DETAILED ACTION
Claims 1-20 are pending.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on September 4, 2024 has been considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
p. 20, [0066] states, “DWT and/or IWN can be configured…” (emphasis added). This appears to be a typographical error and should be “IWT”.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 7-11, 13-18, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Schlemper et al. (Journal publication titled "A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction") ("Schlemper").
Regarding claim 1, Schelmper discloses a deep neural network-based reconstruction system for accelerated magnetic resonance imaging (p. 491, Abstract; p. 492, Section I paragraph 5) of vessel walls comprising:
a first and second subnetwork implemented in a cascade fashion (p. 492, section I paragraph 5; p. 494, section IV, paragraph 2, wherein each CNN is a subnetwork within a cascading network),
wherein the first subnetwork translates a zero-filling reconstructed image to a reduced artifact image (p. 492, section II paragraph 5; p. 495, section VI, paragraph 1, wherein the CNN (i.e. first subnetwork) takes in the zero-filled reconstruction and produces a reconstruction (i.e. translation) that solves the de-aliasing (i.e. reduced artifact image)), and
wherein the second subnetwork boosts an accuracy of the reduced artifact image (p. 494, section IV paragraphs 1-2, wherein the cascading network performs intermediate de-aliasing (i.e. reduced artifact); Fig. 7-8, p. 497-498, section VII-B2 paragraphs 2-3, wherein the cascading network gradually recovers and sharpens the output image (less noise-like aliasing remaining) with each iteration/cascade depth, while reducing reconstruction errors (i.e. boosting accuracy)).
Regarding claim 2, Schlemper discloses the system of claim 1, wherein the first subnetwork comprises:
a convolutional neural network (CNN) (Fig. 4; p. 494, section IV paragraph 2, states “first CNN”); and
an output correcting module (Fig. 4; p. 493, section III paragraphs 1-3, wherein a data consistency (DC) layer corrects the CNN output. This DC layer serves as the output correcting module, and the combination of CNN module and DC layer are included within the first subnetwork).
Regarding claim 7, Schlemper discloses the system of claim 2, wherein the output correcting module is after the CNN and configured to enforce data fidelity (Fig. 4; p. 493, section III paragraphs 1-3, wherein a CNN module is followed by a data consistency (DC) layer (i.e. output correcting module) and the DC layer enforces data fidelity by correcting the CNN output in the k-space using originally-acquired measurements).
Regarding claim 8, Schlemper discloses the system of claim 7, wherein the output correcting module receives predictions from the CNN as inputs and Fourier transforms the predictions to yield k-space information (p. 496, section III paragraphs 1 and 4, wherein the predicted output (i.e. prediction) of the CNN is used by the DC layer (i.e. output correcting module) and applies the Fourier transform F to convert the output to k-space).
Regarding claim 9, Schlemper discloses the system of claim 8, wherein the output correcting module back-transforms the k-space information to an image domain (p. 496, section III paragraphs 1-2 and 4, wherein the DC layer (i.e. output correcting module) applies the inverse Fourier transform FH to the k-space information to an image domain).
Regarding claim 10, Schelmper discloses the system of claim 9, wherein the back-transformed k-space signals in the image domain are provided to the second subnetwork (Fig. 4; pg. 494, section IV paragraph 2, wherein xrec, the output of the DC layer resulting from the inverse Fourier transform FH (i.e. back-transformed k-space signals in the image domain) are the inputs to the next CNN (i.e. second subnetwork)).
Regarding claim 11, Schlemper discloses the system of claim 10, wherein the second subnetwork is an identical duplicate of the first subnetwork (Fig. 4; p. 495, section VI paragraph 1, wherein each subnetwork comprises the same CNN module architecture defined by the same parameters nd (number of convolutional layers) and nf (number of filters), repeated a number of nc times throughout the cascade).
