DETAILED ACTION
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) 1-2, 9-17, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over El-Zein (“A Realistic Collimated X-Ray Image Simulation Pipeline”) in view of Birkhold (US 20210330274 A1).
Regarding claim 1, El-Zein discloses a method for performing image processing in an X-ray imaging system, the X-ray imaging system having a collimator for collimating an incident direction of X-rays with respect to an X-ray detector, the method comprising: each training image of the plurality of training images being obtained by (Pg. 6, ¶ 1 teaches using a collimator. Pg. 2, last paragraph, teaches “The paper physically motivated image processing pipeline is presented that simulates the characteristics of real collimators enabling the expansion of limited datasets of X-ray images without collimators. The data augmentation method enables the generation of unlimited pre-processed image data e.g. for training DNNs.”)
receiving a first 2D projection image acquired by an X-ray imaging system under a condition that no collimator is applied for X-ray collimation, (Pg. 6, ¶ 1 teaches using a acquiring an open field x-ray image with no collimator.)
determining a collimator simulation parameter, (See pg. 6, ¶ 2 and details of the collimator simulation parameters at pg. 3.)
estimating a scatter field for the first 2D projection image, generating a noise- simulating image based on the estimated scatter field, and generating a penumbra-simulating image to simulate a penumbra caused by a collimator, based on the determined collimator simulation parameter, and (See pg. 4, Sections “Scattered Radation Simulation” (which shows the penumbra/transition zone simulation), “Scatter Estimation”, “Scatter Correction” “Poisson Noise Simulation”.)
generating a second 2D projection image, as the training image, by combining the noise-simulating image, the penumbra-simulating image, and the first 2D projection image. (As above, see pg. 4, Sections “Scattered Radation Simulation” (which shows the penumbra/transition zone simulation), “Scatter Estimation”, “Scatter Correction” “Poisson Noise Simulation” which teach combining the noise and penumbra simulated images with an initial 2D projection image.)
In the field of x-ray image enhancement Birkhold teaches what does not expressly disclose, namely, obtaining a two-dimensional (2D) projection image acquired by the X-ray imaging system and inputting the obtained 2D projection image into a pretrained machine-learning model, to infer a 2D projection image from an output of the pretrained machine-learning model, the inferred 2D projection image having an image quality better than an image quality of the obtained 2D projection image, wherein the pretrained machine-learning model is trained on a plurality of training images, (Birkhold teaches a system for x-ray image quality enhancement via a deep learning U-net, see Abstract. ¶ 0014 teaches a trained artificial intelligence algorithm for carrying out a noise correction process of x-ray image data, including the steps of receiving of training datasets of x-ray images, that include x-ray image sub datasets with known noise and with at an error-free reference image dataset, training of the algorithm using the training datasets, and providing the trained determination algorithm.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined El-Zein’s technique for improving machine learning training of x-ray images with Birkhold’s machine learning training system of x-ray images. El-Zein teaches a technique for generating partially synthetic x-ray images specifically to augment the training of deep neural networks with more training images. El Zein does not itself disclose the step of using the actual pre-trained inference model that ingests the initial x-ray images after the pre-training. Birkhold discloses inputting an obtained x-ray image into a pretrained machine-learning model to infer a 2D projection image with a better image quality. The combination constitutes the repeatable and predictable result of simply applying this essential step here of using the inference model and cannot be considered a non-obvious improvement in view of the relevant prior art here. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Regarding claim 2, the above combination discloses the method of claim 1, wherein the determined collimator simulation parameter includes a scaling factor, and the step of estimating the scatter field further includes multiplying the scaling factor with a mean intensity of the first 2D projection image to obtain a constant scatter signal, as the estimated scatter field. (See El Zein, pg. 4, Section “Scatter Estimation”)
Regarding claim 9, the above combination discloses the method of claim 8, wherein the derived noise image includes noise caused by the estimated scatter field, as well as at least one of electronic noise, noise from a polychromatic beam, and noise caused by X-ray detector gain. (El-Zein’s noise simulation is electronic noise, see pg. 5.)
