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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 1 is indefinite because it teaches “ different independent insertions” if it unclear what makes the different insertion independent and how the different insertions are performed. Appropriate correction is required.
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) 1-16 rejected under 35 U.S.C. 103 as being unpatentable over Wu et al (Iterative low dose CT reconstruction with priors trained by artificial neural network) in view of Zhang et al (Improving CBCT quality to CT level using deep learning with generative adversarial network)
As to claim 1, Wu teaches the method for generating noise-reduced medical images, the method comprising: a) accessing projection data with a computer system, wherein the projection data comprise x-ray projection data acquired from a subject using a computed tomography ( Introduction, pg. 1: "In this work, we proposed a novel iterative CT reconstruction method based
on priors learned by a k-sparse autoencoder [13], which will be further explain in section II-B."
receiving an input dataset comprising tomographic projection data of an object)
(b) generating training data from the projection data using the computer system, wherein generating the training data comprises Three-Dimensional Reconstruction,( pg. 3: "Due to the large number of variables in the fully
connected neural network, it was impractical to train the autoencoders on 3D patches. Instead,
three independent autoencoders were trained from axial, sagittal and coronal slices, and the
reconstruction problem was reformed as Equation 10, where S stands for one of the three
directions. Psm is the patch extraction matrix along the sth direction);
generating a set of low-quality projection data by inserting noise to the projection data in different independent insertions (pg. 4: "Quarter-dose data were provided by the challenge committee, which were simulated from the normal-dose data by noise injection.);
While Wu teaches the limitation above, Wu fails to teach” generating augmented low-quality projection data by applying a data augmentation technique to the low-quality projection data;
reconstructing low-quality images from the augmented low-quality projection data;
generating augmented projection data by applying the data augmentation technique to the projection data; and reconstructing high-quality images from the augmented projection data;
wherein the low-quality images and the high-quality images comprise the training data; (c) training a neural network on the training data; (d) reconstructing images of the subject from the projection data; and (e) applying the reconstructed images of the subject to the trained neural network,
generating output as the noise-reduced images of the subject.”
Zhang teaches exactly such a training arrangement. Specifically, Zhang states that "150
paired pelvic CT and CBCT scans were used for model training and validation a total of 12000 slice
pairs of CT and CBCT were used for model training, while 10-cross validation was applied to verify
model robustness" (Abstract) and explains that "the Generator was used to generate synthetic CT (sCT) from the original CBCT, and the Discriminator was used to distinguish the synthetic CT (sCT) from the reference CT (rCT). the sCT and rCT slices were used as input" (2.2 Pix2pix GAN Architecture with
Feature Matching, pg. 3). Additionally, Zhang defines the reference CT as ground truth, stating that "all the deep-learning generated synthetic CTs (sCT) were compared to this reference" (2.1 Data Acquisition and Preprocessing, pg. 3). Thus, Zhang teaches a first training subset comprising higher-quality CT/reference CT data serving as ground-truth data and a second training subset comprising lower-quality CBCT data serving as training input data. Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to apply Zhang's known higher quality/ground-truth versus lower- quality/input training methodology to the machine-learning reconstruction framework of Wu because both references are directed to improving tomographic image reconstruction quality using machine learning. Applying Zhang's training technique to Wu's learned reconstruction prior would have predictable improved the quality and robustness of the learned regularizer by training the model using higher-quality ground-truth image data while utilizing lower-quality image data as training inputs, thereby improved reconstruction performance with a reasonable expectation of success. Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
As to claim 2,Wu et al teaches the method of claim 1, wherein the data augmentation technique comprises applying rotations to the low-quality projection data and the projection data (Table II, rotation).
As to claim 3,Wu et al teaches the method of claim 2, wherein the rotations are applied in an angular increment less than 360 degrees (the stability of the proposed algorithm and investigate its dose reduction limit, lower-dose data was generated by angular subsampling from the quarter-dose data. A portion of projections uniformly distributed across 360° was removed for further dose reduction, section D page 9).
