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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/12/2026 has been entered.
Response to Arguments
Applicant’s arguments in Applicant’s responses filed 02/12/2026 with respect to the rejections of claims 1 and 18 under 35 U.S.C. 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Newly found prior art Chen, et al., US 20170221234 A1 systems and methods for generating one or more denoised images from a series of noisy images acquired with a medical imaging system, whereby the series of noisy images are formed into a spatial-temporal or spatial-spectral image matrix in which each column represents a different noisy image, and the image matrix is then processed to decompose the image matrix into basis images defined by a spatial and a temporal or spectral basis, so that low rank solutions are enforced and extracted from the resulting decomposed image matrix as denoised images.
Therefore, the claims stand rejected.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 4, 6-8, 10, 12-19, 21, 23-25, 27, 29, 30-34 are rejected under 35 U.S.C. 103 as being unpatentable over Dharmakumar, et al., US 20140170069 A1 in view of Chen et al., US 20170221234 A1.
Regarding claims 1 and 18, Dharmakumar teaches a method of claim 1 ([0013]) and an apparatus of claim 18 comprising a processor; a memory storing instructions, wherein when the instructions are executed by the processor, cause the apparatus to ([0072] states “imaging the myocardium using MRI” and [0072] indicates utilizing “MATLAB and C++ using the ITK framework or an equivalent software platform” and hence at least suggests a processor with memory for the implementation of the software in the MRI system. NB: the system performs the steps of the method in an identical manner (see Applicant’s response filed 01/10/2025) and hence, in the current office action, the rejection of the limitations of the method claims correspond to the respective steps performed by the system)) comprising:
receiving a continuous blood-oxygen-level-dependent (BOLD) cardiac magnetic resonance (CMR) image series spanning a plurality of cardiac cycles([0075] states “obtain free-breathing cardiac phase resolved 3D myocardial BOLD images”);
cropping the received BOLD CMR image series into a plurality of single-cycle image series, each single-cycle image series spanning a single cardiac cycle ([0075] further states “K-space lines, time stamped for trigger time are collected using cine SSFP acquisition with image acceleration along the long axis. Central k-space lines corresponding to each cardiac phase will be used to derive the center of mass (COM) curves along the z-axis via 1-D fast Fourier transform (FFT). Based on the COM curves, the k-space lines from each cardiac phase will be sorted into 1-30 bins”);
phase matching the plurality of single-cycle image series to generate a plurality of phase- matched single-cycle image series that are temporally aligned at a plurality of phases ([0075] states “obtain free-breathing cardiac phase resolved 3D myocardial BOLD images”. The images are cardiac phase resolved),
wherein the images of each of the phase-matched single-cycle image series at a particular phase form a phase-vector ([0077] states that “The myocardial MR images obtained with repeat CO.sub.2 stimulation blocks will be loaded in MATLAB (or an equivalent image processing platform) and arranged in a four-dimensional (4D) matrix, where the first 3 dimensions represent volume (voxels) and the fourth dimension is time (cardiac phase). Subsequently, each volume is resampled to achieve isotropic voxel size. End-systole (ES) are identified for each stack based on our minimum cross-correlation approach. A 4D non-linear registration algorithm is used to find voxel-to-voxel correspondences (deformation fields) across all cardiac phases. Using the recovered deformation, all cardiac phases are wrapped to the space of ES, such that all phases are aligned to ES.” The phase-vector comprises a phase-matched single cycle image at a particular phase, according to the specification. Here, the end-systole correlates to the particular phase);
reconstructing a plurality of noise-reduced phase-vectors ([0076] discloses that “To minimize the artifacts from under sampling, the data will be processed with a 3D filter, followed by re-gridding the k-space lines, application of a spatial mask (to restrict the registration to region of the heart) and performing FFT to obtain the under sampled 3D image for each respiratory bin. Using the end-expiration image as the reference image, images from all bins (except bin 1) are registered using kits such as Insight Tool Kit (freely available from www.itk.org), or an equivalent software platform, in an iterative fashion and the transform parameters will be estimated for rotation, scaling, shearing, and translation of heart between the different respiratory bins.”);