Regarding claim 13, Schlemper discloses a method (p. 498, section VII-A, paragraph 1) comprising:
receiving a magnetic resonance imaging (MRI) scan (p. 498, section VII-A, paragraph 1; p. 492, section II, paragraph 1, wherein MR images/scans are acquired);
feeding the MRI scan into a predictive algorithm (p. 492, section II paragraph 1; p. 492, section II paragraph 5, wherein a CNN reconstructs the under-sampled MR images (i.e. MRI scans); p. 493, section III paragraph 3; p. 495, section V paragraph 3, wherein the CNN corresponds to a predictive algorithm); and
outputting an improved MRI scan from the predictive algorithm (Fig. 12; p. 498, section VII-B2 paragraphs 2-3, wherein the cascading network gradually recovers and sharpens the output image (i.e. MRI scan) (less noise-like aliasing remaining) with each iteration/cascade depth, while reducing reconstruction errors (i.e. improved MRI scan)).
Regarding claim 14, Schlemper discloses the method of claim 13, wherein:
the predictive algorithm comprises a neural network having a first and second subnetwork implemented in a cascading fashion (p. 492, section I paragraph 5; p. 494, section IV paragraph 2, wherein each CNN (i.e. predictive algorithm) is a subnetwork within a cascading network);
the first subnetwork removes artifacts from the MRI scan (p. 492, section II paragraph 5; p. 495, section VI paragraph 1, wherein the CNN (i.e. first subnetwork) takes in the zero-filled reconstruction of the MR images (i.e. MRI scan) and produces a reconstruction that solves the de-aliasing (i.e. removes artifacts)); and
the second subnetwork boosts an accuracy of the first subnetwork (p. 494, section IV paragraphs 1-2, wherein the cascading network performs intermediate de-aliasing (i.e. reduced artifact); Fig. 7-8, p. 497-498, section VII-B2 paragraphs 2-3, wherein the cascading network gradually recovers and sharpens the output image (less noise-like aliasing remaining) with each iteration/cascade depth, while reducing reconstruction errors (i.e. boosting accuracy)).
Regarding claim 15, Schlemper discloses the method of claim 13, wherein:
the MRI scan comprises training data (p. 493, section II paragraph 5 and eq. (5); p. 496, section VII-A4 paragraph 1, wherein training data is included in the dataset (i.e. MRI scan));
the method further comprises:
training the predictive algorithm on the training data (p. 493, section II, paragraph 5, wherein the CNN (i.e. predictive algorithm) is trained using the training data),
wherein the predictive algorithm is more accurate after the training than before the training (Fig. 6, 7, and 10; p. 497, section VII-B1 paragraph 2, section VII-B2 paragraph 2; p. 499, V11-C1 paragraph 1, wherein the CNN (i.e. predictive algorithm) training reconstruction errors are lower (i.e. more accurate) than the test reconstruction errors); and
the outputting the improved MRI scan comprises:
outputting the improved MRI scan from the trained predictive algorithm (Fig. 4, 8, and 12; p. 498, section VII-B2 paragraph 2; p. 502, section VIII paragraph 1, wherein the more accurate reconstruction (i.e. improved MRI scan) is output from the trained CNN (i.e. trained predictive algorithm).
Regarding claim 16, Schlemper discloses the method of claim 13, wherein the predictive algorithm comprises a convolutional neural network (CNN) (p. 492, section II paragraph 5; p. 495, section VI, paragraph 1, p. 493, section III paragraph 3; p. 495, section V paragraph 3; , wherein the CNN corresponds to a predictive algorithm that performs de-aliasing).
Regarding claim 17, Schlemper discloses the method of claim 13 further comprising:
correcting an output of the predictive algorithm (Fig. 4; p. 493, section III paragraphs 1-3, wherein a DC layer corrects the CNN output (i.e. output of the predictive algorithm)).
Regarding claim 18, Schlemper discloses the method of claim 17, wherein the correcting the output comprises:
correcting the output of the predictive algorithm by applying a Fourier transform to the output of the predictive algorithm (p. 496, section III paragraphs 1-2 and 4, wherein the output of the CNN (i.e. output of the predictive algorithm) is corrected by the DC layer by applying the Fourier transform F to convert the output to k-space and then applying the inverse Fourier transform FH to the k-space information to an image domain).
Regarding claim 20, Schlemper discloses the method of claim 13, wherein the improved MRI scan has fewer artifacts and greater resolution than the MRI scan (p. 494, section IV paragraphs 1-2, wherein the cascading network performs intermediate de-aliasing (i.e. fewer artifacts); Fig. 7-8, p. 497-498, section VII-B2 paragraphs 2-3, wherein the cascading network gradually recovers and sharpens (i.e. greater resolution) the output image (less noise-like aliasing remaining) with each iteration/cascade depth, while reducing reconstruction errors (i.e. boosting accuracy)).
