mentioned above, preferably according to the specific slab thickness for the first and for the second cluster and by means of the determined reconstruction algorithm for the first and second cluster, respectively; set a first slab having a first slab thickness for the first region, and a second slab having a second slab thickness, which is different from the first slab thickness, for the second region; and generate a projection image corresponding to the tomographic image using the first slab corresponding to the first region and the second slab corresponding to the second region (e.g. method and system which considers non-uniformly distribution of lesions in a volume acquired by using an image acquisition device, including a three-dimensional tomosynthesis scanner apparatus, for example, include tomographic image reconstruction methods that apply different reconstruction algorithms for different regions (i.e. first, second, third… Nth different regions, sites, areas, clusters, etc.) in the volume depending on the distribution of diagnostically relevant information in the volume to be examined, for example, including determining a first reconstruction algorithm for a first region and a second reconstruction algorithm for a second region, and determining (i.e. setting) a first slab thickness for the first and a second slab thickness for the second region, and the slab thickness differs (i.e. is different) for the first and the second region (i.e. set a first slab having a first slab thickness for the first region, and a second slab having a second slab thickness, which is different from the first slab thickness, for the second region), for example, and perform tomographic reconstruction by reconstructing the volume according to according to the specific slab thickness for the first and for the second cluster region and based on the determined reconstruction algorithm for the first and second cluster region, including average or maximum intensity projection techniques, respectively (i.e. and generate a projection image regarding the tomographic image using the first slab and the second slab), as indicated above), for example), but fails to teach the following as further recited in claim 1.
However, Oosawa teaches generate a projection image by projecting (i) pixel values in the first slab onto pixel positions in the first region in the tomographic image, and (ii) pixel values in the second slab onto pixel positions in the second region in the tomographic image (Par. [0002]: method and an apparatus for aiding image interpretation, and to a computer-readable recording medium storing a program therefor. More specifically, the present invention relates to a method and an apparatus for aiding comparative image reading between two projection images by aligning projection images generated from two three-dimensional images representing the same subject photographed at different times, and to a computer-readable recording medium storing a program therefor; Par. [0012-13]: generating first projection images (prj1) of a first region (reg1) in a first three-dimensional image (vol1) representing a subject by projecting, onto a plane perpendicular to a first observation direction (dir1) to the subject in the first three-dimensional image, pixels in the first region specified by a first cross section (sec1)… generating second projection images (prj2) of a second region (reg2) in a second three-dimensional image (vol2) representing the subject photographed at a time different from the first three-dimensional image by projecting, onto a plane perpendicular to a second observation direction to the subject in the second three-dimensional image, pixels in the second region specified by a second cross section (sec2); Par. [0091-98]: image interpretation aiding apparatus 101 of a first embodiment of the present invention generates projection images by projecting pixels in regions specified by cross sections and thicknesses in two CT images representing the same subject but photographed at different times. The image interpretation aiding apparatus 101 then aligns lung fields in the projection images corresponding to each other, and generates subtraction images… first projection image generation means 3 for reading the first CT image data set CT1 from storage means 92, for generating first projection images of a first region in the first CT image CT1 by projecting pixels in the first region specified by the first cross section and the first thickness… second projection image generation means 6 for reading the second CT image data set CT2 from the storage means 92, for generating second projection images of a second region in the second CT image CT2 by projecting pixels in the second region specified by the second cross section and the second thickness; generate a projection image by projecting (i) pixel values in the first slab onto pixel positions in the first region in the tomographic image, and (ii) pixel values in the second slab onto pixel positions in the second region in the tomographic image (e.g. present invention generates projection images (i.e. generate a projection image) by projecting pixels in regions specified by cross sections and thicknesses (i.e. slabs) in two CT images, for example, including generating first projection images of a first region in the a CT image by projecting pixels in the first region specified by the first cross section and the first thickness (i.e. by projecting (i) pixel values in the first slab onto pixel positions in the first region in the tomographic image) and generating second projection images of a second region in the second CT image by projecting pixels in the second region specified by the second cross section and the second thickness (i.e. and (ii) pixel values in the second slab onto pixel positions in the second region in the tomographic image), as indicated above), for example).
JEREBKO and Oosawa are considered to be analogous art because they pertain to medical image processing applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus for reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density that includes a reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices (as disclosed by JEREBKO) with generate a projection image by projecting (i) pixel values in the first slab onto pixel positions in the first region in the tomographic image, and (ii) pixel values in the second slab onto pixel positions in the second region in the tomographic image (as taught by Oosawa, Abstract, Par. [0002, 12-13, 91-98]) to improve alignment accuracy regarding an observation target between projection images representing the observation target in two three-dimensional images of the same subject photographed at different times, and for suppressing artifacts in a subtraction image and to improve diagnostic efficiency (Oosawa, Abstract, Par. [0002-10, 12-13, 58, 60, 91-98]).
Regarding claim 2, claim 1 is incorporated and JEREBKO discloses the apparatus (Par. [0009-19]), wherein the first slab thickness representing a first distance from the tomographic image is different from the second slab thickness representing a second distance from the tomographic image (Par. [0004-17]: tomosynthesis methods have been developed, which acquire several projections of an object (the breast) at different angles and thereafter reconstruct the three-dimensional distribution of the detected grey values in a detector by means of a tomography reconstruction algorithm… A sequence of projection views is acquired by the digital detector as the X-ray source is rotated to different angular positions about a fulcrum over a finite angular range. Anatomical structures or objects at different heights (or depths in the breast) are projected differently at different angles. The subsequent image reconstruction algorithm leads to a stack or a slab of slice images of the different depth layers parallel to the detector surface… three-dimensional images reconstructed as thick slices or thick slabs (for example 2 to 5 mm)… provide a reconstruction method and system which considers non-uniformly distribution of lesions in the volume and reconstruction methods, considering these distribution differences and taking into account that different reconstruction algorithms may be applied to different anatomical structures (lesions) in the same volume to be examined. Moreover, there is a need for a reconstruction algorithm which processes slabs with variable slice thickness… method for image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness)… determining a first reconstruction algorithm for the first region and a second reconstruction algorithm for the second region… determining a first slab thickness for the first and a second slab thickness for the second region… reconstructing the volume such that the first region is reconstructed with the first reconstruction algorithm (preferably with high resolution and in thin slabs) and the second region is reconstructed with the second reconstruction algorithm (preferably with low resolution and thick slabs)… Clusters are detected in the volume which relate to a lesion type. The (different) clusters are reconstructed differently (with different reconstruction algorithms), depending on their information density for diagnostic information content. The reconstruction algorithms differ in resolution and slab thickness; Par. [0030-33]: configuration phase refers to determining and configuring the reconstruction algorithms, wherein different reconstruction algorithms are determined for the first region and for the second region. Further, the slab thickness is configured for the reconstruction algorithm. Here again, the slab thickness may differ for the first and the second region… term "slab" refers to a stack or a sequence of slices in a tomosynthesis volume. A slab has a given thickness. A slab may be construed as consisting of an amount of slices. The slices may be computed with average or maximum intensity projection techniques. A slice may refer to a two-dimensional image plane of the three-dimensional volume stack. A slice typically has a thickness of 1 Voxel. A slab, usually, comprises a plurality of slices, wherein the amount of slices (and thus the thickness of the slab) is configurable. In this method thin slabs are distinguished from thick slabs. A typical slab thickness for the first region with high degree of diagnostically relevant information lies in the range of 0.5 mm to 2 mm and a typical second slab thickness for the second region with low information density of diagnostically relevant data is in the range of 2 mm to 10 mm. The terms "thin" and "thick" are to be construed such as a thin slab has lower height (extension in z-direction) than a thick slab. Preferably, a thick slab is at least twice as thick as a thin slice. The term "thin" and "thick" are to be construed according to general praxis in CT imaging where slices of 1 mm and lower are considered "thin" and slice with 5 mm as "thick". Thin slices provide the highest resolution in z-direction for a given image modality with reasonable x-ray dose and image quality. Thick slices combine retrospectively multiple thin slices for the sake of faster reading or higher signal to noise ratio; wherein the first slab thickness representing a first distance from the tomographic image is different from the second slab thickness representing a second distance from the tomographic image (e.g. method and system which considers non-uniformly distribution of lesions in a volume acquired by using an image acquisition device, including a three-dimensional tomosynthesis scanner apparatus, for example, include tomographic image reconstruction methods that apply different reconstruction algorithms for different regions (i.e. first, second, third… Nth different regions, sites, areas, clusters, etc.) in the volume depending on the distribution of diagnostically relevant information in the volume to be examined, for example, including determining a first slab thickness (i.e. the first slab thickness) for the first region and a second slab thickness (i.e. the second slab thickness) for the second region, and the slab thickness differs (i.e. is different) for the first and the second region, for example, including slabs having a lower height (i.e. extension or distance in z-direction from the tomographic image) slice thickness and slabs having a higher height slice thickness (i.e. wherein the first slab thickness representing a first distance from the tomographic image is different from the second slab thickness representing a second distance from the tomographic image), as indicated above), for example).