for each noise-reduced phase-vector, generating a composite phase image based on the images of the noise-reduced phase-vector ([0077] further states that “Upon completion, all cardiac phases from all acquisitions will be spatially aligned to the space of ES of the first acquisition (used as reference) and all phase-to-phase deformations and acquisition-to-acquisition transformations will be known. An expert delineation of the myocardium in the ES of the first (reference) acquisition will then be performed. Based on the estimated deformation fields and transformations, this segmentation is propagated to all phases and acquisitions, resulting in fully registered and segmented myocardial dynamic volumes”); and
constructing a composite single-cycle image series composed of the generated composite phase images of the phase-vectors ([0078] states that “Since BOLD responses are optimally observed in systolic frames, only L systolic cardiac volumes (centered at ES) are retained from each fully registered and segmented 4D BOLD MR image set obtained above. Only those voxels contained in the myocardium are retained and the corresponding RPP (rate-pressure-product) and PaCO.sub.2 are noted. Assuming N acquisitions per CO.sub.2 state (hypocarbic or hypercarbic) and K, CO.sub.2 stimulation blocks, and each cardiac volume consists of n.times.m.times.p (x=multiplication) isotropic voxels, build a concatenated fully registered 4D dataset consisting of n.times.m.times.p.times.t pixels, where x=multiplication and t=L.times.K.times.N, and export this dataset in NIFTI (or an equivalent) format using standard tools.”).
Dharmakumar does not teach for each vector, performing a Windowed Matrix Decomposition (WMD) operation in an overlapping, sliding window manner on the images of the phase-vector to generate, for each window, a low-rank image component that includes salient physiological information in the window and a high-rank image component that includes sparse information; and utilizing the low-rank image components of each phase-vector in the reconstructing a plurality of noise-reduced phase-vectors.
However, within the same field of endeavor, Chen teaches systems and methods for generating one or more denoised images from a series of noisy images acquired with a medical imaging system, whereby the series of noisy images are formed into a spatial-temporal or spatial-spectral image matrix in which each column represents a different noisy image, and the image matrix is then processed to decompose the image matrix into basis images defined by a spatial and a temporal or spectral basis, so that low rank solutions are enforced and extracted from the resulting decomposed image matrix as denoised images (see abstract). [0030] states that in some embodiments, the noisy images can represent a time series of image frames...Such images may be acquired with an x-ray imaging system or with a magnetic resonance imaging (“MRI”) system. Further, [0006], [0021], and [0023] disclose vectorizing the series of noisy images and concatenating the vectorized noisy images to form spatiotemporal image matrix. [0034] then states that “the initial image matrix can be augmented by reformatting one or more prior images of the subject and concatenating the results as one or more columns to the beginning of the initial image matrix. For instance, the one or more prior images of the subject may be an average of several time frames (e.g., over an entire time series or over various time windows using a sliding window approach)”. [0036] states “a decomposed image matrix is then generated by performing a singular value decomposition (“SVD”) on the initial image matrix, as indicated at step 108”. [0020] then states that “By enforcing low rank solutions in this decomposed matrix, denoised images can be generated by separating the anatomical and physiological information from noise. As one example, the low rank solutions can be enforced using a singular value decomposition of the image matrix and retaining only those Kronecker product of matrix columns associated with singular values above a threshold value. As another example, the low rank solutions can be enforced using a rank-regularized optimization technique”.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Dharmakumar wherein for each vector, performing a Windowed Matrix Decomposition (WMD) operation in an overlapping, sliding window manner on the images of the phase-vector to generate, for each window, a low-rank image component that includes salient physiological information in the window and a high-rank image component that includes sparse information; and utilizing the low-rank image components of each phase-vector in the reconstructing a plurality of noise-reduced phase-vectors, as taught by Chen, to provide systems and methods for denoising medical images that reduce the noise in the images without decreasing the spatial resolution of the images ([0004]).
Regarding claims 2 and 19, Dharmakumar in view of Chen teaches all the limitations of claims 1 and 18.
Dharmakumar fails to teach wherein the WMD operation is a Windowed Singular Value Decomposition (WSVD) operation.
