DETAILED ACTION
Election/Restrictions
Applicant’s election without traverse of Invention I in the reply filed on 10/28/2025 is acknowledged.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 5-9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lebel (US 10635943, cited from IDS ).
Regarding claim 1, Lebel teaches a method for reducing noise and artifacts in previously reconstructed medical images, the method comprising:
(a) accessing patient medical image data with a computer system( 31 in FIG. 1), wherein the patient medical image data comprise one or more medical images acquired with a medical imaging system and depicting a patient (FIG. 1; 815 in FIG. 8));
(b) accessing a trained neural network ( 200 in FIG. 2) with the computer system, wherein the trained neural network has been trained on training data comprising augmented image data, wherein the augmented image data comprise at least one of noise-augmented image data or artifact-augmented image data(Col. 6, last para. to Col. 7, first para., The deep neural network 200 may be trained using pairs of noise-free image data sets and corrupted image data sets … digital photographs of various contents … may be used to train the deep neural network 200 to reduce noise in medical images … such initial image data sets are first transformed to a format specific to the medical imaging modality and then corrupted to provide input and target output image data sets for training the deep neural network; FIG. 5);
(c) inputting the patient medical image data to the trained neural network using the computer system (805 in FIG. 8), generating output as uncorrupted patient medical image data, wherein the uncorrupted patient medical image data comprise one or more medical images depicting the patient and having reduced noise and artifacts relative to the patient medical image data ( 815 in FIG. 8).
Regarding claim 5, Lebel teaches the method of claim 1, wherein the augmented image data comprise artifact-augmented image data generated by extracting artifacts from additional image data and adding the extracted artifacts with the image data( Col. 11, the corruption may include but is not limited to one or more of applying streak artifact the corruption may include but is not limited to one or more of applying streak artifacts acts).
Regarding claim 6, Lebel teaches the method of claim 5, wherein the additional image data comprise at least one of additional patient medical image data ( col. 12, line 55-60, a plurality of non-medical photographs are transformed into a plurality of target images and a plurality of noise images for training the deep neural network) or natural image data retrieved from a natural image database.
Regarding claim 7, Lebel teaches the method of claim 1, wherein the augmented image data comprise both noise-augmented image data and artifact-augmented image data( Col. 7, line 40-45, second transform 425 thus typically introduces noise, artifacts, and/or other typical responses of the imaging system).
Regarding claim 8, Lebel teaches the method of claim 1, wherein the trained neural network comprises a convolutional neural network( FIG. 2).
Regarding claim 9, Lebel teaches the method of claim 1, wherein the medical imaging system is at least one of an x-ray imaging system, a computed tomography (CT) system( Col. 2, line 30-35, the MRI system depicted in FIG. 1) , a magnetic resonance imaging (MRI) system, an ultrasound system, or an optical imaging system.
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) 2-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lebel in view of Mentl ( US 20180268526, cited from IDS).
Regarding claim 2, Lebel teaches the method of claim 1.
Lebel does not expressly teach wherein the augmented image data comprise noise-augmented medical image data generated by combining medical image data obtained with the medical imaging system with the noise-only image data obtained with the medical imaging system.
However, Mentl teaches augmented image data comprise noise-augmented medical image data generated by combining medical image data obtained with the medical imaging system with the noise-only image data obtained with the medical imaging system ([0035], the input 101 is corrupted by a certain type and amount of noise, such as by a noise distribution modeled after real-world noise… The uncorrupted input is used as ground truth data in a training image pair).
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 Lebel and Mentl, by corrupt input image from Lebel by the noise distribution in Mentl, with motivation of “a patch-based approach to learn sparse image representations for denoising image data” ( Mentl, Abstract).
Regarding claim 3, Lebel teaches the method of claim 1, wherein the augmented image data comprise noise-augmented image data generated by combining natural image data retrieved from a natural image database ( Col. 1, line 40-45, the high-resolution of digital non-medical photographs or images can be leveraged for the enhancement or correction of medical images)
Lebel does not expressly teach combining … with the noise-only image data obtained with the medical imaging system.
However, Mentl teaches combining … with the noise-only image data obtained with the medical imaging system(([0035], the input 101 is corrupted by a certain type and amount of noise, such as by a noise distribution modeled after real-world noise).
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 Lebel and Mentl, by corrupt input image from Lebel by the noise distribution in Mentl, with motivation of “a patch-based approach to learn sparse image representations for denoising image data” ( Mentl, Abstract).
Regarding claim 4, Lebel teaches the method of claim 1.
Lebel does not expressly teach wherein the augmented image data comprise noise-augmented image data generated by adding the image data with the noise- only image data obtained with the medical imaging system.
However, Mentl teaches the augmented image data comprise noise-augmented image data generated by adding the image data with the noise- only image data obtained with the medical imaging system([0035], the input 101 is corrupted by a certain type and amount of noise, such as by a noise distribution modeled after real-world noise… The uncorrupted input is used as ground truth data in a training image pair).
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 Lebel and Mentl, by corrupt input image from Lebel by the noise distribution in Mentl, with motivation of “a patch-based approach to learn sparse image representations for denoising image data” ( Mentl, Abstract).
Claim(s) 14 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lebel in view of Varadharajan ( US 20070076841).
Regarding claim 14, Lebel teaches the method of claim 1.
Lebel does not expressly teach wherein the noise-only image data are generated from at least one of phantom image data acquired with the medical imaging system or additional patient image data acquired with the medical imaging system.
However Varadharajan the noise-only image data are generated from at least one of phantom image data acquired with the medical imaging system ( FIG. 21A)
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 Lebel and Varadharajan , by corrupt input image from Lebel by the phantom image taught by Varadharajan, with motivation “ to correct the erroneous data of human projection data with a high efficiency” ( Varadharajan , [0205]).
Regarding claim 16, Lebel in view of Varadharajan teaches the method of claim 14, wherein the additional patient image data are acquired from the patient using the medical imaging system ( Varadharajan, 110 in FIG. 1)
Conclusion
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JIANGENG SUN
Examiner
Art Unit 2661
/Jiangeng Sun/Examiner, Art Unit 2671