As to claim 4,Wu et al teaches the method of claim 1, wherein generating the set of low-quality projection data by inserting noise to the projection data in different independent insertions comprises inserting noise at a selected dose noise level corresponding to a dose value that is lower than a dose used to acquire the projection data(pg. 4: "Quarter-dose data were provided by the challenge committee, which were simulated from the normal-dose data by noise injection.).
As to claim 5, Wu et al teaches the method of claim 4, wherein the selected dose noise level corresponds to a 10 percent dose relative to the dose used to acquire the projection data( figure 7 and 9 different noise level).
As to claim 6, Wu et al teaches the method of claim 4, wherein the selected dose noise level corresponds to a 25 percent dose relative to the dose used to acquire the projection data ( figure 7 and 9 different noise level).
As to claim 7, Zhang et al teaches the method of claim 1, wherein generating the training data comprises pairing low-quality images with high-quality images based on the data augmentation technique (2.1 Data Acquisition and Preprocessing, pg. 3). Thus, Zhang teaches a first training subset comprising higher-quality CT/reference CT data serving as ground-truth data and a second training subset comprising lower-quality CBCT data serving as training input data).
As to claim 8, Zhang et al teaches the method of claim 7, wherein the data augmentation technique comprises applying rotations to the low-quality projection data and the projection data and low-quality images are paired with high-quality images based on rotation angle used during data augmentation(To control the overfitting, three methods were utilized. First, before training, all images were augmented by horizontally flipping, a small angle rotation, as well as adding Med Phys. Author manuscript; some background noise, section 2.4).
As to claim 9, Zhang et al teaches the method of claim 7, wherein pairing the low-quality images and high-quality images comprises matching image patches in the low-quality images with image patches in the high-quality images (2.2 Pix2pix GAN Architecture with Feature Matching, pg. 3).
As to claim 10, Zhang et al teaches the method of claim 1, wherein generating the training data comprises grouping low-quality images and high-quality images into different groups based on the data augmentation technique(To control the overfitting, three methods were utilized. First, before training, all images were augmented by horizontally flipping, a small angle rotation, as well as adding Med Phys. Author manuscript; some background noise, section 2.4).
As to claim 11, Zhang et al teaches the method of claim 10, wherein the data augmentation technique comprises applying rotations to the low-quality projection data and the projection data, and the low-quality images and high-quality images are grouped based on rotation angles used during data augmentation (To control the overfitting, three methods were utilized. First, before training, all images were augmented by horizontally flipping, a small angle rotation, as well as adding Med Phys. Author manuscript; some background noise, section 2.4).
As to claim 12, Zhang et al teaches the method of claim 10, wherein at least one of the different groups is selected for validation of the trained neural network(Data from 30 pelvic patients were included. Each patient had one planning CT and five CBCT scans, a total of 150 pairs of CT-CBCT were used for model training and validation purposes. Section 2.1).
As to claim 13, Zhang et al teaches the method of claim 1, wherein the neural network is a convolutional neural network(The Generator was implemented using U-net architecture, in which each Conv-ReLU-BN block consists of either convolution or de-convolution layers with kernel size of 3×3, a batch normalization layer (BN) and a leaky rectified linear unit (ReLU);section 2.2).
As to claim 14, Zhang et al teaches the method of claim 13, wherein the convolutional neural network is a residual convolutional neural network (The Generator was implemented using U-net architecture, in which each Conv-ReLU-BN block consists of either convolution or de-convolution layers with kernel size of 3×3, a batch normalization layer (BN) and a leaky rectified linear unit (ReLU);section 2.2).
As to claim 15, Zhang et al teaches the method of claim 1, further comprising inserting noise to the projection data before applying the data augmentation technique to the projection data to generate the augmented projection data (all images were augmented by horizontally flipping, a small angle rotation, as well as adding background noise; section 2.4).
As to claim 16, Wu et al teaches the method of claim 15, wherein the amount of noise inserted to the projection data is less than the noise inserted when forming the low-quality projection data (the dataset from the low-dose challenge was simulated with noise injection, which could be different from real situations. But since the KSAE was trained in an unsupervised way and no knowledge on the noise property was assumed during the training, the method should be still applicable to real data., conclusion section).
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NANCY . BITAR
Examiner
Art Unit 2664
/NANCY BITAR/Primary Examiner, Art Unit 2664
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