DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Claims 1-20 remain pending and have not been amended. Amendments made to specification overcome all previously held drawing and specification objections.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-2, 4, 10-12, 14-17, and 19, are rejected under 35 U.S.C. 103 as being unpatentable over Hayden (SG 11202105920T A) in view of Bharat (WO 2013080165 A2).
With respect to claim 1, Hayden teaches A computer-implemented method (“The computer is configured to generate a corrected image data from the first set of imaging data and the motion vector offsets and generate at least one static reconstruction image including the target organ during the imaging period from the corrected imaging data.” Paragraph 0006), comprising: obtaining positron emission tomography (PET) data that includes moving tissue of interest (“At step 308, a motion vector is generated for each four-dimensional volumetric image 408a, 408b using target tracking data generated from the dynamic image (or portion of the dynamic image) corresponding to the temporal dimension t of the selected four-dimensional volumetric image 408a, 408b.” paragraph 0034); generating a set of short PET frames from the PET data (“408b (or frames)” paragraph 0031), wherein each short PET frame of the set of short PET frames is based on a time duration (“408b (or frames). Each four-dimensional volumetric image 408a, 408b includes three spatial dimensions (x, y, z) and a temporal dimension (t) corresponding to the predetermined time period selected from the predetermined diagnostic period.” Paragraph 0031); estimating a motion of the tissue of interest based on the short PET frames (see fig. 3 element 308 and “At step 308, a motion vector is generated for each four-dimensional volumetric image 408a, 408b using target tracking data generated from the dynamic image (or portion of the dynamic image) corresponding to the temporal dimension t of the selected four-dimensional volumetric image 408a, 408b.” paragraph 0034); and motion correcting the tissue of interest for the motion (“In some embodiments, the method 300 results in the removal of artefacts, such as artefacts 206a-206b illustrated in FIG. 2B, and allows generation of diagnostic-quality reconstructed images from traditionally non-diagnostic listmode data 402… As shown in FIG. 8A, the static images 502a-502c have significant motion blur and artefacts such that the images are of non-diagnostic quality and cannot be used for patient diagnosis. FIG. 8B illustrates reconstructions of the target tissue 510b generated from the listmode data 402 using the method 100 of motion correction discussed in conjunction with FIGS. 3-7. “ paragraph 0038).
Hayden does not explicitly teach identifying a first tissue of interest in the set of short PET frames; identifying at least a second tissue of interest in the set of short PET frames; estimating, separately and independently, a first motion of the first tissue of interest and a second motion of second tissue of interest based on the short PET frames; and motion correcting, separately and independently, the first tissue of interest for the first motion and the second tissue of interest for the second motion.
Bharat teaches identifying a first tissue of interest (“The segmentation module 18 identifies and delineates between regions, such as targets and organs at risk, in the received images.” Page 5 lines 8-9 ); identifying at least a second tissue of interest (“The segmentation module 18 identifies and delineates between regions, such as targets and organs at risk, in the received images.” Page 5 lines 8-9); estimating, separately and independently, a first motion of the first tissue of interest (“For each sample of collected motion data (i.e., for each determination of shape), rigid motion is estimated. Rigid motion includes, for example, translations and rotations. In some embodiments, non-rigid motion is additionally or alternatively employed. The motion estimates are applied to the locations of each target or organ at risk in the planning image to yield motion compensated locations.” Page 6 lines 3-7) and a second motion of second tissue of interest (“For each sample of collected motion data (i.e., for each determination of shape), rigid motion is estimated. Rigid motion includes, for example, translations and rotations. In some embodiments, non-rigid motion is additionally or alternatively employed. The motion estimates are applied to the locations of each target or organ at risk in the planning image to yield motion compensated locations.” Page 6 lines 3-7); and motion compensating, separately and independently, the first tissue of interest for the first motion (“The motion estimates are applied to the locations of each target or organ at risk in the planning image to yield motion compensated locations. A cumulative motion pattern, such as a probability density functions, for each target and/or organ at risk is determined by accumulating the motion-compensated locations therefor. The more samples collected, the more accurate the cumulative motion patterns” page 6 lines 5-10) and the second tissue of interest for the second motion (“The motion estimates are applied to the locations of each target or organ at risk in the planning image to yield motion compensated locations. A cumulative motion pattern, such as a probability density functions, for each target and/or organ at risk is determined by accumulating the motion-compensated locations therefor. The more samples collected, the more accurate the cumulative motion patterns” page 6 lines 5-10).
