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 .
Election/Restrictions
Claims 14-18 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected Group II, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on March 19, 2026.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/21/2025 and 04/23/2025 have been entered and considered. Initialed copies of the PTO-1449 by the Examiner are attached.
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, the down-sampling an input non-contrast image to output a down-sampled non-contrast image for each of a plurality of channels; generating a contrast image for each channel corresponding to the down-sampled non-contrast image for each channel using a pre-trained image transformation model; and up-sampling the generated contrast image for each channel to generate a contrast-enhanced image corresponding to the input non-contrast image must be shown or the feature(s) canceled from the claim(s). No new matter should be entered.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 102
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 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.
Claims 1 and 7-8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by.
Zaharchuk et al (Pub No.: 20190108634).
Regarding independent claim 1, Zaharchuk teaches a method for generating contrast-enhanced images using a neural network (method to enhance image quality of diagnostic imaging modalities using a lower dose of contrast than is currently possible using a for e.g. a Convolutional Neural Network – see [p][0006][0008]), the method comprising:
down-sampling an input non-contrast image to output a down-sampled non-contrast image for each of a plurality of channels (multi-contrast image which is a combination of contrast and non-contrast image is fed into a model is an encoder-decoder convolutional neural network with 3 encoder layer 500, 502, 504, wherein each layer down-samples the multi-contrast image – see [p][0030-0031] and Fig 1 and Fig 5);
generating a contrast image for each channel corresponding to the down-sampled non-contrast image for each channel using a pre-trained image transformation model (non-contrast (zero-dose) MRI and the 10% low-dose CE-MRI are provided to the network as inputs, and the output of the network is an approximation of the full-dose CE-MRI -see [p][0030] and preprocessed images are then used to train the deep learning network to predict the full-contrast image from the pre-contrast and low-contrast images. The trained network is then used to synthesize full-contrast images from clinical scans of pre-contrast and low-contrast images -see [p][0009]); and
up-sampling the generated contrast image for each channel to generate a contrast-enhanced image corresponding to the input non-contrast image (note that the network contained 3 decoder layers 506, 508, 510, which upsampled the image received from 504 – see [p][0031] and Fig 5).
Regarding claim 7, Zaharchuk teaches the method of claim 1, wherein the up-sampling the generated contrast image for each channel includes: up-sampling the contrast image for each channel to generate a first contrast image for each channel corresponding to the up-sampled contrast image (this model is an encoder-decoder convolutional neural network with 3 encoder steps 500, 502, 504 and 3 decoder steps 506, 508, 510 – see [p][0031]); generating a second contrast image for each channel corresponding to the first contrast image for each channel using the pre-trained image transformation model (there are 3 convolutional layers connected by 3×3 Conv-BN-ReLU. Encoding steps are connected in sequence by 2×2 max-pooling, and decoder steps are connected in sequence by 2×2 up-sampling – see [p][0031]); and up-sampling the second contrast image for each channel to generate the contrast-enhanced image corresponding to the input non-contrast image using the up-sampled second contrast image ([b]ypass concatenate connections 512, 514, 516 combine symmetric layers to avoid resolution loss. The residual connections 518, 520, 522, 524, 526, 528, 530 enable the model to synthesize a full-dose image by predicting the enhancement signal 540 from a difference between pre-dose image 542 and low-dose image 544. The cost function 532 compares the predicted full-dose image 534 and the reference ground-truth full-dose image 536, which enables the optimization of the network parameters via error backpropagation 538 – see [p][0031]).
Regarding independent claim 8, Zaharchuk teaches a contrast-enhanced image generation device using a neural network (system to enhance image quality of diagnostic imaging modalities using a lower dose of contrast than is currently possible using a for e.g. a Convolutional Neural Network – see [p][0006][0008]), the device comprising: a memory ([p][0036]) configured to store one or more instructions (algorithms – see [p][0036][0044]); and a processor (2 NVIDIA GTX 1080-TI GPUs – see [p][0036]) configured to execute the one or more instructions stored in the memory, wherein the instructions, when executed by the processor (see [p][0036]), cause the processor to
down-sample an input non-contrast image to output a down-sampled non-contrast image for each of a plurality of channels (multi-contrast image which is a combination of contrast and non-contrast image is fed into a model is an encoder-decoder convolutional neural network with 3 encoder layer 500, 502, 504, wherein each layer down-samples the multi-contrast image – see [p][0030-0031] and Fig 1 and Fig 5);
generate a contrast image for each channel corresponding to the down-sampled non-contrast image for each channel using a pre-trained image transformation model (non-contrast (zero-dose) MRI and the 10% low-dose CE-MRI are provided to the network as inputs, and the output of the network is an approximation of the full-dose CE-MRI -see [p][0030] and preprocessed images are then used to train the deep learning network to predict the full-contrast image from the pre-contrast and low-contrast images. The trained network is then used to synthesize full-contrast images from clinical scans of pre-contrast and low-contrast images -see [p][0009]); and
up-sample the generated contrast image for each channel to generate a contrast-enhanced image corresponding to the input non-contrast image (note that the network contained 3 decoder layers 506, 508, 510, which upsampled the image received from 504 – see [p][0031] and Fig 5).
