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 .
EXAMINER’S COMMENT
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/20/2026 has been entered.
Response to Arguments
Regarding claim rejections under 35 USC § 103:
Applicant's arguments filed 05/20/2026 have been fully considered but they are not persuasive.
The Applicant contends:
“Applicants respectfully traverse the Examiner's rejection of claims 1-21. More specifically, with the response applicant have amended independent claims 1 and 16 to each require that the radiation source is rotated around the detector. Support for this limitation is found in the application as filed in at least in paragraph [0053] and FIG. 4. In contrast, as correctly stated by the Examiner, the prior art described in the present application does not show or disclose the 3D volume defined in a pseudo parallel geometry based on a zero-angle selected from the plurality of 2D projection images. However, while Nett discloses a representation of the operation of the pseudo parallel geometric correction and/or reconstruction to be employed by the claimed system and method, the disclosure or suggestion in Nett is exclusively for use of the pseudo parallel geometric correction and/or reconstruction with regard to a C-arm geometry in which the radiation source and the detector rotate about the object being imaged. As such, there is no suggestion to combine the references due to the lack of disclosure or suggestion in Nett of anything other than a C-arm geometry acquisition system. As a result, the subject matter of claims 1 and 16 is not shown or suggested by the cited prior art, such that claim 1, as well as claims 2-15 that depend from claim 1, and claim 16, as well as claims 17-21 that depend from claim 16, are allowable. Applicants therefore respectfully request that the Examiner withdraw the rejections to claims 1-21”
The Examiner disagrees, and asserts that, AAPA specifically discloses “In order to assist in creating more accurate volumes and improve artefact correction within the reconstruction of 2D and 3D images, certain systems and methods have been developed, such as that disclosed in U.S. Pat. No. 11,227,418 (the '418 Patent), entitled Systems and Methods for Deep Learning-Based Image Reconstruction, the entirety of which is expressly incorporated herein by reference for all purposes. In this system and method, the system obtains a plurality of two-dimensional (2D) tomosynthesis projection images of an organ by rotating an x-ray emitter to a plurality of orientations relative to the organ and emitting a first level of x-ray energization from the emitter for each projection image of the plurality of 2D tomosynthesis projection images, reconstructs a three-dimensional (3D) volume of the organ from the plurality of 2D tomosynthesis projection images, obtains an x-ray image of the organ with a second level of x-ray energization, generates a synthetic 2D image generation algorithm from the reconstructed 3D volume based on a similarity metric between the synthetic 2D image and the x-ray image, and deploys a model instantiating the synthetic 2D image generation algorithm.”
AAPA discloses “obtaining the plurality of 2D projection images by rotating the radiation source around the detector” (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application including U.S. Pat. No. 11,227,418 also published under US 20200211240 A1 figure 1 and 2). “In order to assist in creating more accurate volumes and improve artefact correction within the reconstruction of 2D and 3D images, certain systems and methods have been developed, such as that disclosed in U.S. Pat. No. 11,227,418 (the '418 Patent), entitled Systems and Methods for Deep Learning-Based Image Reconstruction, the entirety of which is expressly incorporated herein by reference for all purposes. In this system and method, the system obtains a plurality of two-dimensional (2D) tomosynthesis projection images of an organ by rotating an x-ray emitter to a plurality of orientations relative to the organ and emitting a first level of x-ray energization from the emitter for each projection image of the plurality of 2D tomosynthesis projection images, reconstructs a three-dimensional (3D) volume of the organ from the plurality of 2D tomosynthesis projection images, obtains an x-ray image of the organ with a second level of x-ray energization, generates a synthetic 2D image generation algorithm from the reconstructed 3D volume based on a similarity metric between the synthetic 2D image and the x-ray image, and deploys a model instantiating the synthetic 2D image generation algorithm.”)
Paragraph [0053] of US 20200211240 A1 indicates that “FIG. 2 illustrates example movement of the source/emitter 140 relative to an organ O. As the emitter 140 is rotated about the organ, the emitter 140 may further include beam shaping (not depicted) to direct the X-rays through the organ to the detector 145. As shown in the example of FIG. 2, the object O is positioned between a lower support 202 and a compression support 204 to compress the object, and the detector 145 is integrated into the lower support 202. In other examples, the object/organ can be positioned with respect to the detector 145 without supports 202 and/or 204. The emitter 140 can be rotatable about the organ O to a plurality of orientations with respect to the organ O, for example. In an example, the emitter 140 may rotate through a total arc of 30 degrees relative to the organ O or may rotate 30 degrees in each direction (clockwise and counterclockwise) relative to the organ O. It will be recognized that these arcs of rotation are merely examples and not intended to be limiting on the scope of the angulation which may be used.”