Bharat is analogous art reasonably pertinent to the problem of PET motion detection and correction faced by the inventor. Bharat is directed towards a methodology of estimating and compensating tissue motion present in imaging modalities like PET scans (“The imaging modalities 12 suitably include one or more of a computed tomography (CT) scanner, a positron emission tomography (PET) scanner…” page 4 lines 25-26 and “The motion estimates are applied to the locations of each target or organ at risk in the planning image to yield motion compensated locations. A cumulative motion pattern, such as a probability density functions, for each target and/or organ at risk is determined by accumulating the motion-compensated locations therefor. The more samples collected, the more accurate the cumulative motion patterns” page 6 lines 5-10). A person of ordinary skill in the art before the effective filing date of the claimed invention could have reasoned that combining Hayden and Bharat by incorporating the motion estimation and compensation techniques of Bharat (namely, estimating and compensating for a multitude of types of motion found in a multitude of tissues), into the motion estimation and correction process of Hayden and by utilizing Bharat’s display device to display the results of the correction process, would lead to optimization in medical processes reliant on imaging of tissues (“The optimization module 20 optionally receives other relevant inputs, such as an attenuation map indicative of radiation absorption and/or cumulative motion patterns for targets and/or organs at risk” page 5 lines 25-27) and accurately find patterns within motion of tissues (“A cumulative motion pattern, such as a probability density functions, for each target and/or organ at risk is determined by accumulating the motion-compensated locations therefor. The more samples collected, the more accurate the cumulative motion patterns.” Page 6 lines 7-10). Therefore, it would have been obvious for a person of ordinary skill before the effective filing date of the claimed invention to Hayden and Bharat by incorporating the motion estimation and compensation techniques of Bharat (namely, estimating and compensating for a multitude of types of motion found in a multitude of tissues), into the motion estimation and correction process of Hayden and by utilizing Bharat’s display device to display the results of the correction process, with the expectation that doing so would lead to optimization in medical processes reliant on imaging of tissues (“The optimization module 20 optionally receives other relevant inputs, such as an attenuation map indicative of radiation absorption and/or cumulative motion patterns for targets and/or organs at risk” page 5 lines 25-27) and accurately find patterns within motion of tissues (“A cumulative motion pattern, such as a probability density functions, for each target and/or organ at risk is determined by accumulating the motion-compensated locations therefor. The more samples collected, the more accurate the cumulative motion patterns.” Page 6 lines 7-10).
With respect to claim 2, Hayden and Bharat teach the computer-implemented method of claim 1. Hayden further teaches: rendering the motion corrected tissue of interest (“FIGS. 10A and 10B illustrate the scan data of FIGS. 2A and 2B, respectively, after undergoing a motion correction method as disclosed herein. As shown in FIG. 10B, the polar image 210 generated from the plurality of motion corrected static images 208a-208e does not contain any of the defects 206a-206b included in the original polar image 204. By applying the methods and systems disclosed herein, a diagnostic images 208a-208e, 210 can be generated from data that traditionally generated only non-diagnostic images.”paragraph 0040).
Hayden does not teach a display device, the first tissue of interest and the second tissue of interest.
Bharat teaches a display device that displays images (“displays images … on a display device” page 5 line 16-17), the first tissue of interest (“The segmentation module 18 identifies and delineates between regions, such as targets and organs at risk, in the received images.” Page 5 lines 8-9) and the second tissue of interest (“The segmentation module 18 identifies and delineates between regions, such as targets and organs at risk, in the received images.” Page 5 lines 8-9).
With respect to claim 4, Hayden and Bharat teach the computer-implemented method of claim 1. Bharat further teaches wherein at least one of the first motion and the second motion includes at least one of a translation in an x direction, a rotation about the x direction, a translation in a y direction, a rotation about the y direction, a translation in a z direction, and a rotation about the z direction (“For each sample of collected motion data (i.e., for each determination of shape), rigid motion is estimated. Rigid motion includes, for example, translations and rotations.” Page 6 lines 3-4).
With respect to claim 10, Hayden and Bharat teach the computer-implemented method of claim 1. Bharat further teaches wherein at least one of the first motion and the second motion is rigid motion (“For each sample of collected motion data (i.e., for each determination of shape), rigid motion is estimated. Rigid motion includes, for example, translations and rotations.” Page 6 lines 3-4).
With respect to claim 11, because claim 11 recites a computer system where instructions cause the process to execute the limitations present in claim 1, Hayden and Bharat render obvious all of the claim limitations in consideration of claim 1. Hayden additionally teaches a computing system, comprising: a computer readable storage medium memory that includes instructions for correcting motion in data (“a non-transitory computer readable medium storing instructions is disclosed. The instruction are configured to cause a computer system to execute the steps of receiving a first set of imaging data including a plurality if annihilation events detected during an imaging period and generating a plurality of four-dimensional volumetric images from the imaging data for the imaging period. Each four-dimensional volumetric image includes a target organ. The instructions are further configured to cause the computer to execute a step of determining a motion vector offset for each of the plurality of four-dimensional volumetric images. The motion vector offsets are determined using target tracking data generated for the target organ over a time period associated with the four-dimensional volumetric image. The instructions are further configured to cause the computer to execute the steps of generating corrected imaging data from the first set of imaging data and the motion vector offsets” paragraph 0007); and a processor configured to execute the instructions (“computer” paragraph 0007).