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.
Claims 3 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Schlemper et al. (Journal publication titled "A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction") ("Schlemper") in view of Ramanarayanan et al. (Journal publication titled "DC-WCNN: A Deep Cascade of Wavelet Based Convolutional Neural Networks for MR Image Reconstruction") ("Ramanarayanan").
Regarding claim 3, Schlemper discloses the system of claim 2, wherein the CNN comprises a discrete Fourier transform (DFT) and inverse DFT (IDFT) are used to transform data between domains, where DFT is applied for encoding into the k-space through undersampling (i.e. downsampling) and IDFT reconstructs (i.e. upsamples) the sequence back to the image domain (p. 492, section II paragraph 2).
However, Schlemper fails to teach using a discrete wavelet transform (DWT) configured for downsampling and an inverse wavelet transform (IWT) configured for upsampling, specifically. Ramanarayanan, on the other hand, teaches using a DWT for downsampling and IWT for upsampling within a CNN for MR image reconstruction. More specifically and as it relates to the applicant’s claims, Ramanarayanan discloses the CNN comprising a discrete wavelet transform configured for downsampling and an inverse wavelet transform configured for upsampling (p. 1070, section 1 paragraph 4; p. 1072, section 4 paragraph 1, wherein DWT is used for for downsampling and IWT for upsampling in a CNN; Section 2.1 paragraph 6 for equations 5 and 6 of DWT and IWT, respectively).
Ramanarayanan is combinable with Schlemper because they are from the same art of image processing.
The suggestion/motivation for doing so would have been to circumvent information loss to efficiently represent structural and textural details (Ramanarayanan, p. 1070, section 1 paragraph 4).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate a DWT configured for downsampling and an IWT configured for upsampling, as taught by Ramanarayanan, into the the system, as taught by Schlemper, to obtain the invention as specified in claim 3.
Regarding claim 19, Schlemper discloses the system of claim 13. Schlemper teaches the addition of a pooling layer (p. 495, section VI paragraph 1).
However, Schlemper fails to teach wherein a pooling layer in the predictive algorithm is replaced with an inverse wavelet transform layer. Ramanarayanan, on the other hand, teaches replacing pooling and unpooling layers with DWT and IWT. More specifically and as it relates to the applicant’s claims, Ramanarayanan discloses wherein a pooling layer in the predictive algorithm is replaced with an inverse wavelet transform layer (p. 1070, section 1 paragraph 4; p. 1072, section 4 paragraph 1, wherein IWT is used in place of unpooling (i.e. pooling) in a CNN (i.e. predictive algorithm). “Unpooling” corresponds to pooling because IWT is used for upsampling/reconstructing/deconvoluting the MRI scan into the original image from the sub-bands created from undersampling/downsampling/pooling/convoluting the MRI scan within a cascading network).
Ramanarayanan is combinable with Schlemper because they are from the same art of image processing.
The suggestion/motivation for doing so would have been to circumvent information loss to efficiently represent structural and textural details (Ramanarayanan, p. 1070, section 1 paragraph 4).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate wherein a pooling layer in the predictive algorithm is replaced with an inverse wavelet transform layer, as taught by Ramanarayanan, into the method, as taught by Schlemper, to obtain the invention as specified in claim 19.
Claims 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Schlemper et al. (Journal publication titled "A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction") ("Schlemper") in view of Liu et al. (Journal publication titled " Multi-Level Wavelet Convolutional Neural Networks") (“Liu”).
Regarding claim 4, Schlemper discloses the system of claim 2. Schlemper additionally teaches wherein the CNN concatenates images derived from a magnetic resonance imaging scan into a convolutional block (p. 494, section V paragraph 3).
However, Schlemper fails to specifically teach the CNN concatenating four subband images of a magnetic resonance imaging scan into a convolutional block. Liu, on the other hand, teaches concatenating four subband images into a convolutional block. More specifically and as it relates to the applicant’s claims, Schlemper and Liu disclose wherein the CNN concatenates four subband images (Liu, Fig. 4C; p. 74975, section III-A paragraph 1, wherein xLL, xHL, xHH, and xLH subbands are created (i.e. four subband images) via DWT in a CNN; Fig. 4C, caption, wherein the four subbands are concatenated as input of CNN blocks) of a magnetic resonance imaging scan into a convolutional block (Schlemper, p. 494, section V paragraph 3, wherein the CNN concatenates images of a magnetic resonance imaging scan into a convolutional block).