However, Chen further teaches wherein the WMD operation is a Windowed Singular Value Decomposition (WSVD) operation (see [0036] which states that A decomposed image matrix is then generated by performing a singular value decomposition (“SVD”) on the initial image matrix, as indicated at step 108).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Dharmakumar wherein the WMD operation is a Windowed Singular Value Decomposition (WSVD) operation, as taught by Chen, to provide systems and methods for denoising medical images that reduce the noise in the images without decreasing the spatial resolution of the images ([0004]).
Regarding claims 4 and 21, Dharmakumar in view of Chen teaches all the limitations of claims 1 and 18, respectively.
Modified Dharmakumar further teaches wherein cropping the received BOLD CMR image series comprises cropping the BOLD CMR image series utilizing an image-based technique to determine the plurality of cardiac cycles ([0077] states that “End-systole (ES) are identified for each stack based on our minimum cross-correlation approach. A 4D non-linear registration algorithm is used to find voxel-to-voxel correspondences (deformation fields) across all cardiac phases. Using the recovered deformation, all cardiac phases are wrapped to the space of ES, such that all phases are aligned to ES. Recover the transformations across all ES images from repeat CO.sub.2 blocks and bring them to the same space using a diffeomorphic volume registration tool, such as ANTs. Upon completion, all cardiac phases from all acquisitions will be spatially aligned to the space of ES of the first acquisition (used as reference) and all phase-to-phase deformations and acquisition-to-acquisition transformations will be known”).
Regarding claims 7 and 24, Dharmakumar in view of Chen teaches all the limitations of claim 6 and 23, respectively.
Dharmakumar does not teach wherein the MD operation is a singular value decomposition (SVD) operation.
However, Chen further teaches wherein the MD operation is a singular value decomposition (SVD) operation (see [0036] which states that A decomposed image matrix is then generated by performing a singular value decomposition (“SVD”) on the initial image matrix, as indicated at step 108).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Dharmakumar wherein the WMD operation is a Windowed Singular Value Decomposition (WSVD) operation, as taught by Chen, to provide systems and methods for denoising medical images that reduce the noise in the images without decreasing the spatial resolution of the images ([0004]).
Regarding claims 8 and 25, Dharmakumar in view of Chen teaches all the limitations of claims 1 and 18.
Modified Dharmakumar further teaches for each phase-vector, spatially aligning the images of the phase-vector prior to performing the WMD operation ([0077] of Dharmakumar states “Upon completion, all cardiac phases from all acquisitions will be spatially aligned to the space of ES of the first acquisition (used as reference) and all phase-to-phase deformations and acquisition-to-acquisition transformations will be known”. This occurs before the delineation step in [0077] and hence would occur before the application of the WMD).
Regarding claims 12 and 29, Dharmakumar in view of Chen teaches all the limitations of claims 1 and 18.
Dharmakumar further teaches wherein computing one or more oxygen perfusion biomarkers utilizing the composite image series comprises segmenting each phase of the composite image series to isolate myocardial tissue to generate a segmented image series, and computing the one or more oxygen perfusion biomarkers utilizing the segmented image series ([0077] discloses image segmentation of the images to obtain myocardial dynamic volume to assess organ perfusion ([0082]).
Regarding claims 13 and 30, Dharmakumar in view of Chen teaches all the limitations of claims 12 and 29, respectively.
Dharmakumar further teaches wherein the segmenting is performed by a machine learning system trained to isolate myocardial tissue or by manual myocardial tissue delimitation ([0077] states that “An expert delineation of the myocardium in the ES of the first (reference) acquisition will then be performed”).
Regarding claims 14 and 31, Dharmakumar in view of Chen teaches all the limitations of claims 1 and 31, respectively.