Regarding claim 10, the above combination discloses the method of claim 1, wherein the determined collimator simulation parameter includes a collimator geometry parameter indicating a percentage of the first 2D projection image to be collimated. (El-Zein’s collimator simulation parameter includes a collimator geometry parameter indicating a percentage, see pg. 3, “Binary Mask Sampling Strategy”)
Regarding claim 11, the above combination discloses the method of claim 10, wherein the determined collimator simulation parameter further includes another collimator geometry parameter indicating a collimation aspect ratio between a row direction and a column direction of the X-ray detector. (El-Zein’s collimator simulation parameter includes an aspect ratio/width and heigh parameters, see pg. 3, “Binary Mask Sampling Strategy”)
Regarding claim 12, the above combination discloses the method of claim 10, wherein the determined collimator simulation parameter further includes a Gaussian kernel width, and the step of generating the penumbra-simulating image further includes: generating a first binary mask image, based on the collimator geometry parameter, generating a second binary mask image, the second binary mask image being an inverse of the first binary mask image, and applying a Gaussian blur to the first 2D projection image, based on the Gaussian kernel width, the first binary mask image, and the second binary mask image. (El-Zein’s, see pg. 3, “Binary Mask Sampling Strategy” teach multiple a first and second binary mask, the second being an inverse obtained via rotation, see Fig. 2(c). See Gaussian kernel at pg. 5, ¶ 1.)
Regarding claim 13, the above combination discloses the method of claim 1, wherein the step of determining the collimator simulation parameter includes receiving a collimator simulation parameter specified by a user, or selecting a collimator simulation parameter from a set of predetermined collimator simulation parameters, as the determined collimator simulation parameter. (El-Zein’s, see pg. 3, “Binary Mask Sampling Strategy” teach a set of multiple simulated masks via simulation parameters.)
Regarding claim 14, the above combination discloses the method of claim 1, wherein both the noise-simulating image and the penumbra- simulating image have a same size as the first 2D projection image. (See pg. 7, Fig 4.)
Regarding claim 15, the above combination discloses the method of claim 1, wherein the X-ray imaging system is a 2D projection X-ray imaging system, a Computed Tomography (CT) imaging system, or a C-arm interventional imaging system. (See El-Zein and Birkhold in rejection of claim 1.)
Claims 16-17, 19, and 20 are the apparatus claims corresponding to the method of claims 1-2, 10 and 12. El-Zein Pg. 6, ¶ 1 teaches a system as does Birkhold ¶ 0039 with processing circuitry. Remaining limitations are rejected similarly. See detailed analysis above.
Claim(s) 3, 4 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over El-Zein (“A Realistic Collimated X-Ray Image Simulation Pipeline”) in view of Birkhold (US 20210330274 A1) and Pinto (“A deep learning approach to estimate x-ray scatter in digital breast tomosynthesis: From phantom models to clinical applications”)
Regarding claim 3, the above combination discloses the method of claim 1, but not the remaining limitations.