With respect for claim 12, because claim 12 is directed towards a computer system where instructions cause the process to execute the limitations present in claim 2, Hayden and Bharat teach the computing system of claim 11 and render obvious all claim limitations in consideration of claim 2. Hayden further teaches instructions causing the processor to execute the rest of the limitations in the claim (“a non-transitory computer readable medium storing instructions is disclosed. The instruction are configured to cause a computer system to execute the steps of receiving a first set of imaging data including a plurality if annihilation events detected during an imaging period and generating a plurality of four-dimensional volumetric images from the imaging data for the imaging period. Each four-dimensional volumetric image includes a target organ. The instructions are further configured to cause the computer to execute a step of determining a motion vector offset for each of the plurality of four-dimensional volumetric images. The motion vector offsets are determined using target tracking data generated for the target organ over a time period associated with the four-dimensional volumetric image. The instructions are further configured to cause the computer to execute the steps of generating corrected imaging data from the first set of imaging data and the motion vector offsets” paragraph 0007).
With respect to claim 14, because claim 14 is directed towards a computer system where instructions cause the process to execute the limitations present in claim 4, Hayden and Bharat teach the computing system of claim 11 and render obvious all claim limitations in consideration of claim 4.
With respect to claim 15, Hayden and Bharat teach the computing system of claim 11. Hayden further teaches non-periodic motion causing blur in pet images (“During the PET imaging procedure, movement, discomfort, and/or physiological reactions of the patient can result in nonperiodic movement within the data. When non-periodic movement is present, significant artefacts and/or motion blur can occur.” paragraph 0028 lines 3-6)
With respect to claim 16, because claim 16 is directed towards a computer readable storage medium encoded with instructions that when executed, cause the processor to complete the limitations present in claim 11, Hayden and Bharat render obvious all claim limitations in consideration of claim 11. Hayden additionally teaches a computer readable storage medium (“a non-transitory computer readable medium storing instructions is disclosed” paragraph 0007 lines 1-2)
With respect to claim 17, because claim 17 is directed towards a computer readable storage medium encoded with instructions that when executed, cause the processor to complete the limitations present in claim 12, Hayden and Bharat teach the computer readable storage medium of claim 16 and render obvious all claim limitations in consideration of claim 12.
With respect to claim 19, because claim 19 is directed towards a computer readable storage medium encoded with instructions that when executed, cause the processor to complete the limitations present in claim 4, Hayden and Bharat teach the computer readable storage medium of claim 16 and render obvious all claim limitations in consideration of claim 4.
Claims 3, 13, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Hayden and Bharat as applied to claims 1, 11, and 16 above, and further in view of Osborne (US 20160358334 A1).
With respect to claim 3, Hayden and Bharat teach the computer-implemented method of claim 1. Bharat further teaches a display device that displays images (“displays images … on a display device” page 5 line 16-17), the first tissue of interest (“The segmentation module 18 identifies and delineates between regions, such as targets and organs at risk, in the received images.” Page 5 lines 8-9), and the second tissue of interest (“The segmentation module 18 identifies and delineates between regions, such as targets and organs at risk, in the received images.” Page 5 lines 8-9)
Osborn further teaches inserting a motion corrected tissue of interest (“One skilled in the art would appreciate that, given that motion correction may be performed on the raw listmode data prior to image reconstruction, the head and neck motion correction techniques described herein may be applied to many PET imaging systems.” Paragraph 0029) at a corresponding location in a rendering of the PET data (“other anatomical modalities may be used to generate a reference point for which the transformation matrix may be generated. For instance, the list mode data itself may be used to generate one or more reference points for the reconstruction of three-dimensional volumes from specific time segments within the acquired list mode data. Thus, an alternative reconstruction process may involve segmenting the list mode data, designating specific segments as corresponding to “stationary” geometries, reconstructing a three-dimensional volume for those designated segments, then combining the reconstructed volumes into a single volume.” Paragraph 31);
Osborne is analogous art in the same field of endeavor as the claimed invention. Osborne is directed towards motion correction of medical images (“The present invention provides methods for motion correction for use in medical imaging systems. These methods require no attached electronic hardware devices or invasive camera systems, and offer high resolution tracking of motion that can automatically detect and correct patient movement during imaging.” Paragraph 0005). A person of ordinary skill in the art before the effective filing date of the claimed invention could have reasoned that combining Hayden, Bharat and Osborne by utilizing its reconstruction methodology alongside the combined systems motion correction process and display technology to display corrected images would lead to enabling the combined system to directly be used in a diagnostic capacity by medical professionals, improve their workflow and performance with better quality images (“Medical imaging systems/scanners (e.g., positron emission tomography (PET), computed tomography (CT), etc.) are typically used for diagnostic purposes. Patient movement during medical imaging, however, can result in degraded image quality and reduced diagnostic confidence.” Paragraph 0003 and “The present invention provides methods for motion correction for use in medical imaging systems. These methods require no attached electronic hardware devices or invasive camera systems, and offer high resolution tracking of motion that can automatically detect and correct patient movement during imaging.” Paragraph 0005). Therefore, it would have been obvious for a person of ordinary skill before the effective filing date of the claimed invention to combine Hayden, Bharat and Osborne by utilizing its reconstruction methodology alongside the combined systems motion correction process and display technology to display corrected images, with the expectation that doing so would lead to enabling the combined system to directly be used in a diagnostic capacity by medical professionals, improve their workflow and performance with better quality images (“Medical imaging systems/scanners (e.g., positron emission tomography (PET), computed tomography (CT), etc.) are typically used for diagnostic purposes. Patient movement during medical imaging, however, can result in degraded image quality and reduced diagnostic confidence.” Paragraph 0003 and “The present invention provides methods for motion correction for use in medical imaging systems. These methods require no attached electronic hardware devices or invasive camera systems, and offer high resolution tracking of motion that can automatically detect and correct patient movement during imaging.” Paragraph 0005).