Liu is combinable with Schlemper because they are from the same art of image processing.
The suggestion/motivation for doing so would have been to enlarge receptive field with better tradeoff between efficiency and restoration performance while preserving details (Liu, p. 74974, section I paragraph 4).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the concatenation of four subband images, as taught by Liu, into the concatenation of magnetic resonance imaging scan images into a convolutional block in the system, as taught by Schlemper, to obtain the invention as specified in claim 4.
Regarding claim 5, Schlemper and Liu disclose the system of claim 4.
However, Schlemper fails to teach wherein the concatenating is without any information loss. Liu, on the other hand, teaches a concatenation of the four subbands without information loss. More specifically and as it relates to the applicant’s claims, Liu discloses wherein the concatenating is without any information loss (p. 74976, section III-A paragraph 3, eq (3) demonstrates that all four subbands collectively contain all information necessary to perfectly reconstruct the original input; p. 74978, section III-C1 paragraph 1, wherein DWT uses all subbands with four fixed orthometric weights to avoid information loss; p. 74983, section V paragraph 1, wherein both low- and high-frequency subbands are taken as input and can be downsampled without information loss. These factors allow there to be no information loss when concatenating all four subbands together).
Liu is combinable with Schlemper because they are from the same art of image processing.
The suggestion/motivation for doing so would have been to be more effective and efficient in recovering details when it comes to image denoising (Liu, p. 74983, section V paragraph 1).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the concatenating without any information loss, as taught by Liu, into the system as taught by Schlemper and Liu, to obtain the invention as specified in claim 5.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Schlemper et al. (Journal publication titled "A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction") ("Schlemper") in view of Liu et al. (Journal publication titled " Multi-Level Wavelet Convolutional Neural Networks") (“Liu”) and further in view of Zhao et al. (Publication titled “A new approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms”) (“Zhao”).
Regarding claim 6, Schlemper and Liu disclose the system of claim 5.
However, Schlemper and Liu fail to teach wherein the CNN further comprises an iterative multi-scale refinement (iMR) block for refining coarse features with fine-grained features at different scales to achieve more accurate wall delineation with sharpened boundaries. Zhao, on the other hand, teaches iterating through multi-scale blocks to refine vessel boundaries for more accurate wall delineation. More specifically and as it relates to the applicant’s claims, Zhao discloses wherein the CNN further comprises an iterative (Fig. 3; 7, section 2D paragraph 7, wherein the refinement is done iteratively from coarse-grained to fine-grained) multi-scale refinement (iMR) block (Fig. 4; p. 6, section 2C paragraph 6, wherein the multi-scale block architecture is incorporated in the CNN) for refining coarse features (Abstract, Methods; p. 6, section 2D paragraph 1, wherein the first stage extracts coarse features) with fine-grained features at different scales to achieve more accurate wall delineation with sharpened boundaries (p. 6 section 2D paragraph 2; p. 14, section 4A paragraph 1, wherein the vessel boundary (i.e. walls) are refined for more accurate/precise (i.e. sharpened) wall delineation).
Zhao is combinable with Schlemper and Liu because they are from the same art of image processing.
The suggestion/motivation for doing so would have been to more accurately extract the features of thinner vessels that vary in different sections of images (Zhao, p. 5-6, section 2C paragraph 6).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate wherein the CNN further comprises an iterative multi-scale refinement (iMR) block for refining coarse features with fine-grained features at different scales to achieve more accurate wall delineation with sharpened boundaries, as taught by Zhao, into the system, as taught by Schlemper and Liu to obtain the invention as specified in claim 6.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Sutton et al. (U.S. Publication No. US 2019/0213779 A1) (“Sutton”) in view of Schlemper et al. (Journal publication titled "A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction") ("Schlemper").