Regarding claim 3, claim 1 is incorporated and JEREBKO discloses the apparatus (Par. [0009-19]), wherein the first region and the second region include different sites from each other in the tomographic image (Par. [0010-17]: reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density… apply different reconstruction algorithms for different regions or areas in the volume, depending on the distribution of diagnostically relevant information in the volume to be examined… clustering the analyzed volume based on the determined lesion type in at least a first and a second region… Clusters are detected in the volume which relate to a lesion type. The (different) clusters are reconstructed differently (with different reconstruction algorithms), depending on their information density for diagnostic information content. The reconstruction algorithms differ in resolution and slab thickness; Par. [0029]: volume is clustered in different regions, particularly in a first region and in a second region, wherein the first region has a high degree of diagnostically relevant data and wherein the second region has a low degree of diagnostically relevant data (and for example mainly consists of healthy tissue). It should be noted that it is possible to determine more than two different regions in the volume, which have (and consist of) different information and which are to be reconstructed differently (for instance with MIP or AIP techniques); Par. [0042]: term "clustering" refers to identifying at least two different sections in the volume. The sections differ in their diagnostic relevance. Thus, the term "clustering" may also be construed in the sense of classifying diagnostically relevant and non-relevant regions. Particularly, a first region with a high degree of diagnostic relevance and a second region with a low degree of diagnostic relevance are identified, which are to be reconstructed separately with different reconstruction algorithms; wherein the first region and the second region include different sites from each other in the tomographic image (e.g. method and system which considers non-uniformly distribution of lesions in a volume acquired by using an image acquisition device, including a three-dimensional tomosynthesis scanner apparatus, for example, include tomographic image reconstruction methods that apply different reconstruction algorithms for different regions (i.e. first, second, third… Nth different regions, sites, areas, clusters, etc.) in the volume (i.e. wherein the first region and the second region include different sites from each other in the tomographic image) depending on the distribution of diagnostically relevant information in the volume to be examined, including a first region that has a high degree of diagnostically relevant data and a second region has a low degree of diagnostically relevant data, as indicated above), for example).
Regarding claim 4, claim 1 is incorporated and JEREBKO discloses the apparatus (Par. [0009-19]), wherein the at least one processor causes the image processing apparatus (Par. [0009-19]: reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices… invention refers to reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density… apply different reconstruction algorithms for different regions or areas in the volume, depending on the distribution of diagnostically relevant information in the volume to be examined… a method for image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness)… a control unit system for image reconstruction of a three-dimensional digital breast tomosynthesis volume in a plurality of slabs, having a pre-configurable thickness, wherein the control unit is adapted for executing the method mentioned above… a computer program for executing the method mentioned above, when being implemented and running on the control unit system of a three-dimensional tomosynthesis scanner apparatus. The present invention is also directed to a computer program product, comprising code means adapted to execute the method steps according to the method, described above, when loaded into a computer processor) to:
set at least one of the first slab thickness and the second slab thickness based on a form of a site included in the first region and the second region (Par. [0069-74]: analyze the provided three-dimensional DBT-volume in order to detect diagnostically relevant data, like anomaly structures, suspicious structures, lesions etc. The clustering unit C is adapted for classifying the analyzed volume in at least a first and a second region (which may also be construed as sub-volume or sub-image) within the same volume to be reconstructed. The first and the second region have different information density with respect to those anatomical structures which are relevant for diagnosis… selecting or determining different reconstruction algorithms for the volume clusters, which have been identified by the clustering unit C. Further, the determination unit D is adapted for defining a slab thickness for the respective cluster or region, which has been identified by the clustering unit C… apply different reconstruction algorithms and slabbing methods for slices within the slabs and for slabs within the volume, corresponding to the specific type of information density of the respective cluster… reconstructing the volume according to the determined parameters, as mentioned above, preferably according to the specific slab thickness for the first and for the second cluster and by means of the determined reconstruction algorithm for the first and second cluster, respectively… a computer aided detection algorithm (CAD algorithm) could be used to identify potentially diagnostically relevant areas or sub-volumes. Some types of abnormalities would require a high resolution reconstruction and, on the other hand, some other types require a thick-slab reconstruction with a particular thickness centered in the lesion for optimal visualization; set at least one of the first slab thickness and the second slab thickness based on a form of a site included in the first region and the second region (e.g. method and system which considers non-uniformly distribution of lesions in a volume acquired by using an image acquisition device, including a three-dimensional tomosynthesis scanner apparatus, for example, include tomographic image reconstruction methods that apply different reconstruction algorithms for different regions (i.e. first, second, third… Nth different regions, sites, areas, clusters, etc.) in the volume depending on the distribution of diagnostically relevant information in the volume to be examined (i.e. sites included in the first region and the second region), for example, including determining (i.e. setting) a first slab thickness (i.e. the first slab thickness) for the first region and a second slab thickness (i.e. the second slab thickness) for the second region, and the slab thickness differs (i.e. is different) for the first and the second region, for example, in which the first and second region have different information density with respect to anatomical structures (i.e. forms, shapes, etc.) which are relevant for diagnosis (i.e. set at least one of the first slab thickness and the second slab thickness based on a form of a site included in the first region and the second region), as indicated above), for example).
Regarding claim 6, claim 1 is incorporated and JEREBKO discloses the apparatus (Par. [0009-19]), wherein the at least one processor causes the image processing apparatus (Par. [0009-19]: reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices… invention refers to reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density… apply different reconstruction algorithms for different regions or areas in the volume, depending on the distribution of diagnostically relevant information in the volume to be examined… a method for image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness)… a control unit system for image reconstruction of a three-dimensional digital breast tomosynthesis volume in a plurality of slabs, having a pre-configurable thickness, wherein the control unit is adapted for executing the method mentioned above… a computer program for executing the method mentioned above, when being implemented and running on the control unit system of a three-dimensional tomosynthesis scanner apparatus. The present invention is also directed to a computer program product, comprising code means adapted to execute the method steps according to the method, described above, when loaded into a computer processor) to:
set at least one of the first slab thickness and the second slab thickness in accordance with a likelihood at which a site exists inside the first region and the second region (Par. [0004-20]: tomosynthesis methods have been developed, which acquire several projections of an object… and thereafter reconstruct the three-dimensional distribution of the detected grey values in a detector by means of a tomography reconstruction algorithm… A sequence of projection views is acquired by the digital detector as the X-ray source is rotated to different angular positions… Anatomical structures or objects at different heights (or depths in the breast) are projected differently at different angles. The subsequent image reconstruction algorithm leads to a stack or a slab of slice images of the different depth layers… One of the usual ways of reducing the amount of data for read and for storage in regular computer tomography is the reconstruction of the volume in thick slices. While the modern computer tomographs are capable of producing images of less than 0.5 mm slice thickness (for example in thoracic or in abdominal images), radiologists often read and analyze three-dimensional images reconstructed as thick slices or thick slabs (for example 2 to 5 mm)… provide a reconstruction method and system which considers non-uniformly distribution of lesions in the volume and reconstruction methods, considering these distribution differences and taking into account that different reconstruction algorithms may be applied to different anatomical structures (lesions) in the same volume to be examined… a reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices… reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density… apply different reconstruction algorithms for different regions or areas in the volume, depending on the distribution of diagnostically relevant information in the volume to be examine… a method for image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness)… clustering the analyzed volume based on the determined lesion type in at least a first and a second region, wherein the first region has a high degree of diagnostically relevant data and the second region has a low degree of diagnostically relevant data… determining a first reconstruction algorithm for the first region and a second reconstruction algorithm for the second region… determining a first slab thickness for the first and a second slab thickness for the second region… reconstructing the volume such that the first region is reconstructed with the first reconstruction algorithm (preferably with high resolution and in thin slabs) and the second region is reconstructed with the second reconstruction algorithm (preferably with low resolution and thick slabs)… (different) clusters are reconstructed differently (with different reconstruction algorithms), depending on their information density for diagnostic information content. The reconstruction algorithms differ in resolution and slab thickness… a computer program for executing the method mentioned above, when being implemented and running on the control unit system of a three-dimensional tomosynthesis scanner apparatus. The present invention is also directed to a computer program product, comprising code means adapted to execute the method steps according to the method, described above, when loaded into a computer processor… the invention is implemented in hardware or in hardware modules, which may be combined with software modules. The hardware modules are then adapted to perform the functionality of the steps of the method, described above. Accordingly, it is also possible to have a combination of hardware and software modules. The modules are preferably integrated into an existing medical environment, for example into an image acquisition device (CT, x-ray, tomosynthesis apparatus) or in a control unit of such an apparatus; Par. [0027-33]: present invention refers to an image reconstruction method for a three-dimensional digital breast tomosynthesis volume with a plurality of pre-configurable slabs. The method consists of three phases:… analysis phase is executed before reconstruction of the volume… in the analysis phase the analyzed volume is clustered in different regions, particularly in a first region and in a second region, wherein the first region has a high degree of diagnostically relevant data and wherein the second region has a low degree of diagnostically relevant data (and for example mainly consists of healthy tissue). It should be noted that it is possible to determine more than two different regions in the volume, which have (and consist of) different information and which are to be reconstructed differently (for instance with MIP or AIP techniques)… configuration phase relates to the determination of reconstruction relevant parameters for selecting an appropriate reconstruction algorithm, which best fits, the respective type of image information in that region. Particularly, a type of the region clusters is determined. More specifically, a type of information is determined which is present in the regions which a high degree of diagnostic relevant data. Further, the configuration phase refers to determining and configuring the reconstruction algorithms, wherein different reconstruction algorithms are determined for the first region and for the second region. Further, the slab thickness is configured for the reconstruction algorithm. Here again, the slab thickness may differ for the first and the second region… the first region with a high degree of diagnostically relevant data is reconstructed with high resolution in thin slabs and the second region with low degree of diagnostically relevant data is reconstructed with low resolution and thicker slabs than the first region… In the following, a short explanation and definition of terms, used within this disclosure is given… The term "slab" refers to a stack or a sequence of slices in a tomosynthesis volume. A slab has a given thickness. A slab may be construed as consisting of an amount of slices. The slices may be computed with average or maximum intensity projection techniques. A slice may refer to a two-dimensional image plane of the three-dimensional volume stack. A slice typically has a thickness of 1 Voxel. A slab, usually, comprises a plurality of slices, wherein the amount of slices (and thus the thickness of the slab) is configurable… A typical slab thickness for the first region with high degree of diagnostically relevant information lies in the range of 0.5 mm to 2 mm and a typical second slab thickness for the second region with low information density of diagnostically relevant data is in the range of 2 mm to 10 mm; Par. [0057-71]: the provided volume is analyzed in order to detect and identify diagnostically relevant data, like lesions, suspicious structures and/or clusters of calcifications etc.