In the field of x-ray scatter estimation Pinto teaches estimating the scatter field further includes inputting the first 2D projection image into a second pretrained neural network and obtaining a scatter signal predicted by the second pretrained neural network, as the estimated scatter field. (Section 2.4, “Deep learning model for scatter estimation”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined El-Zein’s technique for improving machine learning training of x-ray images with Pinto’s x-ray scatter estimation. El-Zein teaches a technique for generating partially synthetic x-ray images to augment the training of deep neural networks with more training images and uses x-ray scatter estimation via convolution kernel. Scatter estimation is performed but via a convolution kernel. Pinto teaches scatter estimation via a deep learning neural network. The combination constitutes the repeatable and predictable result of simply applying Pinto’s technique for a different method of accomplishing scatter estimation. Simply substituting this method here cannot be considered a non-obvious improvement in view of the relevant prior art here. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Regarding claim 4, the above combination discloses the method of claim 1, wherein the step of estimating the scatter field further includes, performing a Monte Carlo simulation based on the first 2D projection image and a 3D phantom to derive a scatter signal, as the estimated scatter field. (Pinto, Section “2.2 Monte Carlo simulations”)
Regarding claim 8, the above combination discloses the method of claim 1, wherein the step of generating the noise-simulating image further includes performing a Monte Carlo simulation using the estimated scatter field and the first 2D projection image to derive a noise image, as the generated noise-simulating image. (Pinto, Section “2.2 Monte Carlo simulations”)
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over El-Zein (“A Realistic Collimated X-Ray Image Simulation Pipeline”) in view of Birkhold (US 20210330274 A1) and Rossignol (“Scatter estimation and correction using time-of-flight and deconvolution in x-ray medical imaging”).
Regarding claim 5, the above combination discloses the method of claim 1, but not the remaining limitations.
In the field of x-ray scatter estimation Rossignol teaches estimating the scatter field further includes, solving a radiative transfer equation based on the first 2D projection image and a 3D phantom to derive a scatter signal, as the estimated scatter field. (Section 2, Model teaches a scatter estimation solving a radiative transfer equation based on the first 2D projection image and a 3D phantom to derive a scatter signal.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined El-Zein’s technique for improving machine learning training of x-ray images with Rossingol x-ray scatter estimation. El-Zein teaches a technique for generating partially synthetic x-ray images to augment the training of deep neural networks with more training images and uses x-ray scatter estimation via convolution kernel. Scatter estimation is performed but via a convolution kernel. Rossingol teaches scatter estimation via a radiative transfer equation based on the first 2D projection image and a 3D phantom. The combination constitutes the repeatable and predictable result of simply applying Rossingol’s technique for a different method of accomplishing scatter estimation. Simply substituting this method here cannot be considered a non-obvious improvement in view of the relevant prior art here. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Claim(s) 6, 7 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over El-Zein (“A Realistic Collimated X-Ray Image Simulation Pipeline”) in view of Birkhold (US 20210330274 A1) and Sehnert (US 20120148156 A1)
Regarding claim 6, the above combination discloses the method of claim 1, but not the remaining limitations.
In the field of x-ray scatter estimation Sehnert teaches generating the noise-simulating image further includes retrieving, from a predetermined look-up table, an image corresponding to the estimated scatter field, as the generated noise-simulating image. (¶ 0020 teaches generating a look-up table for characterized image scatter within the radiographic image.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined El-Zein’s technique for improving machine learning training of x-ray images with Sehnert’s x-ray scatter estimation. El-Zein teaches a technique for generating partially synthetic x-ray images to augment the training of deep neural networks with more training images and uses x-ray scatter estimation via convolution kernel. Scatter estimation is performed but via a convolution kernel. Sehnert teaches scatter estimation via a look up table. The combination constitutes the repeatable and predictable result of simply applying Sehnert’s technique for a different method of accomplishing scatter estimation. Simply substituting this method here cannot be considered a non-obvious improvement in view of the relevant prior art here. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Regarding claim 7, the above combination discloses the method of claim 6, wherein the retrieved image is predetermined to simulate noise caused by scatter corresponding to the estimated scatter field, as well as at least one of electronic noise, noise from a polychromatic beam, and noise caused by X-ray detector gain. (El-Zein’s noise simulation is electronic noise, see pg. 5.)
Claim 18 is the apparatus claims corresponding to the method of claims 6. El-Zein Pg. 6, ¶ 1 teaches a system as does Birkhold ¶ 0039 with processing circuitry. Remaining limitations are rejected similarly. See detailed analysis above.
Conclusion
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/RAPHAEL SCHWARTZ/ Examiner, Art Unit 2671
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