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 .
Allowable Subject Matter
Claim 18 is allowed.
Claims 2 and 7 – 16 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.
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.
Claim(s) 1, 2-6, 17, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chen et al. (Improving reproducibility and performance of radiomics
in low-dose CT using cycle GANs) as cited in an IDS.
Regarding claim 1, Chen teaches a computer-implemented method comprising (See page 1, “methods and Materials” for method, page 1 under KEYWORDS, “computed topography”. Also said another way, paper is also directed to a computer implemented method.):
providing a machine learning model, wherein the machine learning model is configured to generate a synthetic 2D image based on input data and parameters of the machine learning model;
providing training data, wherein the training data comprises, for each examination object of a plurality of examination objects, (i) input data and (ii) target data,
wherein the input data comprises one or more stacks of 2D images, wherein each stack represents a 3D examination region of the examination object, wherein each 2D image represents a slice of the 3D examination region,
wherein the target data comprises a stack of 2D target images, wherein the stack of 2D target images represents the 3D examination region of the examination object, wherein each 2D target image represents a slice of the 3D examination region (The GAN is trained using sets ("stacks') of input and target images representing slices
of a 3D examination region of an examination object (the lung) section 2.2, “Randomly chosen samples from two domains are fed to the networks in training a cycle GAN. However, as mentioned in the original cycle GAN article,41 the training will be more successful and stable when focusing on pairs of visually similar images.
In the case of CT scans, assuming all scans belong to the same organ (the lung in our case),we can expect that images belonging to the same slice number will be more similar to each other than images from different slices. Hence, the first slice of a low-dose CT scan will have higher similarity with the first slice of a high-dose CT scan.
Therefore, CT-based cycle GAN training should be fed with pairs of the same (randomly chosen) slice rather than images of different slices. This could be seen as weakly supervised learning. We call this strategy slice-paired training strategy hereafter, a similar training strategy can be found in literature.”);
training the machine learning model, wherein the training comprises, for each examination object of the plurality of examination objects:
unorderedly selecting a slice of the 3D examination region;
inputting the 2D images of the input data representing the selected slice into the machine learning model;
receiving a synthetic 2D image representing the selected slice as an output of the machine learning model;
reducing deviations between the synthetic 2D image and the 2D target image representing the selected slice by modifying model parameters; and
repeating the above training steps until synthetic 2D images have been generated for a pre-defined portion of the 3D examination region (The training of the GAN minimizes a deviation between synthetic/predicted and real target images see section 2.1. In order to accelerate training convergence (See abstract), the method of Chen employs a slice-paired training strategy, which uses pairs of the same randomly chosen ( "unorderedly selected”) input/target slice rather than images of different slices section 2.2. It is implied that the process is repeated for several of these randomly selected slices within a 3D examination region.);
and
outputting and/or storing the trained machine learning model and/or transferring the trained machine learning model to a separate computer system and/or using the trained machine learning model to generate one or more synthetic images of one or more new examination objects (See Fig. 6, & Fig. 6 explanation, “Example of RIDER denoising. (a-1) One original image from RIDER; (b-1) image denoised by EDN (Training at 100 epochs);(c-1) image denoised by CGAN (training at 100 epochs); (d-1) image denoised by cycle GAN trained on simulated data (100 epochs); (e-1)image denoised by cycle GAN trained on real data (100 epochs); (a-2) to (e-2) zoomed ROIs for (a-1) to (e-1).” See page 10 col. 2 3.3 – page 11 before 3.4).
Regarding claim 3, Chen teaches the method of claim 1, wherein each examination object is a mammal, and the examination region is a part of the examination object (See page 13 4 Discussion, notes the region is a human body.).
Regarding claim 4, Chen teaches the method of claim 1, wherein the examination region is or comprises […], lung (See page 8 col. 2 2.5 Lung 1 data set.), brain (see page 14 col. 1 brain), […] breast (See page 2 col. 1 first paragraph) or a part of said parts or another part of the body of a mammal (See page 14 col. 2 human body is any part including another part).
Regarding claim 5, Chen teaches the method of claim 1, wherein the 2D images of the input data and the 2D target images are measured medical images and the synthetic 2D images are synthetic medical images (See page 8 col. 1 first complete paragraph).
Regarding claim 6, Chen teaches the method of claim 1, wherein the 2D images of the input data and the 2D target images are measured radiological images and the synthetic 2D images are synthetic radiological images (See page 8 col. 1 first complete paragraph).
Regarding claim 17, Chen teaches the method of claim 1, wherein the input data comprises, for each examination object of the plurality of examination objects, one or more stacks comprising radiologic images of a first modality, wherein the target data comprises one stack comprising radiologic images of a second modality (See page 8 col. 2 2.5 Radiomics extractions – page 9 col. 2 first incomplete paragraph).
Claim 19 and 20 recite similar limitations to that of claim 1 and is rejected under similar rationale as detailed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Buerger et al. (US 20260141548 A1), abstract, “A system (100) for generating a three-dimensional image of a coronary tree (449) includes a memory (151) and a processor (152). The processor (152) is configured to to: obtain a sequence of two-dimensional angiogram images (410) corresponding to a moving heart from a single viewpoint of an imaging device; and generate, using a trained machine learning model (430), a three-dimensional representation (211A) of the coronary tree (449) based on the sequence of the two-dimensional angiogram images (410) and cardiac motion of the moving heart.”
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/ROBERT J CRADDOCK/Primary Examiner, Art Unit 2618
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