… Preferably two clusters are differentiated: a first region or cluster and a second region or cluster. The first region may be defined as having a high degree of diagnostically relevant data (a high density referring to lesions) and wherein the second region has a low degree of diagnostically relevant data (and thus referring to healthy tissue); set at least one of the first slab thickness and the second slab thickness in accordance with a likelihood at which a site exists inside the first region and the second region (e.g. method and system which considers non-uniformly distribution of lesions in a volume acquired by using an image acquisition device, including a three-dimensional tomosynthesis scanner apparatus, for example, include tomographic image reconstruction methods that apply different reconstruction algorithms for different regions (i.e. first, second, third… Nth different regions, sites, areas, clusters, etc.) in the volume depending on the distribution of diagnostically relevant information in the volume to be examined (i.e. sites included in the first region and the second region), for example, including determining (i.e. setting) a first slab thickness (i.e. the first slab thickness) for the first region and a second slab thickness (i.e. the second slab thickness) for the second region, and the slab thickness differs (i.e. is different) for the first and the second region, for example, in which the first region has a high degree (i.e. likelihood, probability, etc.) of diagnostically relevant data and the second region has a low degree of diagnostically relevant data (i.e. set at least one of the first slab thickness and the second slab thickness in accordance with a likelihood at which a site exists inside the first region and the second region), respectively, as indicated above), for example).
Regarding claim 7, claim 1 is incorporated and JEREBKO discloses the apparatus (Par. [0009-19]), wherein the at least one processor causes the image processing apparatus (Par. [0009-19]: reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices… invention refers to reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density… apply different reconstruction algorithms for different regions or areas in the volume, depending on the distribution of diagnostically relevant information in the volume to be examined… a method for image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness)… a control unit system for image reconstruction of a three-dimensional digital breast tomosynthesis volume in a plurality of slabs, having a pre-configurable thickness, wherein the control unit is adapted for executing the method mentioned above… a computer program for executing the method mentioned above, when being implemented and running on the control unit system of a three-dimensional tomosynthesis scanner apparatus. The present invention is also directed to a computer program product, comprising code means adapted to execute the method steps according to the method, described above, when loaded into a computer processor) to:
set at least one of the first slab thickness and the second slab thickness based on feature information representing a feature of a site obtained from image information of the site included in the first region and the second region (Par. [0004-20]: tomosynthesis methods have been developed, which acquire several projections of an object… and thereafter reconstruct the three-dimensional distribution of the detected grey values in a detector by means of a tomography reconstruction algorithm… A sequence of projection views is acquired by the digital detector as the X-ray source is rotated to different angular positions… Anatomical structures or objects at different heights (or depths in the breast) are projected differently at different angles. The subsequent image reconstruction algorithm leads to a stack or a slab of slice images of the different depth layers… One of the usual ways of reducing the amount of data for read and for storage in regular computer tomography is the reconstruction of the volume in thick slices. While the modern computer tomographs are capable of producing images of less than 0.5 mm slice thickness (for example in thoracic or in abdominal images), radiologists often read and analyze three-dimensional images reconstructed as thick slices or thick slabs (for example 2 to 5 mm)… provide a reconstruction method and system which considers non-uniformly distribution of lesions in the volume and reconstruction methods, considering these distribution differences and taking into account that different reconstruction algorithms may be applied to different anatomical structures (lesions) in the same volume to be examined… a reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices… reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density… apply different reconstruction algorithms for different regions or areas in the volume, depending on the distribution of diagnostically relevant information in the volume to be examine… a method for image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness)… clustering the analyzed volume based on the determined lesion type in at least a first and a second region, wherein the first region has a high degree of diagnostically relevant data and the second region has a low degree of diagnostically relevant data… determining a first reconstruction algorithm for the first region and a second reconstruction algorithm for the second region… determining a first slab thickness for the first and a second slab thickness for the second region… reconstructing the volume such that the first region is reconstructed with the first reconstruction algorithm (preferably with high resolution and in thin slabs) and the second region is reconstructed with the second reconstruction algorithm (preferably with low resolution and thick slabs)… (different) clusters are reconstructed differently (with different reconstruction algorithms), depending on their information density for diagnostic information content. The reconstruction algorithms differ in resolution and slab thickness… a computer program for executing the method mentioned above, when being implemented and running on the control unit system of a three-dimensional tomosynthesis scanner apparatus. The present invention is also directed to a computer program product, comprising code means adapted to execute the method steps according to the method, described above, when loaded into a computer processor… the invention is implemented in hardware or in hardware modules, which may be combined with software modules. The hardware modules are then adapted to perform the functionality of the steps of the method, described above. Accordingly, it is also possible to have a combination of hardware and software modules. The modules are preferably integrated into an existing medical environment, for example into an image acquisition device (CT, x-ray, tomosynthesis apparatus) or in a control unit of such an apparatus; Par. [0027-33]: present invention refers to an image reconstruction method for a three-dimensional digital breast tomosynthesis volume with a plurality of pre-configurable slabs. The method consists of three phases:… analysis phase is executed before reconstruction of the volume… in the analysis phase the analyzed volume is clustered in different regions, particularly in a first region and in a second region, wherein the first region has a high degree of diagnostically relevant data and wherein the second region has a low degree of diagnostically relevant data (and for example mainly consists of healthy tissue). It should be noted that it is possible to determine more than two different regions in the volume, which have (and consist of) different information and which are to be reconstructed differently (for instance with MIP or AIP techniques)… configuration phase relates to the determination of reconstruction relevant parameters for selecting an appropriate reconstruction algorithm, which best fits, the respective type of image information in that region. Particularly, a type of the region clusters is determined. More specifically, a type of information is determined which is present in the regions which a high degree of diagnostic relevant data. Further, the configuration phase refers to determining and configuring the reconstruction algorithms, wherein different reconstruction algorithms are determined for the first region and for the second region. Further, the slab thickness is configured for the reconstruction algorithm. Here again, the slab thickness may differ for the first and the second region… the first region with a high degree of diagnostically relevant data is reconstructed with high resolution in thin slabs and the second region with low degree of diagnostically relevant data is reconstructed with low resolution and thicker slabs than the first region… In the following, a short explanation and definition of terms, used within this disclosure is given… The term "slab" refers to a stack or a sequence of slices in a tomosynthesis volume. A slab has a given thickness. A slab may be construed as consisting of an amount of slices. The slices may be computed with average or maximum intensity projection techniques. A slice may refer to a two-dimensional image plane of the three-dimensional volume stack. A slice typically has a thickness of 1 Voxel. A slab, usually, comprises a plurality of slices, wherein the amount of slices (and thus the thickness of the slab) is configurable… A typical slab thickness for the first region with high degree of diagnostically relevant information lies in the range of 0.5 mm to 2 mm and a typical second slab thickness for the second region with low information density of diagnostically relevant data is in the range of 2 mm to 10 mm; Par. [0057-71]: the provided volume is analyzed in order to detect and identify diagnostically relevant data, like lesions, suspicious structures and/or clusters of calcifications etc.… Preferably two clusters are differentiated: a first region or cluster and a second region or cluster. The first region may be defined as having a high degree of diagnostically relevant data (a high density referring to lesions) and wherein the second region has a low degree of diagnostically relevant data (and thus referring to healthy tissue); set at least one of the first slab thickness and the second slab thickness based on feature information representing a feature of a site obtained from image information of the site included in the first region and the second region (e.g. method and system which considers non-uniformly distribution of lesions in a volume acquired by using an image acquisition device, including a three-dimensional tomosynthesis scanner apparatus, for example, include tomographic image reconstruction methods that apply different reconstruction algorithms for different regions (i.e. first, second, third… Nth different regions, sites, areas, clusters, etc.) in the volume depending on the distribution of diagnostically relevant information in the volume (i.e. feature information representing a feature of a site) to be examined, for example, including determining (i.e. setting) a first slab thickness (i.e. the first slab thickness) for the first region and a second slab thickness (i.e. the second slab thickness) for the second region, and the slab thickness differs (i.e. is different) for the first and the second region (i.e. set at least one of the first slab thickness and the second slab thickness based on feature information representing a feature of a site obtained from image information of the site included in the first region and the second region), as indicated above), for example).
