DETAILED ACTION
Response to Arguments
Applicant’s arguments with respect to rejections under 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.
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.
Claim(s) 1-5,8-10, 12, 15-22,25-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Meyer ( US 20230342886) in view of Zou ( "EDCNN: A Novel Network for Image Denoising," 2019 ) and Garwood(US 20080262339).
Regarding claim 1, Meyer teaches a method for generating a magnetic resonance (MR) image, the method comprising:
obtaining a set of input images from a magnetic resonance imaging (MRI) system( 150 in Fig. 1; 302 in Fig. 3B)
inputting the set of images to a denoising neural network that has been trained to perform denoising on a set of input images( 304-312 in Fig. 3B); and
obtaining a denoised output image from the denoising neural network( 314 in Fig. 3B).
Meyer does not expressly teach wherein the denoising neural network incorporates residual learning with an average of the input images being applied for skip connections.
However, Zou teaches denoising neural network incorporates residual learning with an average of the input images being applied( Fig. 2; page 1331, left column, the averaged mean squared error between the desired residual images and estimated ones from noisy input can be adopted as the loss function) for skip connections ( page 1330, right column, the use of residual excitation can narrow the difference of between observation x and latent clean image y).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Meyer and Zou, by substituting the denoising network in Meyer with the one taught by Zou, with motivation to “help the output image to obtain more useful information from the input one” ( Zou, page 1330, right column).
Meyer in view of Zou does not expressly teach wherein the input images are obtained using a multi-NEX (Number of EXcitations) or multi- NSA (Number of Signal Averages or Acquisitions) protocol ( and the set of input images includes a number of images equal to the number of NEX or NSA.
However Garwood teaches wherein the input images are obtained using a multi-NEX (Number of EXcitations) ( [0030], multi-NEX acquisitions are performed) or multi- NSA (Number of Signal Averages or Acquisitions) protocol ( and the set of input images includes a number of images equal to the number of NEX or NSA.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Meyer in view of Zou and Garwood, by using multi-NSA protocol taught by Garwood in Meyer’s MRI scanning process, with motivation “ to acquire images of the same resolution using lower field strengths … and … to increase SNR”( Garwood, [0030]).
Regarding claim 2, Meyer in view of Zou and Garwood teaches the method of claim 1 wherein the NEX or NSA is exactly two and the set of images includes exactly two images(Meyer, Abstract, The MR image data is subject to complex de-noising operations directly and simultaneously on both real and imaginary parts of the image data).
Regarding claim 3, Meyer in view of Zou and Garwood teaches the method of claim 1 wherein the NEX or NSA is greater than two and the set of images includes more than two images( Meyer, [0122], raw fastMRI dataset contains nearly 7000 fully sampled multi-coil brain MRIs).
Regarding claim 4, Meyer in view of Zou and Garwood teaches the method of claim 1 wherein the input images are two-dimensional (2D) images and the denoising neural network includes a convolutional neural network with one or more 2D kernels(Meyer, [0070], Embodiments of the present disclosure may be applied to 2D images; [0127], All of the ℂConv kernels have a size of 3×3 and a depth of 64)
Regarding claim 5, Meyer in view of Zou and Garwood teaches the method of claim 1 wherein the input images are three-dimensional (3D) images and the denoising neural network includes a convolutional neural network with one or more 3D kernels(Meyer, [0070], Embodiments of the present disclosure may be applied to 2D images, 3D images, or both; [0127], All of the ℂConv kernels have a size of 3×3 and a depth of 64) .
Regarding claim 8, Meyer in view of Zou and Garwood teaches the method of claim 1 further comprising training the denoising neural network using a training data set comprising real MR images with different signal-to-noise ratios( [0012], Acquiring paired low and high SNR images).
Regarding claim 9, Meyer in view of Zou and Garwood teaches the method of claim 8 wherein the training data set includes training images obtained using 2-NEX acquisitions and corresponding ground truth images obtained using multi-NEX acquisitions with NEX greater than 2( NEX number is not given patentable weight, because it does not change the method steps of claimed invention, as stipulated in page 8, Ex parfe JAMES PRESCOTT CURRY
Common situations involving nonfunctional descriptive material are:
- a computer-readable storage medium that differs from the prior art solely with respect to nonfunctional descriptive material, such as music or a literary work, encoded on the medium,
- a computer that differs from the prior art solely with respect to nonfunctional descriptive material that cannot alter how the machine functions (i.e., the descriptive material does not reconfigure the computer), or
- a process that differs from the prior art only with respect to nonfunctional descriptive material that cannot alter how the process steps are to be performed to achieve the utility of the invention).
Regarding claim 10, Meyer in view of Zou and Garwood teaches the method of claim 9 wherein the ground truth images have NEX at least equal to 8( NEX number is not given patentable weight, because it does not change the method steps of claimed invention, as stipulated in page 8, Ex parfe JAMES PRESCOTT CURRY
Common situations involving nonfunctional descriptive material are:
- a computer-readable storage medium that differs from the prior art solely with respect to nonfunctional descriptive material, such as music or a literary work, encoded on the medium,
- a computer that differs from the prior art solely with respect to nonfunctional descriptive material that cannot alter how the machine functions (i.e., the descriptive material does not reconfigure the computer), or
- a process that differs from the prior art only with respect to nonfunctional descriptive material that cannot alter how the process steps are to be performed to achieve the utility of the invention).
Claims 12, 15-17 recite the MRI system for the method in claims 1-5. Since Meyer also teaches a MRI system ( Fig. 1), claims 12, 15-17 are also rejected.
Claims 18-22,25-26 recite the medium for the method in claims 1-5, 8-10, thus are also rejected
Claim(s) 6, 11, 23, 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Meyer in view of Zou and Garwood, further in view of Abdishektaei (US 20200249306) .
Regarding claim 6, Meyer in view of Zou and Garwood teaches the method of claim 1.
Meyer in view of Zou and Garwood does not expressly teach wherein the input images are complex-valued images and the denoising neural network processes the real and imaginary parts of each input image as separate channels.
However Abdishektaei teaches the input images are complex-valued images and the denoising neural network processes the real and imaginary parts of each input image as separate channels( [0010], train the CNN and calculating a two-channel training output of complex output image data; [0055], the complex planes were converted into two channel real-valued data 535, 575 before plugging into the U-Net 500 for training).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Meyer in view of Zou and Garwood with that of Abdishektaei, by substituting the neural network in Meyer with that taught by Abdishektaei, with motivation of “suppressing artifacts in MRI images” ( Abdishektaei, [0008]).
Regarding claim 11, Meyer in view of Zou and Garwood teaches the method of claim 1.
Meyer in view of Zou and Garwood does not expressly teach wherein the images are complex-valued images and the real and imaginary parts of each image are processed as separate channels in the denoising neural network.
However, Abdishektaei teaches the images are complex-valued images and the real and imaginary parts of each image are processed as separate channels in the denoising neural network( [0010], train the CNN and calculating a two-channel training output of complex output image data; [0055], the complex planes were converted into two channel real-valued data 535, 575 before plugging into the U-Net 500 for training).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Meyer in view of Zou and Garwood with that of Abdishektaei, by substituting the neural network in Meyer with that taught by Abdishektaei, with motivation of “suppressing artifacts in MRI images” ( Abdishektaei, [0008]).
Claims 23 and 27 recite the medium for the method in claims 6 and 11, and thus also rejected.
Conclusion
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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JIANGENG SUN
Examiner
Art Unit 2661
/Jiangeng Sun/Examiner, Art Unit 2671