Sutton discloses a magnetic resonance imaging (MRI) scanner (paragraph [0031] and [0033]) configured to obtain an image of the vessel walls (paragraphs [0021] and [0073], wherein cardiac MR images, including vessel walls, are obtained), a computer having a processor (Fig. 11, element 1102; paragraph [0099]), and a computer display terminal connected to the processor (Fig. 11, element 1102 and 1110; paragraph [0099], where the elements are connected via bus) and configured to display the visual representation of the vessel walls (paragraphs [0002] and [0073], wherein the 3D models (i.e. visual representation) include vessel walls).
Although Sutton teaches generating a visual representation of the vessel walls (paragraph [0073], wherein the 3D model of the vessel walls corresponds generating a visual representation of the vessel walls), Sutton does not teach a deep neural network-based reconstruction system for accelerated magnetic resonance imaging of vessel walls comprising: a processor comprising: a first and second subnetwork implemented in a cascade fashion, wherein the first subnetwork receives the image and transforms the image to a reduced artifact image and wherein the second subnetwork boosts an accuracy of the reduced artifact image to generate a visual representation of the vessel walls, wherein the first subnetwork comprises: a convolutional neural network (CNN); and an output correcting module, wherein the second subnetwork is an identical duplicate of the first subnetwork.
Schlemper on the other hand, discloses a deep neural network-based reconstruction system for accelerated magnetic resonance imaging (p. 491, Abstract; p. 492, Section I paragraph 5) of vessel walls comprising:
a computer having a processor, the processor comprising:
a first and second subnetwork implemented in a cascade fashion (p. 492, section I paragraph 5; p. 494, section IV, paragraph 2, wherein each CNN is a subnetwork within a cascading network), wherein the first subnetwork receives the image and transforms the image to a reduced artifact image (p. 492, section II paragraph 5; p. 495, section VI, paragraph 1, wherein the CNN (i.e. first subnetwork) takes in the zero-filled reconstruction of the MR image and produces a reconstruction (i.e. translation) that solves the de-aliasing (i.e. reduced artifact image)) and wherein the second subnetwork boosts an accuracy of the reduced artifact image to generate a visual representation (p. 494, section IV paragraphs 1-2, wherein the cascading network performs intermediate de-aliasing (i.e. reduced artifact); Fig. 7-8, p. 497-498, section VII-B2 paragraphs 2-3, wherein the cascading network gradually recovers and sharpens the output image (less noise-like aliasing remaining) with each iteration/cascade depth, while reducing reconstruction errors (i.e. boosting accuracy)) of the vessel walls,
wherein the first subnetwork comprises:
a convolutional neural network (CNN) (Fig. 4; p. 494, section IV paragraph 2, states “first CNN”); and
an output correcting module (Fig. 4; p. 493, section III paragraphs 1-3, wherein a data consistency (DC) layer corrects the CNN output. This DC layer serves as the output correcting module, and the combination of CNN module and DC layer are included within the first subnetwork),
wherein the second subnetwork is an identical duplicate of the first subnetwork (Fig. 4; p. 495, section VI paragraph 1, wherein each subnetwork comprises the same CNN module architecture defined by the same parameters nd (number of convolutional layers) and nf (number of filters), repeated a number of nc times throughout the cascade).
Schlemper is combinable with Sutton because they are from the same art of image processing.
The suggestion/motivation for doing so would have been to perform reconstruction more efficiently while preserving anatomical structure/details more accurately (Schlemper, Abstract; p. 502, section VIII paragraph 1).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate a deep neural network-based reconstruction system for accelerated magnetic resonance imaging of vessel walls comprising: a processor comprising: a first and second subnetwork implemented in a cascade fashion, wherein the first subnetwork receives the image and transforms the image to a reduced artifact image and wherein the second subnetwork boosts an accuracy of the reduced artifact image to generate a visual representation of the vessel walls, wherein the first subnetwork comprises: a convolutional neural network (CNN); and an output correcting module, wherein the second subnetwork is an identical duplicate of the first subnetwork, as taught by Schlemper, into the structural components of a magnetic resonance imaging (MRI) scanner configured to obtain an image of the vessel walls, a computer having a processor, and a computer display terminal connected to the processor and configured to display the visual representation of the vessel walls, as taught by Sutton, to obtain the invention as specified in claim 12.
Conclusion
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/RACHEL Y DANG/Examiner, Art Unit 2661
/JOHN VILLECCO/Supervisory Patent Examiner, Art Unit 2661
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