Regarding claim 10, claim 1 is incorporated and the combination of JEREBKO and Oosawa, as a whole, teaches the apparatus (JEREBKO, Par. [0009-19]), wherein the at least one processor causes the image processing apparatus (JEREBKO, Par. [0009-19]: reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices… invention refers to reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density… apply different reconstruction algorithms for different regions or areas in the volume, depending on the distribution of diagnostically relevant information in the volume to be examined… a method for image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness)… a control unit system for image reconstruction of a three-dimensional digital breast tomosynthesis volume in a plurality of slabs, having a pre-configurable thickness, wherein the control unit is adapted for executing the method mentioned above… a computer program for executing the method mentioned above, when being implemented and running on the control unit system of a three-dimensional tomosynthesis scanner apparatus. The present invention is also directed to a computer program product, comprising code means adapted to execute the method steps according to the method, described above, when loaded into a computer processor) to:
generate the projection image based on a pixel in a site included in the first slab and the second slab (Oosawa, Par. [0002]: method and an apparatus for aiding image interpretation, and to a computer-readable recording medium storing a program therefor. More specifically, the present invention relates to a method and an apparatus for aiding comparative image reading between two projection images by aligning projection images generated from two three-dimensional images representing the same subject photographed at different times, and to a computer-readable recording medium storing a program therefor; Par. [0012-13]: generating first projection images (prj1) of a first region (reg1) in a first three-dimensional image (vol1) representing a subject by projecting, onto a plane perpendicular to a first observation direction (dir1) to the subject in the first three-dimensional image, pixels in the first region specified by a first cross section (sec1) perpendicular to the first observation direction and a first thickness (thick1) equal to a first pitch (pitch1), which is a thickness from the first cross section in the first observation direction… generating second projection images (prj2) of a second region (reg2) in a second three-dimensional image (vol2) representing the subject photographed at a time different from the first three-dimensional image by projecting, onto a plane perpendicular to a second observation direction to the subject in the second three-dimensional image, pixels in the second region specified by a second cross section (sec2) perpendicular to the second observation direction and a second thickness (thick2) equal to a second pitch (pitch2), which is a thickness from the second cross section in the second observation direction; Par. [0022-25]: generating first projection images of a first region in the first three-dimensional image by projecting, onto a plane perpendicular to the first observation direction, pixels in the first region specified by the first cross section and the first thickness… generating second projection images of a second region in the second three-dimensional image by projecting, onto a plane perpendicular to the second observation direction, pixels in the second region specified by the second cross section and the second thickness; Par. [0091-98]: image interpretation aiding apparatus 101 of a first embodiment of the present invention generates projection images by projecting pixels in regions specified by cross sections and thicknesses in two CT images representing the same subject but photographed at different times. The image interpretation aiding apparatus 101 then aligns lung fields in the projection images corresponding to each other, and generates subtraction images… first projection image generation means 3 for reading the first CT image data set CT1 from storage means 92, for generating first projection images of a first region in the first CT image CT1 by projecting pixels in the first region specified by the first cross section and the first thickness… second projection image generation means 6 for reading the second CT image data set CT2 from the storage means 92, for generating second projection images of a second region in the second CT image CT2 by projecting pixels in the second region specified by the second cross section and the second thickness; generate the projection image based on a pixel in a site included in the first slab and the second slab (e.g. present invention generates projection images (i.e. generate a projection image) by projecting pixels in (i.e. included, within, etc.) regions (i.e. areas, sites, etc.) specified by cross sections and thicknesses (i.e. slabs) in two CT images, for example, including generating first projection images of a first region in the a CT image by projecting pixels in the first region specified by the first cross section and the first thickness (i.e. generate the projection image based on a pixel in a site included in the first slab) and generating second projection images of a second region in the second CT image by projecting pixels in the second region specified by the second cross section and the second thickness (i.e. generate the projection image based on a pixel in a site included in the first slab and the second slab), as indicated above), for example).
The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1.
Regarding claim 12, claim 1 is incorporated and JEREBKO discloses the apparatus (Par. [0009-19]), wherein the at least one processor causes the image processing apparatus (Par. [0009-19]: reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices… invention refers to reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density… apply different reconstruction algorithms for different regions or areas in the volume, depending on the distribution of diagnostically relevant information in the volume to be examined… a method for image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness)… a control unit system for image reconstruction of a three-dimensional digital breast tomosynthesis volume in a plurality of slabs, having a pre-configurable thickness, wherein the control unit is adapted for executing the method mentioned above… a computer program for executing the method mentioned above, when being implemented and running on the control unit system of a three-dimensional tomosynthesis scanner apparatus. The present invention is also directed to a computer program product, comprising code means adapted to execute the method steps according to the method, described above, when loaded into a computer processor) to:
obtain a three-dimensional moving image,
wherein a frame included in the three-dimensional moving image is obtained as the three-dimensional image (Par. [0001-11]: image reconstruction of a three-dimensional digital breast tomosynthesis volume (DBT)… sequence of projection views is acquired by the digital detector as the X-ray source is rotated to different angular positions about a fulcrum over a finite angular range. Anatomical structures or objects at different heights (or depths in the breast) are projected differently at different angles. The subsequent image reconstruction algorithm leads to a stack or a slab of slice images of the different depth layers parallel to the detector surface… image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness); Par. [0033]: term "slab" refers to a stack or a sequence of slices in a tomosynthesis volume. A slab has a given thickness. A slab may be construed as consisting of an amount of slices. The slices may be computed with average or maximum intensity projection techniques. A slice may refer to a two-dimensional image plane of the three-dimensional volume stack; Par. [0054-56]: digital tomosynthesis is based on the fact that a plurality of two-dimensional images is generated, which differ in their angle around the object to be examine… three-dimensional volume data are generated by the mammography tomosynthesis; obtain a three-dimensional moving image, wherein a frame included in the three-dimensional moving image is obtained as the three-dimensional image (e.g. method and system which considers non-uniformly distribution of lesions in a volume acquired by using an image acquisition device, including a three-dimensional tomosynthesis scanner apparatus, for example, include tomographic image (i.e. frame) reconstruction methods that apply different reconstruction algorithms for different regions in the volume depending on the distribution of diagnostically relevant information in the volume (i.e. feature information representing a feature of a site) to be examined, for example, including a stack or a sequence of slices in a tomosynthesis volume obtained from sequence of projection views acquired by a digital detector as is X-ray source is rotated (i.e. moved) to different angular positions about a fulcrum over a finite angular range (i.e. obtain a three-dimensional moving image), for example, in which subsequent image reconstruction algorithm leads to a stack or a slab of slice images (i.e. frames included in the three-dimensional moving image) of the different depth layers parallel to the detector surface by performing image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs with a configurable thickness (i.e. wherein a frame included in the three-dimensional moving image is obtained as the three-dimensional image).