With respect to claim 13, because claim 13 is directed towards a computer system where instructions cause the process to execute the limitations present in claim 3, Hayden and Bharat teach the computing system of claim 11 and Hayden, Bharat, and Osborne render obvious all claim limitations in consideration of claim 3.
With respect to claim 18, because claim 18 is directed towards a computer readable storage medium encoded with instructions that when executed, cause the processor to complete the limitations present in claim 13, Hayden and Bharat teach the computer readable storage medium of claim 16 and Hayden, Bharat, and Osborne render obvious all claim limitations in consideration of claim 13.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Hayden and Bharat as applied to claim 1 above, and further in view of Wollenweber (US 20160163095 A1).
With respect to claim 5, Hayden and Bharat teach the computer-implemented method of claim 1. Hayden further teaches generating different volumes of interest for the tissue of interest (“a plurality of four-dimensional volumetric images are generated from imaging data for a predetermined imaging period. Each four-dimensional volumetric image includes target tissue. A motion vector offset is determined for each of the plurality of fourdimensional volumetric images. The motion vector offsets are determined using target tracking data generated for the target tissue over a time period associated with the four-dimensional volumetric image.” Paragraph 0025), and where only PET data within the volume of interest is utilized to estimate the motion (“Each four-dimensional volumetric image includes a target organ. The computer is further configured to determine a motion vector offset for each of the plurality of four-dimensional volumetric images.” Paragraph 0006).
Bharat teaches the first tissue of interest (“The segmentation module 18 identifies and delineates between regions, such as targets and organs at risk, in the received images.” Page 5 lines 8-9), and the second tissue of interest (“The segmentation module 18 identifies and delineates between regions, such as targets and organs at risk, in the received images.” Page 5 lines 8-9)
Wollenweber teaches wherein the first volume of interest is less than an entire field of view of the PET data (“a volume of interest (VOI) of the emission imaging data, wherein the VOI defines a volume smaller than an imaged volume of the object.” Paragraph 0004) and only PET data within the first volume of interest is utilized to estimate the first motion (“It may be noted that the VOI may include a single contiguous volume (e.g., a single organ), or multiple, discrete volumes (e.g., multiple organs, lesions, or tumors, among others)” paragraph 0014 and “Various embodiments provide for improved addressing of motion in PET scanning, for example by performing a PCA and/or related motion mitigation on a specified VOI of diagnostic interest that forms a smaller subset of the imaging data for a volume acquired during a scan.” Paragraph 0020); and wherein the second volume of interest is less than the entire field of view of the PET data (“a volume of interest (VOI) of the emission imaging data, wherein the VOI defines a volume smaller than an imaged volume of the object.” Paragraph 0004) and only PET data within the second volume of interest is utilized to estimate the second motion (“It may be noted that the VOI may include a single contiguous volume (e.g., a single organ), or multiple, discrete volumes (e.g., multiple organs, lesions, or tumors, among others)” paragraph 0014 and “Various embodiments provide for improved addressing of motion in PET scanning, for example by performing a PCA and/or related motion mitigation on a specified VOI of diagnostic interest that forms a smaller subset of the imaging data for a volume acquired during a scan. “ paragraph 0020 ), wherein the first volume of interest and the second volume of interest are different volumes of interest (“It may be noted that the VOI may include a single contiguous volume (e.g., a single organ), or multiple, discrete volumes (e.g., multiple organs, lesions, or tumors, among others)” paragraph 0014 ).
