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 .
DETAILED ACTION
Response to Amendment
Claims 1-20 are pending in this application [1/30/2026].
Claims 1, 4, 8, 11, 15 and 17 have been amended [1/30/2026].
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 8 and 15 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 based on newly applied reference Moeller et al. (US-2025/0061618).
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.
Claim(s) 1-4, 8-11, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Eggers (EP-4474847A1) in view Moeller et al. (US-2025/0061618).
As to Claim 8, Eggers teaches ‘A system for improving image quality of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) imaging, comprising: a memory encoding processor-executable routines; and a processor configured to access the memory and to execute the processor-executable routines, wherein the routines, when executed by the processor, cause the processor to: acquire a plurality of blades of k-space data of a region of interest in a rotational manner around a center of k-space via a magnetic resonance imaging (MRI) scanner from a coil during a PROPELLER sequence, wherein each blade of the plurality of blades of k-space data comprises a plurality of parallel phase encoding lines sampled in a phase encoding order [par 0013-0015, 0018, 0026-0027, 0039, 0055-0058 – medical system including a memory and processor for magnetic resonance image reconstruction by acquiring subsampled k-space data for imaging a subject where the subsampled k-space data comprises multiple subsets of k-space data that have been acquired in accordance with a PROPELLER acquisition technique using multiple receive coils, where each subset of k-space data represents: one PROPELLER blade, or one PROPELLER blade that has been acquired by a specific receive coil of the receive coils, and may define the number of blades, where the number of blades D may be higher than or equal to the number of slices S, wherein each blade is subsampled in the phase encoding direction by a factor]; utilize a deep learning-based denoising network to denoise each blade of the plurality of blades of k-space data to generate a plurality of denoised blades [par 0026-0028, 0032, 0057-0059, 0071 – denoising at least one aliased image relying on convolutional neural networks in complex or magnitude images to denoising aliased images includes grouping the aliased images according to the respective blades and receive coils]; and utilize a PROPELLER reconstruction algorithm to generate a complex image from the plurality of denoised blades [par 0010, 0018, 0021, 0033, 0059-0061, 0071 – reconstructing a magnetic resonance from the denoised k-space data using a second magnetic resonance image reconstruction of the PROPELLER blades]’.
Egger does not disclose expressly ‘utilize a deep learning-based denoising network to individually denoise each blade of the plurality of blades of k-space data to generate a plurality of denoised blades of k-space data, wherein the denoising occurs in a k-space domain’.
Moeller in the proposed combination of Egger teaches ‘utilize a deep learning-based denoising network to individually denoise each blade of the plurality of blades of k-space data to generate a plurality of denoised blades of k-space data, wherein the denoising occurs in a k-space domain [Abstract, par 0006, 0020, 0027, 0038, 0040-0046 – creating denoised k-space data to be then input to a neural network or other machine learning model by performing denoising directly on k-space data accessed in k-space domain]’.
Egger and Moeller are analogous art because they are from the same field of endeavor, namely image reconstruction. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to include denoising in k-space domain, as taught by Moeller. The motivation for doing so would have been to improving highly-accelerated MRI. Therefore, it would have been obvious to incorporate Moeller with Eggers to obtain the invention as specified in claim 8.
Further, in regards to claim 1, the system of claim 8 performs the computer-implemented method of claim 1.
Further, in regards to claim 15, the computer-implemented of claim 1 is fully embodied on the non-transitory computer-readable medium of claim 15.
As to Claims 2, 9 and 16, Eggers teaches ‘wherein the plurality of blades of k-space data is acquired from a single channel of the coil [par 0066 – radiofrequency coil for manipulating the orientations of magnetic spins, where radiofrequency antenna may contain multiple coil elements and may be referred to as a channel]’.
As to Claims 3 and 10, Eggers teaches ‘wherein the plurality of blades of k-space data is acquired from a plurality of channels of the coil [par 0066 – radiofrequency coil for manipulating the orientations of magnetic spins, where radiofrequency antenna may contain multiple coil elements and may also have multiple receive/transmit elements and the radiofrequency transceiver may have multiple receive/transmit channels]’.