Regarding claim 13, claim 1 is incorporated and JEREBKO discloses the apparatus (Par. [0009-19]), wherein the at least one processor causes the image processing apparatus (Par. [0009-19]: reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices… invention refers to reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density… apply different reconstruction algorithms for different regions or areas in the volume, depending on the distribution of diagnostically relevant information in the volume to be examined… a method for image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness)… a control unit system for image reconstruction of a three-dimensional digital breast tomosynthesis volume in a plurality of slabs, having a pre-configurable thickness, wherein the control unit is adapted for executing the method mentioned above… a computer program for executing the method mentioned above, when being implemented and running on the control unit system of a three-dimensional tomosynthesis scanner apparatus. The present invention is also directed to a computer program product, comprising code means adapted to execute the method steps according to the method, described above, when loaded into a computer processor) to:
set at least one of the first slab thickness and the second slab thickness based on dynamic information representing movement of a site included in a frame of a three-dimensional moving image obtained as the three-dimensional image (Par. [0001-11]: image reconstruction of a three-dimensional digital breast tomosynthesis volume (DBT)… sequence of projection views is acquired by the digital detector as the X-ray source is rotated to different angular positions about a fulcrum over a finite angular range. Anatomical structures or objects at different heights (or depths in the breast) are projected differently at different angles. The subsequent image reconstruction algorithm leads to a stack or a slab of slice images of the different depth layers parallel to the detector surface… image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness); Par. [0027-33]: present invention refers to an image reconstruction method for a three-dimensional digital breast tomosynthesis volume with a plurality of pre-configurable slabs. The method consists of three phases:… analysis phase is executed before reconstruction of the volume… in the analysis phase the analyzed volume is clustered in different regions, particularly in a first region and in a second region, wherein the first region has a high degree of diagnostically relevant data and wherein the second region has a low degree of diagnostically relevant data (and for example mainly consists of healthy tissue). It should be noted that it is possible to determine more than two different regions in the volume, which have (and consist of) different information and which are to be reconstructed differently (for instance with MIP or AIP techniques)… configuration phase relates to the determination of reconstruction relevant parameters for selecting an appropriate reconstruction algorithm, which best fits, the respective type of image information in that region. Particularly, a type of the region clusters is determined. More specifically, a type of information is determined which is present in the regions which a high degree of diagnostic relevant data. Further, the configuration phase refers to determining and configuring the reconstruction algorithms, wherein different reconstruction algorithms are determined for the first region and for the second region. Further, the slab thickness is configured for the reconstruction algorithm. Here again, the slab thickness may differ for the first and the second region… the first region with a high degree of diagnostically relevant data is reconstructed with high resolution in thin slabs and the second region with low degree of diagnostically relevant data is reconstructed with low resolution and thicker slabs than the first region… In the following, a short explanation and definition of terms, used within this disclosure is given… The term "slab" refers to a stack or a sequence of slices in a tomosynthesis volume. A slab has a given thickness. A slab may be construed as consisting of an amount of slices. The slices may be computed with average or maximum intensity projection techniques. A slice may refer to a two-dimensional image plane of the three-dimensional volume stack. A slice typically has a thickness of 1 Voxel. A slab, usually, comprises a plurality of slices, wherein the amount of slices (and thus the thickness of the slab) is configurable… A typical slab thickness for the first region with high degree of diagnostically relevant information lies in the range of 0.5 mm to 2 mm and a typical second slab thickness for the second region with low information density of diagnostically relevant data is in the range of 2 mm to 10 mm; Par. [0054-71]: digital tomosynthesis is based on the fact that a plurality of two-dimensional images is generated, which differ in their angle around the object to be examine… the provided volume is analyzed in order to detect and identify diagnostically relevant data, like lesions, suspicious structures and/or clusters of calcifications etc.… Preferably two clusters are differentiated: a first region or cluster and a second region or cluster. The first region may be defined as having a high degree of diagnostically relevant data (a high density referring to lesions) and wherein the second region has a low degree of diagnostically relevant data (and thus referring to healthy tissue)… reconstruction is region-specific and lesion-type specific. Thus a first lesion type is determined as a first cluster and reconstructed with a first reconstruction algorithm, whereas a second lesion type is determined as a second cluster and reconstructed with a second reconstruction algorithm… a first reconstruction algorithm is determined for the first region and a second reconstruction algorithm is going to be determined for the second region or cluster, which has been identified in step 14. As mentioned before, different reconstruction algorithms are executed for the different clusters. For example a maximum intensity projection (MIP technique) could be applied to reconstruct slabs containing calcification clusters and an average intensity projection (AIP technique) may be used to reconstruct slabs through the masses. Moreover, a memory and computationally expensive iterative reconstruction could be used to reconstruct slabs corresponding to micro-calcifications and a less resource intensive ("cheaper") filtered back projection method could be used for the other slices…This has the advantage that different reconstruction techniques may be applied for slices and/or for slabs of the same volume but corresponding to different types of lesions or different types of information density clusters… a slab thickness for the respective region is determined within the same volume. This means that different slabbing methods for slices and slabs of the same volume are applied, which correspond to the respective different types of information density clusters, which have been identified in step 14… the identified and determined reconstruction algorithm is applied to the volume clusters according to the before mentioned method steps such that the first region is reconstructed with high resolution in thin slabs and the second region is reconstructed with low resolution and thicker slabs… the reconstruction result is displayed on a monitor or on another user interface for the purpose of diagnosis… selecting or determining different reconstruction algorithms for the volume clusters, which have been identified by the clustering unit C. Further, the determination unit D is adapted for defining a slab thickness for the respective cluster or region, which has been identified by the clustering unit C. With the determination unit D it is possible to use different reconstruction algorithms and different slabbing methods for the different volume clusters, which have been identified by the clustering unit C. It is possible to apply different reconstruction algorithms and slabbing methods for slices within the slabs and for slabs within the volume, corresponding to the specific type of information density of the respective cluster… The reconstruction unit R is adapted for reconstructing the volume according to the determined parameters, as mentioned above, preferably according to the specific slab thickness for the first and for the second cluster and by means of the determined reconstruction algorithm for the first and second cluster, respectively; set at least one of the first slab thickness and the second slab thickness based on dynamic information representing movement of a site included in a frame of a three-dimensional moving image obtained as the three-dimensional image (e.g. method and system which considers non-uniformly distribution of lesions in a volume acquired by using an image acquisition device, including a three-dimensional tomosynthesis scanner apparatus, for example, include tomographic image reconstruction methods that apply different reconstruction algorithms for different regions (i.e. first, second, third… Nth different regions, sites, areas, clusters, etc.) in the volume depending on the distribution of diagnostically relevant information in the volume to be examined, for example, including determining a first reconstruction algorithm for a first region and a second reconstruction algorithm for a second region, and determining (i.e. setting) a first slab thickness for the first and a second slab thickness for the second region (i.e. set at least one of the first slab thickness and the second slab thickness), for example, including a stack or a sequence of slices in a tomosynthesis volume obtained from sequence of projection views acquired by a digital detector as is X-ray source is rotated (i.e. moved) to different (i.e. dynamic) angular positions about a fulcrum over a finite angular range (i.e. a three-dimensional moving image), for example, in which subsequent image reconstruction algorithm leads to a stack or a slab of slice images (i.e. a frame of a three-dimensional moving image obtained as the three-dimensional image) of the different depth layers parallel to the detector surface by performing image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs with a configurable thickness (i.e. set at least one of the first slab thickness and the second slab thickness based on dynamic information representing movement of a site included in a frame of a three-dimensional moving image obtained as the three-dimensional image).
Regarding claim 14, claim 1 is incorporated and JEREBKO discloses the apparatus (Par. [0009-19]), wherein the at least one processor causes the image processing apparatus (Par. [0009-19]: reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices… invention refers to reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density… apply different reconstruction algorithms for different regions or areas in the volume, depending on the distribution of diagnostically relevant information in the volume to be examined… a method for image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness)… a control unit system for image reconstruction of a three-dimensional digital breast tomosynthesis volume in a plurality of slabs, having a pre-configurable thickness, wherein the control unit is adapted for executing the method mentioned above… a computer program for executing the method mentioned above, when being implemented and running on the control unit system of a three-dimensional tomosynthesis scanner apparatus. The present invention is also directed to a computer program product, comprising code means adapted to execute the method steps according to the method, described above, when loaded into a computer processor) to:
control to display the projection image on a display (Par. [0062-67]: show an intermediate result on a display comprising the detected diagnostically relevant structures (lesions, calcifications and others) and the detected and associated type of the respective lesion and its classification in a specific cluster… the reconstruction result is displayed on a monitor or on another user interface for the purpose of diagnosis).
Regarding claim 16, is a corresponding method claim rejected as applied to the apparatus claim 1 above.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over JEREBKO, in view of Oosawa, as applied to claim 1 above, in further view of REYNOLDS et al. (US PG Publication No. 2017/0262978 A1), hereafter referred to as REYNOLDS.
Regarding claim 8, claim 1 is incorporated and the combination of JEREBKO and Oosawa, as a whole, teaches discloses the apparatus (JEREBKO, Par. [0009-19]), wherein the at least one processor causes the image processing apparatus (JEREBKO, Par. [0009-19]: reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices… invention refers to reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density… apply different reconstruction algorithms for different regions or areas in the volume, depending on the distribution of diagnostically relevant information in the volume to be examined… a method for image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness)… a control unit system for image reconstruction of a three-dimensional digital breast tomosynthesis volume in a plurality of slabs, having a pre-configurable thickness, wherein the control unit is adapted for executing the method mentioned above… a computer program for executing the method mentioned above, when being implemented and running on the control unit system of a three-dimensional tomosynthesis scanner apparatus. The present invention is also directed to a computer program product, comprising code means adapted to execute the method steps according to the method, described above, when loaded into a computer processor) to:
set at least one of the first slab thickness and the second slab thickness based on a variance of values obtained from image information of a site included in the first region and the second region (Par. [0069-74]: analyze the provided three-dimensional DBT-volume in order to detect diagnostically relevant data, like anomaly structures, suspicious structures, lesions etc. The clustering unit C is adapted for classifying the analyzed volume in at least a first and a second region (which may also be construed as sub-volume or sub-image) within the same volume to be reconstructed. The first and the second region have different information density with respect to those anatomical structures which are relevant for diagnosis… selecting or determining different reconstruction algorithms for the volume clusters, which have been identified by the clustering unit C. Further, the determination unit D is adapted for defining a slab thickness for the respective cluster or region, which has been identified by the clustering unit C… apply different reconstruction algorithms and slabbing methods for slices within the slabs and for slabs within the volume, corresponding to the specific type of information density of the respective cluster… reconstructing the volume according to the determined parameters, as mentioned above, preferably according to the specific slab thickness for the first and for the second cluster and by means of the determined reconstruction algorithm for the first and second cluster, respectively… a computer aided detection algorithm (CAD algorithm) could be used to identify potentially diagnostically relevant areas or sub-volumes. Some types of abnormalities would require a high resolution reconstruction and, on the other hand, some other types require a thick-slab reconstruction with a particular thickness centered in the lesion for optimal visualization; set at least one of the first slab thickness and the second slab thickness based on a variance of values obtained from image information of a site included in the first region and the second region (e.g. method and system which considers non-uniformly distribution of lesions in a volume acquired by using an image acquisition device, including a three-dimensional tomosynthesis scanner apparatus, for example, include tomographic image reconstruction methods that apply different reconstruction algorithms for different regions (i.e. first, second, third… Nth different regions, sites, areas, clusters, etc.) in the volume depending on the distribution of diagnostically relevant information in the volume to be examined (i.e. sites included in the first region and the second region), for example, including determining (i.e. setting) a first slab thickness (i.e. the first slab thickness) for the first region and a second slab thickness (i.e. the second slab thickness) for the second region, and the slab thickness differs (i.e. is different) for the first and the second region, for example, in which the first and second region have different information density (i.e. variance of values) with respect to anatomical structures (i.e. forms, shapes, etc.) which are relevant for diagnosis (i.e. set at least one of the first slab thickness and the second slab thickness based on a variance of values obtained from image information of a site included in the first region and the second region, as indicated above), for example), but fails to teach the following as further cited in claim 8.