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 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 of this title, 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.
Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Zaharchuk et al (Pub No.: 20190108634) in view of Li (Pub No.: US20220208355A1).
Regarding claim 6, Zaharchuk teaches the method of claim 1, wherein the image transformation model is trained by receiving a training non-contrast image for each channel ([t]he preprocessed images are then used to train the deep learning network to predict the full-contrast image from the pre-contrast and low-contrast images – see [p][0009]) and, Zaharchuk does not explicitly teach a training contrast-enhanced image serving as label data for each channel and transforming the training non-contrast image for each channel to a training contrast image for each image.
Li explicitly teaches a training contrast-enhanced image serving as label data for each channel and transforming the training non-contrast image for each channel to a training contrast image for each image (semi-supervised learning makes use of supervised and unsupervised techniques. Semi-supervised learning can involve having correct results for part, but not all, of training data. Therefore, semi-supervised learning typically involves partial inputs having corresponding labels, and other partial inputs having unknown labels (or no labels), and semi-supervised learning algorithms aim to use both types of inputs to learn a mapping from the input to the correct label. Semi-supervised learning algorithms can be suited for tasks where the label is difficult to annotate – see [p][0271]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Zaharchuk of method for generating contrast-enhanced images using a neural network, the method comprising: down-sampling an input non-contrast image with the teachings of Li a training contrast-enhanced image serving as label data for each channel and transforming the training non-contrast image for each channel to a training contrast image for each image.
Wherein having Zaharchuk a training contrast-enhanced image serving as label data for each channel and transforming the training non-contrast image for each channel to a training contrast image for each image.
. The motivation behind the modification would have been for predicting a synthesized full-dose contrast agent image from a low-dose contrast agent image and a pre-dose image by concurrent and simultaneous synthesis of a medical CA-free-AI-enhanced image and medical diagnostic image analysis since both Zaharchuk and Li relates to create synthetic modality of image, wherein Zaharchuk to predict a synthesized full-dose contrast agent image from a low-dose contrast agent image and a pre-dose image while Li concurrently and simultaneously synthesis of a medical CA-free-AI-enhanced image and medical diagnostic image analysis (Please see Zaharchuk et al (Pub No.: 20190108634), see [p][007] and Li (Pub No.: US20220208355A1), see abstract).
Regarding claim 13, which corresponds to claim 6 except for reciting a different statutory category of a device. Therefore, the rejection analysis of claim 6 is fully applicable to claim 13.
Allowable Subject Matter
Claims 2-5 and 9-12 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
PIETSCH et al (Pub No.: 20250078474) discloses the present invention relates to systems, methods and computer programs for training and using a machine learning model to generate synthetic contrast-enhanced computed tomography images with the help of magnetic resonance contrast agents.
Hooge et al (Pub No.: 20250191734) discloses systems, methods, and computer programs disclosed herein relate to training a machine learning model and using the trained machine learning model to generate synthetic images, preferably synthetic medical images.
PIETSCH et al (Pub No.: 20240407663) discloses systems, methods, and computer programs disclosed herein relate to training and using a machine learning model to generate contrast-enhanced magnetic resonance images.
JOST et al (Pub No.: 20240346718 ) discloses a systems and methods for generating artificial contrast-enhanced computed tomography (CT) images. An exemplary computer-implemented method involves receiving representations of an examination region of an examination object after administration of a contrast agent. The representations result from CT examination of the examination region at different X-ray energies. The method involves generating a representation of the contrast agent signals on the basis of the received representations (e.g., a signal intensity distribution brought about by the contrast agent in the examination region). The method involves generating a synthetic representation of the examination region comprising an α-fold addition of the representation of the contrast agent signals to one of the received representations or to a virtual non-contrast agent representation of the examination region. α is a negative or positive real number. The method involves outputting, storing, and/or transmitting the synthetic representation to a separate computer system.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRAE S ALLISON whose telephone number is (571)270-1052. The examiner can normally be reached on Monday-Friday 9am-5pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chineyere Wills-Burns, can be reached on (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDRAE S ALLISON/
Primary Examiner, Art Unit 2673
May 29, 2026
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