Dharmakumar further teaches computing one or more oxygen perfusion biomarkers utilizing the composite image series, wherein the one or more oxygen perfusion biomarkers include one or more of:" total signal intensity over time;" oxygenation;" deoxygenation;" ratio of oxygenation to deoxygenation;" differential of oxygenation to deoxygenation;" oxygenation kinetics;" deoxygenation kinetics;" ratio of oxygenation kinetics to deoxygenation kinetics;" differential of oxygenation kinetics to deoxygenation kinetics;" signal intensity ratio of End-Diastolic (ED) to End-Systolic (ES) phases;" differential of ED and ES;" oxygen total variance;" vascular function change; or " vascular function change related to respiration ([0107] states that “In detail, the color images (lower panel of FIG. 1) are color-coded to the signal intensities of grayscale images (above). The darker colors (blue/black) represent territories of baseline myocardial oxygenation and the brighter regions represent those regions that are hyperemic”).
Regarding claims 15-16, and 32-33, Dharmakumar in view of Chen teaches all the limitations of claims 1 and 18, respectively.
Dharmakumar further teaches wherein receiving the continuous BOLD CMR image series comprises receiving continuous BOLD CMR image series that are obtained utilizing at least two different breathing paradigms; and wherein the at least two different breathing paradigms are at least two of normal breathing([0074]), hyperventilation ([0068]), or breath hold.
Regarding claims 17 and 34, Dharmakumar in view of Chen teaches all the limitations of claim 1 and 18, respectively.
Dharmakumar further teaches computing at least one functional biomarker utilizing the composite image series, the at least one functional biomarker being at least one of:" radial strain;" circumferential strain;" ejection fraction; or " systolic wall thickening ([0103] states that “the improved BOLD MRI technique described above provides a non-invasive and reliable determination of ischemic volume (no radiation, contrast-media, or adenosine) and other value-added imaging biomarkers from the same acquisition (Ejection Fraction, Wall Thickening)”).
Claims 6, 10, 23, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Dharmakumar, et al., US 20140170069 A1 in view of Chen et al., US 20170221234 A1, as applied to claims 1 and 18, respectively, and further in view of Liu, et al., US 20130289912 A1,
Regarding claims 6 and 23, Dharmakumar in view of Chen teaches all the limitations of claims 1 and 18, respectively.
Dharmakumar in view of Chen fails to teach for each phase-vector, performing a Matrix Decomposition (MD) operation on low-rank image components generated by the WMD operation to generate, for each low-rank image component, a plurality of ranked eigen modes; and wherein reconstructing plurality of noise-reduced phase-vectors comprises reconstructing the images of the noise-reduced phase-vector utilizing a predetermined number of lowest-rank modes of the generated ranked eigen modes.
However, within the same field of endeavor, Liu teaches a method for estimating a coil sensitivity map for a magnetic resonance (MR) image including providing a matrix A of sliding blocks of a 2D image of coil calibration data, calculating (62) a left singular matrix V‖ from a singular value decomposition of A corresponding to tau leading singular values (see abstract) and for each phase-vector, performing a Matrix Decomposition (MD) operation on low-rank image components generated by the WMD operation to generate, for each low-rank image component, a plurality of ranked eigen modes ([0083] discloses “A low rank subspace of A can be computer by singular value decomposition A=V.SIGMA.U.sup.H, and setting V.sub..parallel. to the left singular vectors of A corresponding to the leading .tau. singular values. Let S.sup.c be the coil sensitivity at coil c.epsilon.[1, 2, . . . , c], which are computed following an eigenvector approach according to an embodiment of the invention as discussed above, i.e., set to the eigenvectors corresponding to the largest eigenvalues”); and wherein reconstructing plurality of noise-reduced phase-vectors comprises reconstructing the images of the noise-reduced phase-vector utilizing a predetermined number of lowest-rank modes of the generated ranked eigen modes ([0070] states that “The optimal value of .tau. may be related to the noise in the calibration data (note that, for the simulated phantom, no noise is added). Experiments on dozens of data sets show that setting .tau. as the smallest value such that SIGMA..sub.k=1.sup..tau..sigma..sub.k/.SIGMA..sub.k=1.sup.288.sigma..sub- .k.gtoreq.0.95 works well, where .sigma..sub.k is the k-th largest singular vector of A in EQ. (7)”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Dharmakumar, as modified by Chen, for each phase-vector, performing a Matrix Decomposition (MD) operation on low-rank image components generated by the WMD operation to generate, for each low-rank image component, a plurality of ranked eigen modes; and wherein reconstructing plurality of noise-reduced phase-vectors comprises reconstructing the images of the noise-reduced phase-vector utilizing a predetermined number of lowest-rank modes of the generated ranked eigen modes, as taught by Liu, to produce images with substantially less artifacts ([0084]-[0085]).