However, REYNOLDS teaches variance of pixel values (Par. [0069-74]: rays provide a cylindrical projection that allows an unfolded view of the ribs to be obtained. The rays are used to map points in the image to points on the manifold surface. Each ray corresponds to a respective pixel of the output image… image generation circuitry 28 determines a respective pixel value for each of the plurality of ray paths 500 using the voxel intensity values sampled at stage 118… the image generation circuitry 28 determines a pixel value for each ray 500 by applying a transfer function to the voxel intensity value for the point 510 at which the ray cast along that ray path 500 intersects the manifold. The voxel intensity value for each point 510 has been determined at stage 118 by sampling the medical imaging data set … any method of obtaining a pixel value for each ray 500 may be used, and the curved plane may be in the form of a slab… image generation circuitry 28 samples a number of voxels or other points along each ray (for example, 3, 4 or 5 voxels) that are nearest the intersection of the ray with the original manifold 400. The image generation circuitry 28 uses a projection of the slab to obtain the pixel value for each ray… image generation circuitry 28 uses a maximum intensity projection of the slab to determine the pixel value for each ray… the image generation circuitry 28 uses a variance projection, in which pixel values are dependent on a rate of change of signal across the portion of the ray that is within the slab. In general, a collection of sample points, each offset in the ray direction, may be used to generate a slabbed image and an IP projection method may be used to determine the output result; Par. [0108]: image generation circuitry 28 obtains pixel values, for example by using a transfer function, and displays a final rendered image using the pixel values; variance of pixel values (e.g. image generation circuitry uses a variance projection, in which pixel values are dependent on a rate of change (i.e. variance of pixel values) of signal across the portion of a ray that is within the slab, for example, and determines pixel values corresponding to sampled points using voxel intensity values sampled by using a maximum intensity projection of a slab to determine the pixel value for each ray used to map points in an image to points on a manifold surface, for example, and each ray corresponds to a respective pixel of an output image, as indicated above), for example).
JEREBKO, Oosawa, and REYNOLDS are considered to be analogous art because they pertain to medical image processing applications. Therefore, the combined teachings of JEREBKO, Oosawa, and REYNOLDS, as a whole, would have rendered obvious the invention recited in claim 8 with a reasonable expectation of success in order to modify the apparatus for reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density that includes a reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices (as disclosed by JEREBKO) with variance of pixel values (as taught by REYNOLDS, Abstract, Par. [0069-74]) to view ribs of a patient or other subject, to be able to identify and count displaced and non-displaced fractures or bone lesions when performing chest examinations, to ensure that all ribs have been examined and to determine in which rib any fractures or lesions are located, and to identify any fractures and to be able to identify and count displaced and non-displaced fractures or bone lesions when performing chest examinations (REYNOLDS, Abstract, Par. [0002-9, 69-74, 80-81, 90, 98]).
Claims 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over JEREBKO, in view of Okerlund et al. (US PG Publication No. 2016/0012613 A1), hereafter referred to as Okerlund, in further view of Oosawa.
Regarding claim 15, JEREBKO discloses an image processing apparatus (Par. [0009-19]: a reconstruction method and system which considers non-uniformly distribution of lesions in the volume and reconstruction methods… a reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices… invention refers to reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density… apply different reconstruction algorithms for different regions or areas in the volume, depending on the distribution of diagnostically relevant information in the volume to be examined… a method for image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness)… a control unit system for image reconstruction of a three-dimensional digital breast tomosynthesis volume in a plurality of slabs, having a pre-configurable thickness, wherein the control unit is adapted for executing the method mentioned above… a computer program for executing the method mentioned above, when being implemented and running on the control unit system of a three-dimensional tomosynthesis scanner apparatus. The present invention is also directed to a computer program product, comprising code means adapted to execute the method steps according to the method, described above, when loaded into a computer processor)
obtain a first region in a tomographic image included in a three-dimensional image, and second region in the tomographic image included in the three-dimensional image different from the first region (Par. [0010-20]: reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density. It should be possible to apply different reconstruction algorithms for different regions or areas in the volume, depending on the distribution of diagnostically relevant information in the volume to be examined… a method for image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness), comprising the steps of… analyzing a volume in order to detect diagnostically relevant data by determining a type of lesion (like masses, calcifications etc.)… clustering the analyzed volume based on the determined lesion type in at least a first and a second region, wherein the first region has a high degree of diagnostically relevant data and the second region has a low degree of diagnostically relevant data… determining a first reconstruction algorithm for the first region and a second reconstruction algorithm for the second region… system for image reconstruction of a three-dimensional digital breast tomosynthesis volume in a plurality of slabs, having a pre-configurable thickness, wherein the control unit is adapted for executing the method mentioned above… a computer program for executing the method mentioned above, when being implemented and running on the control unit system of a three-dimensional tomosynthesis scanner apparatus. The present invention is also directed to a computer program product, comprising code means adapted to execute the method steps according to the method, described above, when loaded into a computer processor… invention is implemented in hardware or in hardware modules, which may be combined with software modules. The hardware modules are then adapted to perform the functionality of the steps of the method, described above. Accordingly, it is also possible to have a combination of hardware and software modules. The modules are preferably integrated into an existing medical environment, for example into an image acquisition device (CT, x-ray, tomosynthesis apparatus) or in a control unit of such an apparatus; Par. [0024-43]: FIG. 1 is a schematic illustration of a medical tomosynthesis system according to an embodiment of present invention… FIG. 3 is a flowchart of a reconstruction method according to a preferred embodiment of the present invention for reconstructing different volume regions differently… analysis phase is executed before reconstruction of the volume… present invention refers to an image reconstruction method for a three-dimensional digital breast tomosynthesis volume with a plurality of pre-configurable slabs … in the analysis phase the analyzed volume is clustered in different regions, particularly in a first region and in a second region, wherein the first region has a high degree of diagnostically relevant data and wherein the second region has a low degree of diagnostically relevant data… it is possible to determine more than two different regions in the volume, which have (and consist of) different information and which are to be reconstructed differently (for instance with MIP or AIP techniques)… determining and configuring the reconstruction algorithms, wherein different reconstruction algorithms are determined for the first region and for the second region… The regions or the clusters of high and low diagnostically relevant information density may refer to two-dimensional regions or three-dimensional regions in the volume… different reconstruction algorithms may be applied. Particularly, different reconstruction algorithms are executed on the same volume. A first reconstruction algorithm is determined for a first region and a second reconstruction algorithm is determined for the second region (or vice versa); Par. [0061-66]: the analyzed volume is clustered. This means that the three-dimensional volume is classified in different types of clusters or classes. Preferably two clusters are differentiated: a first region or cluster and a second region or cluster. The first region may be defined as having a high degree of diagnostically relevant data (a high density referring to lesions) and wherein the second region has a low degree of diagnostically relevant data (and thus referring to healthy tissue). In other embodiments it is also possible to define more than two regions or clusters, which are going to be distinguished with respect to determining the reconstruction algorithm. With other words, reconstruction is region-specific and lesion-type specific… a first reconstruction algorithm is determined for the first region and a second reconstruction algorithm is going to be determined for the second region or cluster, which has been identified in step 14. As mentioned before, different reconstruction algorithms are executed for the different clusters. For example a maximum intensity projection (MIP technique) could be applied to reconstruct slabs containing calcification clusters and an average intensity projection (AIP technique) may be used to reconstruct slabs through the masses… the identified and determined reconstruction algorithm is applied to the volume clusters according to the before mentioned method steps such that the first region is reconstructed with high resolution in thin slabs and the second region is reconstructed with low resolution and thicker slabs; obtain a first region in a tomographic image included in a three-dimensional image, and a second region [in the tomographic image included in the three-dimensional image] different from the first region (e.g. method and system which considers non-uniformly distribution of lesions in a volume (i.e. a three-dimensional image) acquired by using an image acquisition device, including a three-dimensional tomosynthesis scanner apparatus (i.e. an image processing apparatus), for example, include tomographic image reconstruction methods that apply different reconstruction algorithms for different regions (i.e. first, second, third… Nth different regions, sites, areas, clusters, etc.) in the volume depending on the distribution of diagnostically relevant information in the volume to be examined (i.e. obtain a first region in a tomographic image included in a three-dimensional image, and a second region in the tomographic image included in the three-dimensional image different from the first region), for example, including determining a first reconstruction algorithm for a first region and a second reconstruction algorithm for a second region, in which the first region has a high degree of diagnostically relevant data and the second region has a low degree of diagnostically relevant data, respectively, as indicated above), for example);
set a first slab having a first slab thickness for the first region, and a second slab having a second slab thickness, which is different from the first slab thickness, for the second region; and
