# US 12,602,854 B2 · System and method for medical imaging > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,602,854 B2 - Document ID: US12602854B2 - Status: Granted - Kind: B2 - Application number: 18/504,311 - Filed: 2023-11-08 - Published: 2024-06-06 - Granted: 2026-04-14 - Earliest claimed: 2022-12-06 - Assignees: Body Vision Medical Ltd. - Applicants: Body Vision Medical Ltd. - Inventors: Dima Sezganov; Dorian Averbuch - Web page: https://patentgrep.com/patent/US12602854B2 ## CPC - G06T 12/30: Image post-processing, e.g. metal artefact correction - A61B 6/4441: {the rigid structure being a C-arm or U-arm} - A61B 6/5205: {involving processing of raw data to produce diagnostic data} - G06T 5/50: using two or more images, e.g. averaging or subtraction - G06T 5/60: using machine learning, e.g. neural networks - G06T 5/70: Denoising; Smoothing - G06T 7/0012: {Biomedical image inspection} - G06T 7/73: using feature-based methods - G06T 12/20: Inverse problem, i.e. transformations from projection space into object space - A61B 6/547: {involving tracking of position of the device or parts of the device} - G06T 2207/10064: Fluorescence image - G06T 2207/10081: Computed x-ray tomography [CT] - G06T 2207/10121: Fluoroscopy - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30061: Lung - G06T 2207/30096: Tumor; Lesion - G06T 2207/30204: Marker - G06T 2210/41: Medical - G06T 2211/421: Filtered back projection [FBP] - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) ## Abstract A method including receiving, from a C-arm device, a plurality of fluoroscopic images of a lung, wherein each fluoroscopic image is obtained with the C-arm device positioned at a particular pose of a plurality of poses traversed by the C-arm device while the C-arm device is moved through a range of motion including a range of rotation, the range of rotation encompassing a sweep angle between 45 degrees and 120 degrees; generating an enhanced tomographic image of the lung, by utilizing: a trained machine learning model and the plurality of fluoroscopic images; and outputting a representation of the enhanced tomographic image, wherein, when tested by a method in which: the lung includes a lesion smaller than 30 millimeters, and the representation is an axial slice showing a boundary of the lesion, the lesion has a contrast-to-noise value of at least 5 as compared to a background of the representation. ## Claims ### Claim 1 A method, comprising: a) receiving, by a controller unit, from a C-arm device, a plurality of fluoroscopic images of at least one portion of a lung of a patient, wherein each image of the plurality of fluoroscopic images is obtained with the C-arm device positioned at a particular pose of a plurality of poses traversed by the C-arm device while the C-arm device is moved through a range of motion, wherein the range of motion comprises at least a range of rotation, and wherein the range of rotation encompasses a sweep angle that is between 45 degrees and 75 degrees; b) generating, by the controller unit, an enhanced tomographic image of the at least a portion of the lung, by utilizing at least: a trained machine learning model and the plurality of fluoroscopic images; and c) outputting, by the controller unit, a representation of the enhanced tomographic image, wherein, when tested by a test method in which: (a) the at least one portion of the lung of the patient includes at least one lesion having a size of less than 30 millimeters, and (b) the representation of the enhanced tomographic image is an axial slice showing a defined boundary of the at least one lesion, the at least one lesion has a contrast-to-noise value of at least 5 as compared to a background of the representation. ### Claim 2 (depends on 1) The method of claim 1, wherein the step of generating the enhanced tomographic image comprises: reconstructing a tomographic image based on the plurality of fluoroscopic images; and enhancing the tomographic image using the trained machine learning model to generate the enhanced tomographic image. ### Claim 3 (depends on 2) The method of claim 2, wherein the step of reconstructing the tomographic image based on the plurality of fluoroscopic images comprises reconstructing the tomographic image using filtered back projection. ### Claim 4 (depends on 2) The method of claim 2, wherein the step of reconstructing the tomographic image based on the plurality of fluoroscopic images comprises determining a pose of each of the plurality of fluoroscopic images. ### Claim 5 (depends on 4) The method of claim 4, wherein the determining the pose of each of the plurality of fluoroscopic images comprises image-based pose estimation. ### Claim 6 (depends on 5) The method of claim 5, wherein the image-based pose estimation comprises recognition of at least one of an anatomical feature or a radiopaque marker. ### Claim 7 (depends on 1) The method of claim 1, wherein the sweep angle is between 60 degrees and 75 degrees. ### Claim 8 (depends on 1) The method of claim 1, wherein, when tested by a test method in which: (a) the at least one portion of the lung of the patient includes at least one lesion having a size of less than 10 millimeters, and (b) the representation of the enhanced tomographic image is an axial slice showing a defined boundary of the at least one lesion, the at least one lesion has a contrast-to-noise value of at least 5 as compared to a background of the representation. ### Claim 9 (depends on 1) The method of claim 1, wherein the trained machine learning model comprises a gradient descent machine learning model. ### Claim 10 (depends on 1) The method of claim 1, wherein the range of motion further comprises a range of translational motion. ### Claim 11 (depends on 1) The method of claim 1, wherein the sweep angle is between 45 degrees and 60 degrees.