Regarding claims 10 and 27, Dharmakumar in view of Chen teaches all the limitations of claims 1 and 18, respectively.
Dharmakumar in view of Chen does no teach wherein constructing a composite phase image utilizing the images of the noise-reduced phase-vector comprises performing, on the images of the noise-reduced phase-vectors, one of: a two-dimensional (2D) median operation; a 2D mean operation; a principle component analysis operation; a spectral-based operation; or a machine learning based operation.
However, Liu further teaches wherein constructing a composite phase image utilizing the images of the noise-reduced phase-vector comprises performing, on the images of the noise-reduced phase-vectors, one of: a two-dimensional (2D) median operation; a 2D mean operation; a principle component analysis operation; a spectral-based operation; or a machine learning based operation ([0069]-[0070] describe performing a mean absolute correlation (mac) using different numbers of kept singular vectors).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Dharmakumar, as modified by Chen wherein constructing a composite phase image utilizing the images of the noise-reduced phase-vector comprises performing, on the images of the noise-reduced phase-vectors, one of: a two-dimensional (2D) median operation; a 2D mean operation; a principle component analysis operation; a spectral-based operation; or a machine learning based operation, as taught by Liu, to produce images with substantially less artifacts ([0084]-[0085]).
Claims 3, 5, 20, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Dharmakumar, et al., US 20140170069 A1 in view of Chen et al., US 20170221234 A1, as applied to claims 1 and 18, respectively, and further in view of Lyu, et al., US 20210201490 A1.
Regarding claims 3 and 20, Dharmakumar in view of Chen teaches all the limitations of claims 1 and 18, respectively.
Dharmakumar in view of Chen fails to teach wherein the BOLD CMR image series comprise a Digital Imaging and Communications in Medicine (DICOM) containing timing tags, and cropping the received BOLD CMR image series comprises utilizing the timing tags to crop the BOLD CMR image series into the plurality of single-cycle image series.
However, Lyu teaches a magnetic resonance system ([0034]) for acquiring a plurality of successive images, e.g., cardiac cine images [0081], of a region of interest (ROI) of a patient's heart ([0036]), wherein the BOLD CMR image series comprise a Digital Imaging and Communications in Medicine (DICOM) containing timing tags [0107], and cropping the received BOLD CMR image series comprises utilizing the timing tags to crop the BOLD CMR image series into the plurality of single-cycle image series [0131].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Dharmakumar, as modified by Chen, wherein the BOLD CMR image series comprise a Digital Imaging and Communications in Medicine (DICOM) containing timing tags, and cropping the received BOLD CMR image series comprises utilizing the timing tags to crop the BOLD CMR image series into the plurality of single-cycle image series, as taught by Lyu, to provide a more accurate and less time consuming approach to determining cardiac information ([0002]).
Regarding claims 5 and 22, Dharmakumar in view of Chen teaches all the limitations of claims 1 and 21.
Dharmakumar states in [0104] that “At each CO.sub.2 level, free-breathing and cardiac gated blood-oxygen-level-dependent (BOLD) acquisitions were prescribed at mid diastole and Doppler flow velocities were measured” but Dharmakumar in view of Chen does not teach wherein the image-based technique comprises: identifying diastole images of the BOLD CMR image series by comparing a relative size of a left ventricle in the images of the BOLD CMR image series and sequential image similarity metrics; and determining each single-cycle image series as the images of the BOLD CMR image series between two sequential identified diastole images.
