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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 23, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1 and 14-15 are objected to because of the following informalities:
Claim 1, “receiving an input image comprising” should read “receiving input images comprising” being that the input is further defined to be a pre-contrast image and a full-dose image.
Similar issue in claim 15
Further, if “input image” is changed to “input images” each other corresponding spot discussing input image should additionally be changed
Claim 14, “is further” should read “are further”
Claim 14, “generate” should read “to generate”
Appropriate correction is required.
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.
Claims 1-20 are 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, “a second model trained to denoise an image.” The phrase “an image” leaves the claim unclear to what the second model is acting upon. The examiner believes based on the specification the second model should read to either be acting on the input image, or the contrast-enhanced image. The claim should be amended to reflect the correct image the second model is operating on.
A similar issue is present in claim 15 and should be clarified.
Claims 2-14 and 16-20 are rejected for inheriting the deficiency of claims 1 and 15, while also failing to cure the deficiency.
Claim 8 claims “a transforming magnetic resonance (MR) device.” This term is not defined in the specification to a manner for a person of ordinary skill in the art to understand what a transforming MR device is or how this is different than typical MRI devices.
Claims 9-14 are rejected for inheriting the deficiency of claims 1 and 15, while also failing to cure the deficiency.
Claim 13 claims “a first model trained to learn features of the respective input image” and claim 13 indirectly depends on claim 1. Claim 1 states, “a first model trained to predict a contrast-enhanced image.” It is unclear if these are the same “first model” or if these are two different models. If they are the same model, they should reflect proper antecedent basis; if they are different models they should be renamed for clarity.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter: the closest prior arts of record teach methods of processing MRI images to limit contrast dose while maintaining optimal image quality. However, none of them alone or in any combination teaches inputting images containing a pre-contrast image and a full-dose image and selecting a path to process the input images where optional paths are made of two models where one model is trained to predict a contrast-enhanced image and another model trained to perform denoising.
The closest prior art being U.S. Publication No. 2021/024158 to Zaharchuk et al. (hereinafter Zaharchuk) discloses, “A method for diagnostic imaging with reduced contrast agent dose uses a deep learning network (DLN) [114] that has been trained using zero-contrast [100] and low-contrast [102] images as input to the DLN and full-contrast images [104] as reference ground truth images (abstract).” Further, Zaharchuk discloses, “Surprisingly, the techniques of the present invention are able to predict a synthesized full-dose contrast agent image from a low-dose contrast agent image and a pre-dose image. The low dose may be any fraction of the full dose, but is preferably 1/10 or less of the full dose. Significantly, naively amplifying the contrast enhancement of a 1/10 low-dose CE-MRI by a factor of ten results in poor image quality with widespread noise and ambiguous structures. Even though the low-contrast image cannot be used for diagnosis directly, or by simply amplifying its uptake, the techniques of the present invention remarkably are able to recover the full contrast signal and generate predicted full-contrast images with high diagnostic quality. Specifically, in experimental tests, the method yielded significant improvements over the 10% low-dose images, with over 5 dB PSNR gains, 11% SSIM increased, and improvements in ratings on image quality and contrast enhancement (paragraph 0007)” and further at paragraph 0029, “By using a deep learning method, we learn the denoising to generate high-quality predicted contrast uptake 410 and then combine this with the pre-contrast scan 400 to synthesize a full-dose CE-MRI image 412.”
However, Zaharachuk fails to disclose inputting images containing a pre-contrast image and a full-dose image and selecting a path to process the input images where optional paths are made of two models where one model is trained to predict a contrast-enhanced image and another model trained to perform denoising.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Patent No. 10,467,751 discloses, “adaptively tune model parameters to approximate the reference image from an initial set of the input and reference images, with the goal of outputting an improved quality image of other sets of low SNR low resolution images, for analysis by a physician (abstract).”
U.S. Publication No. 2022/0188602 discloses, “Systems and methods for denoising a magnetic resonance (MR) image utilize an unsupervised deep convolutional neural network (U-DCNN) (abstract).”
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Courtney J. Windsor whose telephone number is (571)272-3956. The examiner can normally be reached Monday - Friday 8:00 - 4:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Villecco can be reached at 571-272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/COURTNEY JOAN NELSON/Primary Examiner, Art Unit 2661
Prosecution Insights