Wollenweber is analogous art in the same field of endeavor as the claimed invention. Wollenweber is directed towards imaging apparatus that acquires emission imaging data processing it into volumes of interest where motion is estimated (“a method includes acquiring, with a detector defining a field of view (FOV), emission data (e.g., positron emission tomography (PET) imaging data) of an object over the FOV. The method also includes determining, with one or more processing units, a volume of interest (VOI) of the emission imaging data, wherein the VOI defines a volume smaller than an imaged volume of the object. Further, the method includes performing, with the one or more processing units, a multivariate data analysis (e.g., a principle components analysis (PCA)) on the VOI to generate a waveform for the VOI. Also, the method includes determining, with the one or more processing units, an amount of motion for at least the VOI based on the waveform.” Paragraph 0004). A person of ordinary skill in the art before the effective filing date of the claimed invention could have reasoned that combining Hayden, Bharat and Wollenweber by utilizing its teaching of volumes to further define the volumes and motion estimation processing of Hayden would lead to more user control over the data and how its presented thus enhancing diagnostic effectiveness (“The particular organs selected for inclusion in the VOI, projection into the imaging volume, and use in generating a VOI mask may be determined based on a user input (e.g., a user may select lungs, liver, bladder, or the like, and a VOI corresponding to the selected organs may be identified using an anatomical model)” paragraph 0030 and “display information may be stored and/or communicated for subsequent analysis. The PET display information generated may be one or more reconstructed PET images. For example, a PET image for the FOV or the imaged volume of the object may be generated, using motion mitigation information as appropriate for the VOI within the FOV or the imaged volume of the object.” Paragraph 0035). Therefore, it would have been obvious for a person of ordinary skill before the effective filing date of the claimed invention to combine Hayden, Bharat and Wollenweber by utilizing its teaching of volumes to further define the volumes and motion estimation processing of Hayden, with the expectation that doing so would lead to more user control over the data and how its presented thus enhancing diagnostic effectiveness (“The particular organs selected for inclusion in the VOI, projection into the imaging volume, and use in generating a VOI mask may be determined based on a user input (e.g., a user may select lungs, liver, bladder, or the like, and a VOI corresponding to the selected organs may be identified using an anatomical model)” paragraph 0030 and “display information may be stored and/or communicated for subsequent analysis. The PET display information generated may be one or more reconstructed PET images. For example, a PET image for the FOV or the imaged volume of the object may be generated, using motion mitigation information as appropriate for the VOI within the FOV or the imaged volume of the object.” Paragraph 0035).
Claims 6 , 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Hayden, Bharat, and Wollenweber as applied to claim 5 above, and further in view of Tang (US 20220323035 A1).
With respect to claim 6, Hayden, Bharat, and Wollenweber teach the computer-implemented method of claim 5. Hayden, Bharat, and Wollenweber do not explicitly teach determining a first patch within the first volume of interest to motion correct; and determining a second patch within the second volume of interest to motion correct.
Tang teaches determining a patch within a volume of interest to correct (“Next, in block B820, the image-generation device generates PARs of a patch that includes the contour. For example, FIG. 9A illustrates a patch 1027 that includes the contour 1026” paragraph 0074, see also figure 8 B835).
Tang is analogous art in the same field of endeavor as the claimed invention. Tang is directed towards motion correction of images taken by various forms of anatomical scanners including PET (“The one or more image-generation devices 110 are configured to perform motion-correction operations while generating reconstructed images.” Page 26 paragraph 0043 lines 17-19). A person of ordinary skill in the art before the effective filing date of the claimed invention could have reasoned that combining Hayden, Bharat, Wollenweber and Tang by utilize the teachings of tang to incorporate the partitioning by patching method within the combined system’s multi-volume motion correction process, would lead to the combined system being able to isolate specific areas moving during image capturing further allowing it to motion correct and reconstruct these areas (“For example, FIG. 9A illustrates an example embodiment of a contour 1026 of a track caused by a moving object. In this example, the object is a vessel, and the contour 1026 delineates the area in the half reconstruction 1025 in which the vessel moved during the capture of the scan data.” Page 29 paragraph 0073 lines 6-16 And “Next, in block B820, the image-generation device generates PARs of a patch that includes the contour. For example, FIG. 9A illustrates a patch 1027 that includes the contour 1026.” Paragraph 0074 And “The one or more image-generation devices 110 are configured to perform motion-correction operations” page 26 paragraph 0043 lines 17-19). Therefore, it would have been obvious for a person of ordinary skill before the effective filing date of the claimed invention to combine Hayden, Bharat, Wollenweber and Tang by utilize the teachings of Tang to incorporate the partitioning by patching method within the combined system’s multi-volume motion correction process, with the expectation that doing so would lead the combined system being able to isolate specific areas moving during image capturing further allowing it to motion correct and reconstruct these areas (“For example, FIG. 9A illustrates an example embodiment of a contour 1026 of a track caused by a moving object. In this example, the object is a vessel, and the contour 1026 delineates the area in the half reconstruction 1025 in which the vessel moved during the capture of the scan data.” Page 29 paragraph 0073 lines 6-16 And “Next, in block B820, the image-generation device generates PARs of a patch that includes the contour. For example, FIG. 9A illustrates a patch 1027 that includes the contour 1026.” Paragraph 0074 And “The one or more image-generation devices 110 are configured to perform motion-correction operations” page 26 paragraph 0043 lines 17-19).