generate a projection image regarding the tomographic image using the first slab and the second slab (Par. [0004-20]: tomosynthesis methods have been developed, which acquire several projections of an object… and thereafter reconstruct the three-dimensional distribution of the detected grey values in a detector by means of a tomography reconstruction algorithm… A sequence of projection views is acquired by the digital detector as the X-ray source is rotated to different angular positions… Anatomical structures or objects at different heights (or depths in the breast) are projected differently at different angles. The subsequent image reconstruction algorithm leads to a stack or a slab of slice images of the different depth layers… One of the usual ways of reducing the amount of data for read and for storage in regular computer tomography is the reconstruction of the volume in thick slices. While the modern computer tomographs are capable of producing images of less than 0.5 mm slice thickness (for example in thoracic or in abdominal images), radiologists often read and analyze three-dimensional images reconstructed as thick slices or thick slabs (for example 2 to 5 mm)… provide a reconstruction method and system which considers non-uniformly distribution of lesions in the volume and reconstruction methods, considering these distribution differences and taking into account that different reconstruction algorithms may be applied to different anatomical structures (lesions) in the same volume to be examined… a reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices… reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density… apply different reconstruction algorithms for different regions or areas in the volume, depending on the distribution of diagnostically relevant information in the volume to be examine… a method for image reconstruction of a three dimensional (3D) digital breast tomosynthesis volume (DBT) data in a plurality of slabs (each slab has a configurable thickness)… clustering the analyzed volume based on the determined lesion type in at least a first and a second region, wherein the first region has a high degree of diagnostically relevant data and the second region has a low degree of diagnostically relevant data… determining a first reconstruction algorithm for the first region and a second reconstruction algorithm for the second region… determining a first slab thickness for the first and a second slab thickness for the second region… reconstructing the volume such that the first region is reconstructed with the first reconstruction algorithm (preferably with high resolution and in thin slabs) and the second region is reconstructed with the second reconstruction algorithm (preferably with low resolution and thick slabs)… (different) clusters are reconstructed differently (with different reconstruction algorithms), depending on their information density for diagnostic information content. The reconstruction algorithms differ in resolution and slab thickness… a computer program for executing the method mentioned above, when being implemented and running on the control unit system of a three-dimensional tomosynthesis scanner apparatus. The present invention is also directed to a computer program product, comprising code means adapted to execute the method steps according to the method, described above, when loaded into a computer processor… the invention is implemented in hardware or in hardware modules, which may be combined with software modules. The hardware modules are then adapted to perform the functionality of the steps of the method, described above. Accordingly, it is also possible to have a combination of hardware and software modules. The modules are preferably integrated into an existing medical environment, for example into an image acquisition device (CT, x-ray, tomosynthesis apparatus) or in a control unit of such an apparatus; Par. [0027-33]: present invention refers to an image reconstruction method for a three-dimensional digital breast tomosynthesis volume with a plurality of pre-configurable slabs. The method consists of three phases:… analysis phase is executed before reconstruction of the volume… in the analysis phase the analyzed volume is clustered in different regions, particularly in a first region and in a second region, wherein the first region has a high degree of diagnostically relevant data and wherein the second region has a low degree of diagnostically relevant data (and for example mainly consists of healthy tissue). It should be noted that it is possible to determine more than two different regions in the volume, which have (and consist of) different information and which are to be reconstructed differently (for instance with MIP or AIP techniques)… configuration phase relates to the determination of reconstruction relevant parameters for selecting an appropriate reconstruction algorithm, which best fits, the respective type of image information in that region. Particularly, a type of the region clusters is determined. More specifically, a type of information is determined which is present in the regions which a high degree of diagnostic relevant data. Further, the configuration phase refers to determining and configuring the reconstruction algorithms, wherein different reconstruction algorithms are determined for the first region and for the second region. Further, the slab thickness is configured for the reconstruction algorithm. Here again, the slab thickness may differ for the first and the second region… the first region with a high degree of diagnostically relevant data is reconstructed with high resolution in thin slabs and the second region with low degree of diagnostically relevant data is reconstructed with low resolution and thicker slabs than the first region… In the following, a short explanation and definition of terms, used within this disclosure is given… The term "slab" refers to a stack or a sequence of slices in a tomosynthesis volume. A slab has a given thickness. A slab may be construed as consisting of an amount of slices. The slices may be computed with average or maximum intensity projection techniques. A slice may refer to a two-dimensional image plane of the three-dimensional volume stack. A slice typically has a thickness of 1 Voxel. A slab, usually, comprises a plurality of slices, wherein the amount of slices (and thus the thickness of the slab) is configurable… A typical slab thickness for the first region with high degree of diagnostically relevant information lies in the range of 0.5 mm to 2 mm and a typical second slab thickness for the second region with low information density of diagnostically relevant data is in the range of 2 mm to 10 mm; Par. [0057-71]: the provided volume is analyzed in order to detect and identify diagnostically relevant data, like lesions, suspicious structures and/or clusters of calcifications etc.… Preferably two clusters are differentiated: a first region or cluster and a second region or cluster. The first region may be defined as having a high degree of diagnostically relevant data (a high density referring to lesions) and wherein the second region has a low degree of diagnostically relevant data (and thus referring to healthy tissue)… reconstruction is region-specific and lesion-type specific. Thus a first lesion type is determined as a first cluster and reconstructed with a first reconstruction algorithm, whereas a second lesion type is determined as a second cluster and reconstructed with a second reconstruction algorithm… a first reconstruction algorithm is determined for the first region and a second reconstruction algorithm is going to be determined for the second region or cluster, which has been identified in step 14. As mentioned before, different reconstruction algorithms are executed for the different clusters. For example a maximum intensity projection (MIP technique) could be applied to reconstruct slabs containing calcification clusters and an average intensity projection (AIP technique) may be used to reconstruct slabs through the masses. Moreover, a memory and computationally expensive iterative reconstruction could be used to reconstruct slabs corresponding to micro-calcifications and a less resource intensive ("cheaper") filtered back projection method could be used for the other slices…This has the advantage that different reconstruction techniques may be applied for slices and/or for slabs of the same volume but corresponding to different types of lesions or different types of information density clusters… a slab thickness for the respective region is determined within the same volume. This means that different slabbing methods for slices and slabs of the same volume are applied, which correspond to the respective different types of information density clusters, which have been identified in step 14… the identified and determined reconstruction algorithm is applied to the volume clusters according to the before mentioned method steps such that the first region is reconstructed with high resolution in thin slabs and the second region is reconstructed with low resolution and thicker slabs… the reconstruction result is displayed on a monitor or on another user interface for the purpose of diagnosis… selecting or determining different reconstruction algorithms for the volume clusters, which have been identified by the clustering unit C. Further, the determination unit D is adapted for defining a slab thickness for the respective cluster or region, which has been identified by the clustering unit C. With the determination unit D it is possible to use different reconstruction algorithms and different slabbing methods for the different volume clusters, which have been identified by the clustering unit C. It is possible to apply different reconstruction algorithms and slabbing methods for slices within the slabs and for slabs within the volume, corresponding to the specific type of information density of the respective cluster… The reconstruction unit R is adapted for reconstructing the volume according to the determined parameters, as mentioned above, preferably according to the specific slab thickness for the first and for the second cluster and by means of the determined reconstruction algorithm for the first and second cluster, respectively; set a first slab having a first slab thickness for the first region, and a second slab having a second slab thickness, which is different from the first slab thickness, for the second region; and generate a projection image corresponding to the tomographic image using the first slab corresponding to the first region and the second slab corresponding to the second region (e.g. method and system which considers non-uniformly distribution of lesions in a volume acquired by using an image acquisition device, including a three-dimensional tomosynthesis scanner apparatus, for example, include tomographic image reconstruction methods that apply different reconstruction algorithms for different regions (i.e. first, second, third… Nth different regions, sites, areas, clusters, etc.) in the volume depending on the distribution of diagnostically relevant information in the volume to be examined, for example, including determining a first reconstruction algorithm for a first region and a second reconstruction algorithm for a second region, and determining a first slab thickness for the first and a second slab thickness for the second region, and the slab thickness differs (i.e. is different) for the first and the second region (i.e. set a first slab having a first slab thickness for the first region, and a second slab having a second slab thickness, which is different from the first slab thickness, for the second region), for example, and perform tomographic reconstruction by reconstructing the volume according to according to the specific slab thickness for the first and for the second cluster region and based on the determined reconstruction algorithm for the first and second cluster region, including average or maximum intensity projection techniques, respectively (i.e. and generate a projection image regarding the tomographic image using the first slab and the second slab), as indicated above), for example), but fails to teach the following as further recited in claim 15.