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Nett discloses the 3D volume defined in a pseudo parallel geometry based on the zero angle from the plurality of 2D projection images (abstract section II figure 1 “We present a method for utilizing parallel beam reconstruction algorithms in an internally consistent manner. The concept of a virtual image object is utilized. This virtual image object has the property that cone-beam projections through the real object are directly related to parallel-beam projections of the virtual object. The virtual object may then be reconstructed using any algorithm derived for parallel beam projections. Finally, an
affine transform may be applied to the virtual image object in order to generate the reconstruction result.”)
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
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 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 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over Applicant Admitted Prior Art (AAPA page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053] of the present Application) in view of Nett ("Planar tomosynthesis reconstruction in a parallel-beam framework via virtual object reconstruction." Medical Imaging 2007)
Regarding claim 1, AAPA discloses correcting artefacts within a three-dimensional (3D) volume reconstructed from a plurality of two-dimensional (2D) projection images of an object comprising a. providing an imaging system comprising: i. a radiation source; ii. a detector positionable to receive radiation emitted from the radiation source and passing through an object positioned between the source and the detector; iii. a display for presenting information to a user; iv. a processing unit connected to the display and operable to control the operation of the radiation source and detector to generate a plurality of 2D projection images of the object; and v. a memory operably connected to the processing unit and storing processor-executable code for a reconstruction algorithm that when executed by the processing unit is operable to reconstruct a three-dimensional (3D) volume of the organ from the plurality of 2D projection images; b. obtaining the plurality of 2D projection images by rotating the radiation source around the detector; c. selecting a zero angle from a range of angles over which the plurality of 2D projection images are obtained; d. reconstructing the 3D volume from the plurality of 2D projection images (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application including U.S. Pat. No. 11,227,418 also published under US 20200211240 A1 figure 1 and 2 “FIG. 2 illustrates example movement of the source/emitter 140 relative to an organ O. As the emitter 140 is rotated about the organ, the emitter 140 may further include beam shaping (not depicted) to direct the X-rays through the organ to the detector 145.”).
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AAPA doesn’t specifically disclose the 3D volume defined in a pseudo parallel geometry based on the zero angle from the plurality of 2D projection images.
Nett discloses the 3D volume defined in a pseudo parallel geometry based on the zero angle from the plurality of 2D projection images (abstract section II figure 1 “We present a method for utilizing parallel beam reconstruction algorithms in an internally consistent manner. The concept of a virtual image object is utilized. This virtual image object has the property that cone-beam projections through the real object are directly related to parallel-beam projections of the virtual object. The virtual object may then be reconstructed using any algorithm derived for parallel beam projections. Finally, an
affine transform may be applied to the virtual image object in order to generate the reconstruction result.”)
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AAPA and Nett are analogous art because they are from the same field of communications. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to incorporate in the technique disclosed by AAPA the parallel-beam framework via virtual object reconstruction disclosed by Nett. The suggestion/motivation for doing so would have been to reduce artifacts (Nett abstract). See also KSR. In the KSR case, the Court stated that in certain circumstances what is obvious to try is also obvious, such as where "there is a design need or market pressure to solve a problem, and there are a finite number of identified, predictable solutions, a person of ordinary skill has good reason to pursue the known options within his or her technical grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense." Regarding hindsight, the Court found that "[r]igid preventive rules that deny fact finders recourse to common sense . . . are neither necessary under our case law nor consistent with it." The Court stated that "familiar items may have obvious uses beyond their primary purposes," analogizing an obvious invention to the fitting together of pieces to a puzzle. The Court in this regard further stated that the person of ordinary skill is also a person of ordinary creativity, and not "an automaton."