With respect to claim 7, Hayden, Bharat, and Wollenweber teach the computer-implemented method of claim 6. Hayden, Bharat, and Wollenweber do not explicitly teach the additional claim limitations. Tang teaches, where a volume of interest has a first size a patch has a second size, and the second size is equal to the first size (“Also, FIG. 7A illustrates a patch 1027 that includes an object or a part of an object.” Volume as object page 28 paragraph 0064 lines 5-6).
With respect to claim 8, Hayden, Bharat, and Wollenweber teach the computer-implemented method of claim 6. Hayden, Bharat, and Wollenweber do not explicitly teach the additional claim limitations. Tang teaches where a volume has a first size, a patch has a second size and the second size is smaller than the first size (“The flow then moves to block B615, where the image-generation device identifies one or more patches, in the half reconstruction” paragraph 0064 lines 1-3)
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Hayden, Bharat, and Wollenweber as applied to claim 5 above, and further in view of Kabus (WO 2012004742 A1).
With respect to claim 9, Hayden, Bharat, and Wollenweber teach the computer-implemented method of claim 5. However, they do not explicitly teach the rest of the claim limitations. Kabus teaches modifying the size of the first volume of interest (“One reason for this observation may be
that large deformations providing more accurate alignment often lead to deformations resulting in unreasonably large volume changes. “ page 4 lines 19-21 and “In an embodiment of the system, the metric of the local volume change is based on the Jacobian metric. In an embodiment of the system, the metric unit is further arranged for computing an image-intensity-based metric of the local volume change at the plurality of locations, based on the first and second image, and the local property of the first or second image defined at the plurality of locations is the computed image-intensity-based metric.” Page 4 lines 23-28 , deformation as volume change ) and the size of the second volume of interest (“One reason for this observation may be that large deformations providing more accurate alignment often lead to deformations resulting in unreasonably large volume changes. “ page 4 lines 19-21 and “In an embodiment of the system, the metric of the local volume change is based on the Jacobian metric. In an embodiment of the system, the metric unit is further arranged for computing an image-intensity-based metric of the local volume change at the plurality of locations, based on the first and second image, and the local property of the first or second image defined at the plurality of locations is the computed image-intensity-based metric.” Page 4 lines 23-28 , deformation as volume change ) for a short PET frame (“first and second image” page 4 line 27) during motion estimation (“Based on the value of the conformity measure, the DFV estimating the motion is validated. Experiments show that the conformity measure based on the computed metric of a local volume change at a plurality of locations and the local property of the first or second image, defined at the plurality of locations” page 4 lines 15-18) based on the estimated motion (“Based on the value of the conformity measure, the DFV estimating the motion is validated. Experiments show that the conformity measure based on the computed metric of a local volume change at a plurality of locations and the local property of the first or second image, defined at the plurality of locations” page 4 lines 15-18, conformity measure used to validate motion estimation).
Kabus is analogous art in the same field of endeavor as the claimed invention. Kabus is directed towards medical imaging motion correction and estimation (“In general, this invention relates to automatic point-wise validation of motion estimation, wherein the motion is estimated by a deformation vector field for transforming a first image at a first phase of the motion into a second image at a second phase of the motion. In particular, this invention relates to automatic point-wise validation of respiratory motion estimation on the basis of CT images.” Field of invention). A person of ordinary skill in the art before the effective filing date of the claimed invention could have reasoned that combining Hayden, Bharat , Wollenweber and Kabus by utilizing Kabus’ teachings of the impact of volumetric changes on motion estimation processes would lead to improvements in motion compensation and image registration by eliminating erroneous measurements caused by volumetric changes (“Typically, an image registration scheme aims at balancing two types of forces: an outer force driven by the difference of the two images and an inner force driven by a physical model. Consequently, a weighting factor is introduced to balance these two forces. Generally, the application of a large weight on the outer force is likely to yield a small residuum image. Unfortunately, it often introduces incorrect deformations, even folding, into the DVF. Therefore, using a residuum image for validating a DVF may often lead to an erroneous determination of the DVF. It would be useful to provide a validation scheme for reducing the likelihood of positive validation of an erroneously estimated DVF.” Page 3 lines 27-33 and page 4 lines 1-2). Therefore, it would have been obvious for a person of ordinary skill before the effective filing date of the claimed invention to combine Hayden, Bharat , Wollenweber and Kabus by utilizing Kabus’ teachings of the impact of volumetric changes on motion estimation processes , with the expectation that doing so would lead to improvements in motion compensation and image registration by eliminating erroneous measurements caused by volumetric changes (“Typically, an image registration scheme aims at balancing two types of forces: an outer force driven by the difference of the two images and an inner force driven by a physical model. Consequently, a weighting factor is introduced to balance these two forces. Generally, the application of a large weight on the outer force is likely to yield a small residuum image. Unfortunately, it often introduces incorrect deformations, even folding, into the DVF. Therefore, using a residuum image for validating a DVF may often lead to an erroneous determination of the DVF. It would be useful to provide a validation scheme for reducing the likelihood of positive validation of an erroneously estimated DVF.” Page 3 lines 27-33 and page 4 lines 1-2).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Hayden and Bharat as applied to claim 16 above, and further in view of Thielemans (US 20090154641 A1).