However, Lyu further teaches wherein the image-based technique comprises: identifying diastole images of the BOLD CMR image series by comparing a relative size of a left ventricle in the images of the BOLD CMR image series and sequential image similarity metrics; and determining each single-cycle image series as the images of the BOLD CMR image series between two sequential identified diastole images ([0131] states that “The processing device 140 may divide the determined diastolic images into two or more second groups based on the similarity of pixel values of pixels in the determined diastolic images and/or the serial numbers of the determined diastolic images. In some embodiments, the processing device 140 may divide the determined diastolic images into the two or more second groups using K-means clustering. In some embodiments, the images in the second group may be deemed as reflecting the cardiac diastole. For at least two of the two or more second groups, the processing device 140 may determine the images at the same location (e.g., the first location, the last location, or the middle location, etc.) in the at least two of the two or more second groups as the two or more terminal images”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Dharmakumar, as modified by Chen, wherein the image-based technique comprises: identifying diastole images of the BOLD CMR image series by comparing a relative size of a left ventricle in the images of the BOLD CMR image series and sequential image similarity metrics; and determining each single-cycle image series as the images of the BOLD CMR image series between two sequential identified diastole images, as taught by Lyu, to provide a more accurate and less time consuming approach to determining cardiac information ([0002]).
Claims 9 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Dharmakumar, et al., US 20140170069 A1 in view of Chen et al., US 20170221234 A1, as applied to claims 1 and 18, respectively, and further in view of Dharmakumar, et al., US 20220117508 A1, hereinafter referred to as “Yang”.
Regarding claims 9 and 26, Dharmakumar in view of Chen teaches all the limitations of claims 8 and 25, respectively.
Dharmakumar in view of Chen does not teach wherein spatially aligning the images of the phase-vector is performed utilizing a non-rigid registration operation.
However, Yang teaches cardiac BOLD fMRI systems and methods ([0018] and [0023]), wherein spatially aligning the images of the phase-vector is performed utilizing a non-rigid registration operation (“All BOLD CMR images were analyzed with custom scripts written in Matlab. For 2D image data sets, BOLD CMR images (T2 maps) were first registered to the initial normocapnic image using a non-rigid registration tool from an open source image registration toolbox (ANTs)” [0219]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Dharmakumar, as modified by Chen, wherein spatially aligning the images of the phase-vector is performed utilizing a non-rigid registration operation, as taught by Yang, providing signals with stronger sensitivity for more accurate diagnostic images [0016].
Claims 11 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Dharmakumar, et al., US 20140170069 A1 in view of Chen et al., US 20170221234 A1, as applied to claims 1 and 18, respectively, and further in view of Hayes, C., US 20090214090 A1.
Regarding claims 11 and 28, Dharmakumar in view of Chen teaches all the limitations of claims 1 and 28.
Dharmakumar in view of Chen does not teach wherein phase matching the plurality of single-cycle image series to generate the plurality of phase-matched single-cycle image series comprises generating phase-matched matrix in which the images of a given phase-matched single cycle image series are included in a respective row of the phase-matched matrix, and the columns correspond to respective phases such that the images in a given column are the phase-vector for the phase that corresponds to that column.
However, Hayes teaches processing medical image data which image a structure layer by layer, the image data for at least some layers respectively including a plurality of layer images (abstract), the structure being a human heart ([0060]) wherein phase matching the plurality of single-cycle image series to generate the plurality of phase-matched single-cycle image series comprises generating phase-matched matrix in which the images of a given phase-matched single cycle image series are included in a respective row of the phase-matched matrix, and the columns correspond to respective phases such that the images in a given are the phase-vector for the phase that corresponds to that column (see fig. 7 and [0112]-[0113], with [0113] stating that “Each of the columns shown in FIG. 7 therefore contains layer images which have been recorded from the same phase of the cardiac cycle. For subsequent compilation of a layer image set, it is expedient to select only layer images with the same time stamp since the heart has a different volume in different phases and meaningful volume calculation would not therefore be possible with mixed time stamps.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Dharmakumar, as modified by Chen, wherein phase matching the plurality of single-cycle image series to generate the plurality of phase-matched single-cycle image series comprises generating phase-matched matrix in which the images of a given phase-matched single cycle image series are included in a respective row of the phase-matched matrix, and the columns correspond to respective phases such that the images in a given are the phase-vector for the phase that corresponds to that column, as taught by Hayes, to provide a more accurate imaging method for examining a heart ([0003][0008]).
Conclusion
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/FAROUK A BRUCE/ Examiner, Art Unit 3797
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