Regarding claim 2, AAPA and Nett disclose claim 1, AAPA also discloses reconstructing a 3D virtual object defined in the pseudo parallel geometry based on the zero angle from the plurality of 2D projection images and correcting the 3D virtual object to form the 3D volume (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application “To correct the artefacts at locations p1 p2 and p3, a reconstruction algorithm that contains networks (e.g., CNNs) to reduce artefacts must be trained with artefacts of all possible appearance and orientations to be able to accommodate for the angular positions of the artefacts with respect to changes in orientation, and must also be tested against the variability in artefact appearance”)
Regarding claim 3, AAPA and Nett disclose claim 2, AAPA also discloses a network operable to reconstruct and correct the 3D virtual object defined in the pseudo parallel geometry based on the zero angle from the plurality of 2D projection images (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application “To correct the artefacts at locations p1 p2 and p3, a reconstruction algorithm that contains networks (e.g., CNNs) to reduce artefacts must be trained with artefacts of all possible appearance and orientations to be able to accommodate for the angular positions of the artefacts with respect to changes in orientation, and must also be tested against the variability in artefact appearance”).
Regarding claim 4, AAPA and Nett disclose claim 3, AAPA also discloses plurality of 2D projection images is selected from a 2D or 2.5D network (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application “The algorithm can contain CNNs in the projection domain and/or CNNs in the volume domain. In the volume domain, the CNN can process each plane separately (i.e., a 2D CNN), each plane with some context of or from the neighboring planes (i.e., a 2.5D CNN) or use 3D CNN (using local 3D neighborhoods and 3D features).”).
Regarding claim 5, AAPA and Nett disclose claim 4, AAPA also discloses a network that reconstructs and corrects the 3D virtual object along an XZ plane defined by the imaging system (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application " Moreover, while a simple 2D CNN operating in the XZ planes could correct efficiently artefacts close to the chest wall where they are totally contained in these XZ planes, e.g. at point p1, but the 2D trained CNN would not be as efficient at correction of artefacts further from the XZ plane e.g., at point p2.”).
Regarding claim 6, AAPA and Nett disclose claim 4, AAPA also discloses a network that reconstructs and corrects the virtual object along an XY plane defined by the imaging system (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application "For the determination and/or identification of the various views of the object, a coordinate system 2000 is defined by the imaging system 2001, such that the various views of the imaged object can be selected along one or more of the XY plane 2002, XZ plane 2004 or YZ plane 2006, as defined by the coordinate system 2000, e.g., where the XY planes are all planes parallel to the XY plane, (same for XZ, YZ).”).
Regarding claim 7, AAPA and Nett disclose claim 3, AAPA also discloses a convolutional neural network (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application “A reconstruction algorithm that contains networks (e.g., CNNs) to reduce artefacts must be trained with artefacts of all possible appearance and orientations to be able to accommodate for the angular positions of the artefacts with respect to changes in orientation, and must also be tested against the variability in artefact appearance”).
Regarding claim 8, AAPA and Nett disclose claim 3, AAPA also discloses a back projection operator and a forward projection operator, and wherein the back projection and the forward projection operators are implemented in the pseudo parallel geometry (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application “While the system and methods of the '418 patent and of the '123 application enhance the reconstruction of the 3D volume in comparison with prior reconstruction systems and processes, the disclosures of the '418 patent and the '123 application still require the utilization of a complex deep learning algorithm trained to attenuate and/or remove artefacts in a manner that employs the natural geometry of the object to be reconstructed using the known conic X-ray beam shape to perform the operations of backprojection and forward projection”).
Regarding claim 9, AAPA and Nett disclose claim 8, AAPA also discloses learned primal dual reconstruction network (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application “learned primal dual reconstructions disclosed in Jonas Teuwen, Nikita Moriakov, Christian Fedon, Marco Caballo, Ingrid Reiser, Pedrag Bakic, Eloy García, Oliver Diaz, Koen Michielsen, Ioannis Sechopoulos, “Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation”, Medical Image Analysis, Volume 71, 2021, 102061, ISSN 1361-8415, the entirety of which are each expressly incorporated herein by reference for all purposes”).
Regarding claim 10, AAPA and Nett disclose claim 3, AAPA also discloses a 3D slice, a 3D slab, a 3D plane, a 3D volume and combinations thereof (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application “DBT and/or CE-DBT creates a three-dimensional (3D) image of the breast using x-rays. By taking multiple x-ray pictures of each breast from many angles, a computer can generate a 3D image used to detect abnormalities.”).
Regarding claim 11, AAPA and Nett disclose claim 1, AAPA also discloses a 3D slice, a 3D slab, a 3D plane, a 3D volume and combinations thereof (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application “DBT and/or CE-DBT creates a three-dimensional (3D) image of the breast using x-rays. By taking multiple x-ray pictures of each breast from many angles, a computer can generate a 3D image used to detect abnormalities.”).
