# US 2025/0095142 A1 · Iterative restoration of corrupted mr images > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0095142 A1 - Document ID: US20250095142A1 - Status: Published application - Kind: A1 - Application number: 18/470,787 - Filed: 2023-09-20 - Published: 2025-03-20 - Earliest claimed: 2023-09-20 - Assignees: Siemens Healthineers AG - Applicants: Siemens Healthineers AG - Inventors: Mahmoud Mostapha; Mariappan S. Nadar - Web page: https://patentgrep.com/patent/US20250095142A1 ## CPC - G06T 7/0012: {Biomedical image inspection} - G06T 3/40: Scaling of whole images or parts thereof, e.g. expanding or contracting - G06T 5/60: using machine learning, e.g. neural networks - G06T 5/70: Denoising; Smoothing - G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS - G16H 30/40: for processing medical images, e.g. editing - G06T 2207/10088: Magnetic resonance imaging [MRI] - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30168: Image quality inspection - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract Systems and methods for image restoration of medical imaging data using an incremental process. The image restoration problem is decomposed into a sequence of intermediate steps that are easier to process than a single large step directly from the input to an output. Intermediate reconstructions are generated iteratively which provide for mapping a low-quality input to a high-quality reconstruction through a sequence of slightly less corrupted images. ## Claims ### Claim 1 A method for image restoration, the method comprising: acquiring medical imaging data; inputting the medical imaging data into an iterative restoration network, the iterative restoration network configured to output higher quality medical imaging data using multiple incremental steps that provide a sequence of slightly less corrupted images; and outputting, by the iterative restoration network, the higher quality medical imaging data. ### Claim 2 (depends on 1) The method of claim 1, wherein the medical imaging data is acquired using a magnetic resonance imaging device. ### Claim 3 (depends on 1) The method of claim 1, wherein the iterative restoration network is configured to reconstruct a high-resolution image from a low-resolution image. ### Claim 4 (depends on 1) The method of claim 1, wherein the iterative restoration network is configured to denoise the medical imaging data. ### Claim 5 (depends on 1) The method of claim 1, wherein each incremental step includes a CNN and a data consistency layer. ### Claim 6 (depends on 1) The method of claim 1, wherein a rate of restoration at each incremental step is controlled by a predefined parameter. ### Claim 7 (depends on 6) The method of claim 6, wherein the predefined parameter is defined as a function of time or as a constant speed. ### Claim 8 (depends on 1) The method of claim 1, wherein the iterative restoration network is trained by optimizing a loss of a convex combination of input and ground truth images. ### Claim 9 (depends on 1) The method of claim 1, wherein an amount of white noise is added to an output of each incremental step. ### Claim 10 A system for image restoration, the system comprising: a medical imaging device configured to acquire medical imaging data of a patient; an iterative restoration network with a plurality of stages trained using machine learning, each stage of the plurality of stages configured to incrementally improve a quality of input image data from a previous stage; and a processor configured to apply the iterative restoration network to medical imaging data from the medical imaging device and to provide a representation of the patient based on an output of the iterative restoration network. ### Claim 11 (depends on 10) The system of claim 10, wherein the medical imaging device comprises a magnetic resonance imaging device. ### Claim 12 (depends on 10) The system of claim 10, wherein the iterative restoration network is configured to reconstruct a high-resolution image from a low-resolution image. ### Claim 13 (depends on 10) The system of claim 10, wherein the iterative restoration network is configured to denoise the medical imaging data. ### Claim 14 (depends on 10) The system of claim 10, wherein a rate of an incremental improvement by each stage of the plurality of stages is controlled by a predefined parameter. ### Claim 15 (depends on 14) The system of claim 14, wherein the predefined parameter is defined as a function of time or as a constant speed. ### Claim 16 (depends on 10) The system of claim 10, wherein the iterative restoration network is trained by optimizing a loss of a convex combination of input and ground truth images. ### Claim 17 A non-transitory computer readable storage medium comprising a set of computer-readable instructions stored thereon which, when executed by at least one processor cause the processor to: acquire medical imaging data; input the medical imaging data into an iterative restoration network, the iterative restoration network configured to output higher quality medical imaging data using a plurality of incremental steps that provide a sequence of slightly less corrupted images; and output, by the iterative restoration network, higher quality medical imaging data. ### Claim 18 (depends on 17) The non-transitory computer readable storage medium of claim 17, further comprising instructions to: display the higher quality medical imaging data. ### Claim 19 (depends on 17) The non-transitory computer readable storage medium of claim 17, wherein the higher quality medical imaging data includes less noise, is less blurry, or includes less noise and is less blurry than the input image data. ### Claim 20 (depends on 17) The non-transitory computer readable storage medium of claim 17, wherein a rate of each incremental step of the plurality of incremental steps is controlled by a predefined parameter.