As to Claims 4 and 11, Eggers in view of Moeller teaches ‘wherein the routines, when executed by the processor, further cause the processor to combine corresponding blades of k-space data acquired from the plurality of channels to generate the plurality of blades of k-space data prior to utilizing the deep learning-based denoising network to individually denoise each blade of the plurality of blades of k-space data [par 0027-0028, 0058-0059, 0066 – grouping the aliased images according to the respective blades for denoising from the multiple channels]’.
As to Claim 17, Eggers in view of Moeller teaches ‘wherein the plurality of blades of k-space data is acquired from a plurality of channels of the coil and wherein the processor-executable code, when executed by the processor, further causes the processor to combine corresponding blades of k-space data acquired from the plurality of channels to generate the plurality of blades of k-space data prior to utilizing the deep learning-based denoising network to individually denoise each blade of the plurality of blades of k-space data [par 0027-0028, 0058-0059, 0066 – radiofrequency coil for manipulating the orientations of magnetic spins, where radiofrequency antenna may contain multiple coil elements and may also have multiple receive/transmit elements and the radiofrequency transceiver may have multiple receive/transmit channels and grouping the aliased images according to the respective blades for denoising from the multiple channels]’.
Claim(s) 5-6, 12-13 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eggers in view of Moeller et al. and further in view of Wang et al. (US-2019/0325621).
As to Claims 5, 12 and 18, Eggers in view of Moeller teaches all of the claimed elements/features as recited in independent claims 1, 8 and 15, respectively. Eggers in view of Moeller does not disclose expressly ‘further comprising wherein the routines, when executed by the processor, further cause the processor to utilize a deep learning-based de-streaking network on the complex image to remove streaks’, although Eggers teaches contrast weighting step and signal decay correction step for scaling data to compensate for an expected decay of the signal during the acquisition [par 0025].
Wang teaches ‘further comprising wherein the routines, when executed by the processor, further cause the processor to utilize a deep learning-based de-streaking network on the complex image to remove streaks [par 0038, 0063 – where a system of an embodiment of the subject invention can be good at de-noising or de-streaking using deep learning neural network to reduce artifacts with existing image reconstruction algorithms utilized to generate initial images and deep networks to do more intelligent work based on initial images]’.
Eggers in view of Moeller are analogous art with Wang because they are from the same field of endeavor, namely image reconstruction. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to include de-streaking, as taught by Wang. The motivation for doing so would have been to reducing artifacts in medical images to obtain final high-quality images. Therefore, it would have been obvious to combine Wang with Eggers in view of Moeller to obtain the invention as specified in claims 5, 12 and 18.
As to Claims 6, 13 and 19, Eggers in view of Moeller and Wang teaches ‘wherein the routines, when executed by the processor, further cause the processor to train the deep learning-based denoising network on input-output data pairs utilizing supervised learning, wherein the input-output data pairs comprise near perfect and conventional MR images simulated from natural images, and wherein the deep learning-based denoising network is trained to predict noise in Cartesian acquired images [Eggers: par 0007 – magnetic resonance image of the volume may be obtained by applying a specific acquisition technique performed in accordance with a spatial resolution defined by a field of view (FOV) and a matrix size to define the size of k-space; Wang et al.: par 0038, 0063-0064, 0100 – where a system of an embodiment of the subject invention can be good at de-noising or de-streaking k-space data-set (i.e. cartesian) using deep learning neural network to reduce artifacts in a supervised learning process with existing image reconstruction algorithms utilized to generate initial images and deep networks to do more intelligent work, forward prediction to estimate and output given an unlabeled input based on initial images to reconstructed images]’.
Eggers in view of Moeller are analogous art with Wang because they are from the same field of endeavor, namely image reconstruction. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to include a supervised learning process when de-noising, as taught by Wang. The motivation for doing so would have been to reducing artifacts in medical images to obtain final high-quality images. Therefore, it would have been obvious to combine Wang with Eggers in view of Moeller to obtain the invention as specified in claims 6, 13 and 19.
Allowable Subject Matter
Claims 7, 14 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Eggers in view of Moeller, Wang and further in view of the prior art searched and/or cited does not teach the combination of limitations based on dependency “wherein at least some pairs of simulated images comprise skewed aspect ratios”, as recited in dependent claims 7, 14 and 20.
Conclusion
The prior art made of record
a. US Publication No. 2025/0061618
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/MIYA J CATO/Primary Examiner, Art Unit 2681