Regarding claim 12, AAPA and Nett disclose claim 1, AAPA also discloses to reconstruct and correct a 3D virtual object defined in the pseudo parallel geometry based on the zero angle from the plurality of 2D projection images, by reconstructing the 3D virtual object defined in the pseudo parallel geometry based on the zero angle from the plurality of 2D projection images and providing the 3D virtual object as an input to the network (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application "Planar tomosynthesis reconstruction in a parallel-beam framework via virtual object reconstruction." Medical Imaging 2007 “With reference now to FIGS. 6A-6B, a representation of the operation of the pseudo parallel geometric correction and/or reconstruction to be employed by the AI 152/CNN 154 is illustrated, an operation which is not discussed at length herein but is disclosed in Nett, Brian E., Shuai Leng, and Guang-Hong Chen. “Planar tomosynthesis reconstruction in a parallel-beam framework via virtual object reconstruction.” Medical Imaging 2007: Physics of Medical Imaging. Vol. 6510. SPIE, 2007 (Nett), incorporated by reference herein in its entirety for all purposes”).
Regarding claim 13, AAPA and Nett disclose claim 11, AAPA also discloses to the network is trained in a supervised manner on a database where a ground truth has been mapped to a virtual ground truth according to the pseudo parallel geometry (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application "Planar tomosynthesis reconstruction in a parallel-beam framework via virtual object reconstruction." Medical Imaging 2007 “With reference now to FIGS. 6A-6B, a representation of the operation of the pseudo parallel geometric correction and/or reconstruction to be employed by the AI 152/CNN 154 is illustrated, an operation which is not discussed at length herein but is disclosed in Nett, Brian E., Shuai Leng, and Guang-Hong Chen. “Planar tomosynthesis reconstruction in a parallel-beam framework via virtual object reconstruction.” Medical Imaging 2007: Physics of Medical Imaging. Vol. 6510. SPIE, 2007 (Nett), incorporated by reference herein in its entirety for all purposes” … Nett “The concept of a virtual image object is utilized. This virtual image object has the property that cone-beam projections through the real object are directly related to parallel-beam projections of the virtual object. The virtual object may then be reconstructed using any algorithm derived for parallel beam projections”).
Regarding claim 14, AAPA and Nett disclose claim 11, AAPA also discloses the network is trained from a 2D image database obtained from 1D projections (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application including in Nett "To correct the artefacts at locations p1 p2 and p3, a reconstruction algorithm that contains networks (e.g., CNNs) to reduce artefacts must be trained with artefacts of all possible appearance and orientations to be able to accommodate for the angular positions of the artefacts with respect to changes in orientation, and must also be tested against the variability in artefact appearance”).
Regarding claim 15, AAPA and Nett disclose claim 11, AAPA also discloses the network is trained on a database including synthetic numerical object phantoms derived from CT or MRI scans of organs of interest, breast CT or breast MRI scans and chest CT or chest MRI scans (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application including in Nett " Further, PCT Patent Application Publication No. WO2021/155123A1 (the '123 application), entitled Systems And Methods For Artifact Reduction In Tomosynthesis With Deep Learning Image Processing, the entirety of which is expressly incorporated herein by reference for all purposes, also provides an alternative to this issue that relies on a tomosynthesis acquisition dataset and processes it with a very specific kind of deep learning based reconstruction to reduce the artefacts, using a standard single energy acquisition.”).
Regarding claim 16, AAPA discloses a. a radiation source; b. a detector positionable to receive radiation emitted from the radiation source and passing through an object positioned between the source and the detector; c. a display for presenting information to a user; d. a processing unit connected to the display and operable to control the operation of the radiation source and detector to rotate the radiation source around the detector and generate a plurality of 2D projection images of the object; and e. a memory operably connected to the processing unit and storing processor-executable code for a reconstruction algorithm that when executed by the processing unit is operable to reconstruct a three-dimensional (3D) volume of the organ from the plurality of 2D projection images; wherein the memory includes processor-executable code for: reconstructing the 3D volume from the plurality of 2D projection images the 3D volume. AAPA doesn’t specifically disclose a pseudo parallel geometry based on a zero angle from the plurality of 2D projection images, wherein the step of reconstructing the 3D volume comprises: 1. reconstructing a 3D virtual object defined in the pseudo parallel geometry based on the zero angle from the plurality of 2D projection images; and 2. correcting the 3D virtual object to form the 3D volume. Nett discloses a pseudo parallel geometry based on a zero angle from the plurality of 2D projection images, wherein the step of reconstructing the 3D volume comprises: 1. reconstructing a 3D virtual object defined in the pseudo parallel geometry based on the zero angle from the plurality of 2D projection images; and 2. correcting the 3D virtual object to form the 3D volume (abstract section II figure 1 “We present a method for utilizing parallel beam reconstruction algorithms in an internally consistent manner. The concept of a virtual image object is utilized. This virtual image object has the property that cone-beam projections through the real object are directly related to parallel-beam projections of the virtual object. The virtual object may then be reconstructed using any algorithm derived for parallel beam projections. Finally, an affine transform may be applied to the virtual image object in order to generate the reconstruction result.”) AAPA and Nett are analogous art because they are from the same field of communications. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to incorporate in the technique disclosed by AAPA the parallel-beam framework via virtual object reconstruction disclosed by Nett. The suggestion/motivation for doing so would have been to reduce artifacts (Nett abstract). See also KSR above.