With respect to claim 20, Hayden and Bharat teach the computer readable storage medium of claim 16, but don’t explicitly teach the further limitations. Thielemans teaches where observed motion in pet images includes affine motion (“The above methods for motion correction are applicable to arbitrary motions, including non-rigid motion. The third embodiment is preferably applied to motions where the positions of the body at different instants of time are connected by an affine transformation, including rigid motion of the body.” Paragraph 0023)
Thielemans is analogous art in the same field of endeavor as the claimed invention. Thielemans is directed towards motion correction in medical imaging (“The invention relates to a method of and software for conducting motion correction in tomographic scanning and a system for tomographic scanning using this method, in particular but not exclusively a positron emission tomography (PET) scanning system.” Field of invention). A person of ordinary skill in the art before the effective filing date of the claimed invention could have reasoned that combining Hayden, Bharat and Thielemans by incorporating its teachings of affine motion in PET images would lead to an improved motion correction strategy that takes into account affine motion which can lead to blur and/or poor registration or diagnosis capabilities (“The above methods for motion correction are applicable to arbitrary motions, including non-rigid motion. The third embodiment is preferably applied to motions where the positions of the body at different instants of time are connected by an affine transformation, including rigid motion of the body.” Paragraph 0023). Therefore, it would have been obvious for a person of ordinary skill before the effective filing date of the claimed invention to combine Hayden, Bharat and Thielemans by incorporating its teachings of affine motion in PET images, with the expectation that doing so would lead to an improved motion correction strategy that takes into account affine motion which can lead to blur and/or poor registration or diagnosis capabilities (“The above methods for motion correction are applicable to arbitrary motions, including non-rigid motion. The third embodiment is preferably applied to motions where the positions of the body at different instants of time are connected by an affine transformation, including rigid motion of the body.” Paragraph 0023).
Response to Arguments
Applicant's arguments filed 11/20/2025 have been fully considered.
With respect to the 101 rejections, the examiner agrees and withdraws them recognizing the disavowal of transitory signals present in the specification.
With respect to the 103 rejections, the majority of applicant’s arguments pertain to Bharat and Hayden combination in the rejection of claims 1, 11, and 16. Applicant first argues that “Hayden does not teach motion correcting even a single image “ (see Remarks page 10 lines 2-3 and page 11 paragraph 5). The examiner disagrees and points out figure 3 in Hayden, which specifically visualizes the flow of execution including receiving PET imaging data, generating volumetric images, generating dynamic images which show organ motion, determining a motion vector for each volumetric image and then shifting data based on motion vector to reconstruct static PET images.
Applicant then argues that Bharat does not disclose or suggest motion raw PET data or images and that the combination of Hayden and Bharat wouldn’t be obvious because of the difference in how motion compensation is used in Hayden and Bharat (page Remarks 12 paragraph 1). The examiner respectfully disagrees. Hayden and Bharat both estimation motion overtime in 4d images. Hayden looks at organ movement overtime while Bharat estimates specific forms of motion and applies them to images (inclusive of PET images (see Bharat page 3 (page 2 if not counting cover) line 26)) to generate motion compensated locations which are then accumulated to determine a cumulative motion pattern (see Bharat page 6 (5 if not counting cover) paragraph 1 "The motion module 22, in some embodiments, further works in conjunction with the other modules to facilitate the generation of a motion compensated treatment plan. For each sample of collected motion data (i.e., for each determination of shape), rigid motion is estimated. Rigid motion includes, for example, translations and rotations. In some embodiments, non-rigid motion is additionally or alternatively employed. The motion estimates are applied to the locations of each target or organ at risk in the planning image to yield motion compensated locations. A cumulative motion pattern, such as a probability density functions, for each target and/or organ at risk is determined by accumulating the motion-compensated locations therefor. The more samples collected, the more accurate the cumulative motion patterns."). It would be obvious, due to the substantial similarities of Hayden in its teachings of motion compensation and Bharat’s cumulative motion estimation (as indicated above), to incorporate (or combine) their teachings, as previously stated in the motivation combination statement (see above claim mapping).