However, Okerlund teaches a region including one of a lung and a heart (Par. [0100-105]: a cardiac region is segmented… First, an initial segmentation is performed to remove the lungs and spine… The lungs may identified by applying a threshold at 550 Houndsfield Units (HU) to the image, where regions of low CT number are identified as potential lung regions… the heart region may be segmented. The Euclidian distance, D, from any point on the image to the closest point on the lungs is calculated… The initial heart segmentation may include all values not in the lungs that are within a Euclidian distance… ribs are removed by finding points on the right and left side of the heart where the ribs connect to both the heart and lungs, hereafter referred to as rib connection points… an image is constructed whose value is equal to 700 in the heart center, defined previously as the region where D>DThresh, and decreases linearly to zero at the pixel that halfway between the center of the heart and the lungs; a region including one of a lung and a heart (e.g. a cardiac region (i.e. a region) is segmented including and a lung region a heart region (i.e. including one of a lung and a heart), as indicated above), for example).
JEREBKO and Okerlund are considered to be analogous art because they pertain to medical image processing applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus for reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density that includes a reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices (as disclosed by JEREBKO) with a region including one of a lung and a heart (as taught by Okerlund, Abstract, Par. [0100-105]) to determine at least one range of phases of a cardiac cycle from which to select a selected phase (e.g., for final imaging or image reconstruction), to generate an image for diagnostic use using imaging information from the selected phase, to generate corresponding intermediate images for each phase of at least one range of phases of a cardiac cycle, and to determine an optimal phase location within the cardiac cycle and/or to address any motion in a selected phase (or phases) to maximize or improve image quality of coronary images (Okerlund, Abstract, Par. [0002-8, 42]).
The combination of JEREBKO and Okerlund, as a whole, teaches the apparatus, as indicated above, but fails to teach the following as further recited in claim 15.
However, Oosawa teaches generate a projection image by projecting (i) pixel values in the first slab onto pixel positions in the first region in the tomographic image, and (ii) pixel values in the second slab onto pixel positions in the second region in the tomographic image (Par. [0002]: method and an apparatus for aiding image interpretation, and to a computer-readable recording medium storing a program therefor. More specifically, the present invention relates to a method and an apparatus for aiding comparative image reading between two projection images by aligning projection images generated from two three-dimensional images representing the same subject photographed at different times, and to a computer-readable recording medium storing a program therefor; Par. [0012-13]: generating first projection images (prj1) of a first region (reg1) in a first three-dimensional image (vol1) representing a subject by projecting, onto a plane perpendicular to a first observation direction (dir1) to the subject in the first three-dimensional image, pixels in the first region specified by a first cross section (sec1)… generating second projection images (prj2) of a second region (reg2) in a second three-dimensional image (vol2) representing the subject photographed at a time different from the first three-dimensional image by projecting, onto a plane perpendicular to a second observation direction to the subject in the second three-dimensional image, pixels in the second region specified by a second cross section (sec2); Par. [0091-98]: image interpretation aiding apparatus 101 of a first embodiment of the present invention generates projection images by projecting pixels in regions specified by cross sections and thicknesses in two CT images representing the same subject but photographed at different times. The image interpretation aiding apparatus 101 then aligns lung fields in the projection images corresponding to each other, and generates subtraction images… first projection image generation means 3 for reading the first CT image data set CT1 from storage means 92, for generating first projection images of a first region in the first CT image CT1 by projecting pixels in the first region specified by the first cross section and the first thickness… second projection image generation means 6 for reading the second CT image data set CT2 from the storage means 92, for generating second projection images of a second region in the second CT image CT2 by projecting pixels in the second region specified by the second cross section and the second thickness; generate a projection image by projecting (i) pixel values in the first slab onto pixel positions in the first region in the tomographic image, and (ii) pixel values in the second slab onto pixel positions in the second region in the tomographic image (e.g. present invention generates projection images (i.e. generate a projection image) by projecting pixels in regions specified by cross sections and thicknesses (i.e. slabs) in two CT images, for example, including generating first projection images of a first region in the a CT image by projecting pixels in the first region specified by the first cross section and the first thickness (i.e. by projecting (i) pixel values in the first slab onto pixel positions in the first region in the tomographic image) and generating second projection images of a second region in the second CT image by projecting pixels in the second region specified by the second cross section and the second thickness (i.e. and (ii) pixel values in the second slab onto pixel positions in the second region in the tomographic image), as indicated above), for example).
JEREBKO, Okerlund , and Oosawa are considered to be analogous art because they pertain to medical image processing applications. Therefore, the combined teachings of JEREBKO, Okerlund , and Oosawa, as a whole, would have rendered obvious the invention recited in claim 15 with a reasonable expectation of success in order to modify the apparatus for reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density that includes a reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices (as disclosed by JEREBKO) with generate a projection image by projecting (i) pixel values in the first slab onto pixel positions in the first region in the tomographic image, and (ii) pixel values in the second slab onto pixel positions in the second region in the tomographic image (as taught by Oosawa, Abstract, Par. [0002, 12-13, 91-98]) to improve alignment accuracy regarding an observation target between projection images representing the observation target in two three-dimensional images of the same subject photographed at different times, and for suppressing artifacts in a subtraction image and to improve diagnostic efficiency (Oosawa, Abstract, Par. [0002-10, 12-13, 58, 60, 91-98]).
Regarding claim 17, is a corresponding method claim rejected as applied to the apparatus claim 15 above.
Claim 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over JEREBKO, in view of Oosawa, as applied to claim 1 above, in further view of Okerlund.
Regarding claim 18, claim 16 is incorporated and the combination of JEREBKO and Oosawa, as a whole, teaches the apparatus (JEREBKO, Par. [0009-19]), as indicated above, but fails to teach the following as further recited in claim 18.
However, Okerlund teaches a non-transitory computer readable storage medium storing a program for causing a computer to execute each step of the image processing method defined in claim 16 (Okerlund, Par. [0152-153]: computer or processor executes a set of instructions that are stored in one or more storage elements, in order to process input data. The storage elements may also store data or other information as desired or needed. The storage element may be in the form of an information source or a physical memory element within a processing machine… The set of instructions may include various commands that instruct the computer or processor as a processing machine to perform specific operations such as the methods and processes of the various embodiments. The set of instructions may be in the form of a software program. The software may be in various forms such as system software or application software and which may be embodied as a tangible and non-transitory computer readable medium).
JEREBKO, Oosawa, and Okerlund are considered to be analogous art because they pertain to medical image processing applications. Therefore, the combined teachings of JEREBKO, Oosawa, and Okerlund, as a whole, would have rendered obvious the invention recited in claim 18 with a reasonable expectation of success in order to modify the apparatus for reconstructing digital breast tomosynthesis volumes with variable slice thickness based on diagnostically relevant information density that includes a reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices (as disclosed by JEREBKO) with a non-transitory computer readable storage medium storing a program for causing a computer to execute each step of the image processing method defined in claim 16 (as taught by Okerlund, Abstract, Par. [0152-153]) to perform specific operations (Okerlund, Abstract, Par. [0002-8, 152-153]).
Regarding claim 19, claim 1 is incorporated and the combination of JEREBKO and Oosawa, as a whole, teaches the apparatus (JEREBKO, Par. [0009-19]), as indicated above, but fails to teach the following as further recited in claim 19.
However, Okerlund teaches wherein the first region and the second region contain different organs (Par. [0100-105]: a cardiac region is segmented… First, an initial segmentation is performed to remove the lungs and spine… The lungs may identified by applying a threshold at 550 Houndsfield Units (HU) to the image, where regions of low CT number are identified as potential lung regions… the heart region may be segmented. The Euclidian distance, D, from any point on the image to the closest point on the lungs is calculated… The initial heart segmentation may include all values not in the lungs that are within a Euclidian distance… ribs are removed by finding points on the right and left side of the heart where the ribs connect to both the heart and lungs, hereafter referred to as rib connection points… an image is constructed whose value is equal to 700 in the heart center, defined previously as the region where D>DThresh, and decreases linearly to zero at the pixel that halfway between the center of the heart and the lungs; wherein the first region and the second region contain different organs (e.g. a cardiac regions (i.e. a first, second, third… Nth region) is segmented including and a lung region a heart region (i.e. wherein the first region and the second region contain different organs), as indicated above), for example).
The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 18.
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/GUILLERMO M RIVERA-MARTINEZ/ Primary Examiner, Art Unit 2677
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