Regarding claim 17, AAPA and Nett disclose claim 16, AAPA also discloses a network operable to reconstruct and correct the 3D virtual object defined in the pseudo parallel geometry based on the zero angle from the plurality of 2D projection images (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application "Planar tomosynthesis reconstruction in a parallel-beam framework via virtual object reconstruction." Medical Imaging 2007 “To correct the artefacts at locations p1 p2 and p3, a reconstruction algorithm that contains networks (e.g., CNNs) to reduce artefacts must be trained with artefacts of all possible appearance and orientations to be able to accommodate for the angular positions of the artefacts with respect to changes in orientation, and must also be tested against the variability in artefact appearance”).
Regarding claim 18, AAPA and Nett disclose claim 16, AAPA also discloses plurality of 2D projection images is selected from a 2D or 2.5D network (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application "Planar tomosynthesis reconstruction in a parallel-beam framework via virtual object reconstruction." Medical Imaging 2007 “The algorithm can contain CNNs in the projection domain and/or CNNs in the volume domain. In the volume domain, the CNN can process each plane separately (i.e., a 2D CNN), each plane with some context of or from the neighboring planes (i.e., a 2.5D CNN) or use 3D CNN (using local 3D neighborhoods and 3D features).”).
Regarding claim 19, AAPA and Nett disclose claim 16, AAPA also discloses a network that reconstructs and corrects the 3D virtual object along an XZ plane defined by the imaging system (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application " Moreover, while a simple 2D CNN operating in the XZ planes could correct efficiently artefacts close to the chest wall where they are totally contained in these XZ planes, e.g. at point p1, but the 2D trained CNN would not be as efficient at correction of artefacts further from the XZ plane e.g., at point p2.”).
Regarding claim 20, AAPA and Nett disclose claim 16, AAPA also discloses a network that reconstructs and corrects the virtual object along an XY plane defined by the imaging system (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application " For the determination and/or identification of the various views of the object, a coordinate system 2000 is defined by the imaging system 2001, such that the various views of the imaged object can be selected along one or more of the XY plane 2002, XZ plane 2004 or YZ plane 2006, as defined by the coordinate system 2000, e.g., where the XY planes are all planes parallel to the XY plane, (same for XZ, YZ).”).
Regarding claim 21, AAPA and Nett disclose claim 16, AAPA also discloses to reconstruct and correct a 3D virtual object defined in the pseudo parallel geometry based on the zero angle from the plurality of 2D projection images, by reconstructing the 3D virtual object defined in the pseudo parallel geometry based on the zero angle from the plurality of 2D projection images and providing the 3D virtual object as an input to the network (see page 1 paragraph [003] to page 7 paragraph [0015], page 16 paragraph [0053], pages 28-29 paragraphs [0080]-[0081] of the present Application "Planar tomosynthesis reconstruction in a parallel-beam framework via virtual object reconstruction." Medical Imaging 2007 “With reference now to FIGS. 6A-6B, a representation of the operation of the pseudo parallel geometric correction and/or reconstruction to be employed by the AI 152/CNN 154 is illustrated, an operation which is not discussed at length herein but is disclosed in Nett, Brian E., Shuai Leng, and Guang-Hong Chen. “Planar tomosynthesis reconstruction in a parallel-beam framework via virtual object reconstruction.” Medical Imaging 2007: Physics of Medical Imaging. Vol. 6510. SPIE, 2007 (Nett), incorporated by reference herein in its entirety for all purposes”).
Conclusion
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/JUAN A TORRES/Primary Examiner, Art Unit 2634
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