Applicant then argues that Bharat nor Hayden teach independently or in combination the claim 1 limitations of “estimating, separately and independently, a first motion of the first tissue of interest and a second motion of second tissue of interest based on the short PET frames; and motion correcting, separately and independently, the first tissue of interest for the first motion and the second tissue of interest for the second motion.” (See remarks page 12). The examiner disagrees. Bharat teaches preforming motion estimation and motion correction on different targets or at-risk organs (see above claim mapping). Furthermore, Bharat teaches estimating motion for each determination of shape and generating motion compensated locations based on the applied estimated motion for each target or at-risk organ (see above claim mapping). Since these targets or organs are delineated or segmented away from each other (separated) and the motion is estimated and motion compensated locations are generated for each delineated area (independently).
On page 13, applicant argues that Bharat does not disclose that images or raw PET data can be motion-compensated. The examiner disagrees. Bharat teaches compensating motion estimated from targets or at-risk organs. Bharat identifies motion and these locations using image data. The motion information present in the inputted image data at locations in the inputted data is what is being motion compensated. That aside, claim 1 calls for motion correction. This limitation is fulfilled by taking both Hayden and Bharat in combination. Further on page 13, the applicant argues that Bharat and Hayden exist in stark contrast and that “The motion compensation information of Bharat used to motion compensate the dose distribution cannot be used to motion-compensate raw PET data, at least not in view of Bharat and Hayden”. The examiner disagrees with the assertion that these sources exist in “stark contrast”. Although for different purposes, as stated above, Bharat and Hayden both teach estimation motion overtime in images with Hayden’s motion vector functioning similarly to Bharat’s cumulative motion information. It would be obvious, due to the substantial similarities of Hayden in its teachings of motion compensation and Bharat’s cumulative motion estimation (as indicated above), to incorporate (or combine) their teachings, as previously stated in the motivation combination statement (see above claim mapping). Walking through the combination, Bharat shows applying and estimating motion information separately and independently to a plurality of independent locations of interest, using images, but does not teach motion correction (see above claim mapping). While Hayden teaches motion correcting and estimation using 4D volumetric PET images (see figure 3), but does not teach doing so separately or independently in different tissues of interest. Taken in combination these sources together render obvious claim 1 (see above claim mapping for combination motivation statement).
On pages 13 (last two paragraphs) – 14, the applicant argues that Bharat’s cumulative motion information is used to generate treatment plans and that it does not teach where using motion estimation to compensate raw PET data and again argues that Hayden and Bharat as combined do not disclose the limitations of claim 1 both because Bharat doesn’t mention manipulating listmode data and because it is directed towards generating treatment plans. The examiner disagrees. First the motion vector of Hayden is determined for each volumetric image, this information is then used to motion correct the listmode data (see Hayden figure 3). Similarly, Bharat estimates motion using 4d images. Incorporation or combination of these motion estimation teachings does not necessitate them occurring on the listmode data, because neither source teaches that and though Bharat may not teach correcting listmode data, it was never stated that it did, nor is correcting listmode data a limitation of claim 1. Nonetheless applicant is reminded that independently Bharat does not have to teach every limitation. Walking through the combination, Bharat shows applying and estimating motion information separately and independently to a plurality of independent locations of interest, using images, but does not teach motion correction (see above claim mapping). While Hayden teaches motion correcting and estimation using 4D volumetric PET images (see Hayden figure 3), but does not teach doing so separately or independently in different tissues of interest. Taken in combination these sources together render obvious claim 1 (see above claim mapping for combination motivation statement). Secondly, the intended use of Bharat holds no bearing on the combination itself as the segmentation (tissue identification), motion estimation and compensation processes are not dependent or the generation of a treatment plan. In other words, the intended use of Bharat is non limiting.
Because the examiner disagrees on the above arguments with respect to claim 1 and applicant holds claim 1 as applying “mutatis mutandis” (see page 14 last paragraph) to claims 11 and 16, the examiner maintains the 103 rejections for claims 1, 11 and 16. On page 14-16 applicant argues that the rejection claims dependent on claims 1, 11, and 16 (claims 2-10, 12-15, and 17- 20) should be withdrawn because the combination of Bharat and Hayden fails to satisfy the independent claims with any additional source, used to teach further limitations, not being curative. Due to the rejections of the independent claims being maintained the examiner fines this argument moot and maintains the rejections on the dependent claims accordingly.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(US 20220047227 A1) Heukensfeldt – discloses methods and systems for motion detection in PET images
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/REBECCA COLETTE WILLIAMS/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677