# US 2024/0202995 A1 · Systems and methods for reconstructing cardiac images > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2024/0202995 A1 - Document ID: US20240202995A1 - Status: Published application - Kind: A1 - Application number: 18/587,896 - Filed: 2024-02-26 - Published: 2024-06-20 - Earliest claimed: 2018-06-11 - Assignees: Shanghai United Imaging Healthcare Co., Ltd. - Applicants: Shanghai United Imaging Healthcare Co., Ltd. - Inventors: Yi WANG - Web page: https://patentgrep.com/patent/US20240202995A1 ## CPC - G06T 12/30: Image post-processing, e.g. metal artefact correction - A61B 5/489: {Blood vessels} - A61B 6/032: {Transmission computed tomography [CT]} - A61B 6/503: {for diagnosis of the heart} - A61B 6/504: {for diagnosis of blood vessels, e.g. by angiography} - G06T 5/20: using local operators - G06T 5/50: using two or more images, e.g. averaging or subtraction - G06T 5/73: Deblurring; Sharpening - G06T 7/0014: {using an image reference approach} - G06T 7/11: Region-based segmentation - G06T 7/90: Determination of colour characteristics - G06T 12/10: Image preprocessing, e.g. calibration, positioning of sources or scatter correction - G06T 12/20: Inverse problem, i.e. transformations from projection space into object space - A61B 6/481: {involving the use of contrast agents} - A61B 6/541: {involving acquisition triggered by a physiological signal} - G06T 2207/10072: Tomographic images - G06T 2207/20201: Motion blur correction - G06T 2207/30048: Heart; Cardiac - G06T 2207/30101: Blood vessel; Artery; Vein; Vascular - G06T 2207/30168: Image quality inspection - G06T 2211/404: Angiography - G06T 2211/412: Dynamic - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) ## Abstract A method for reconstructing target cardiac images is provided. The method may include: obtaining a plurality of projection data corresponding to a plurality of cardiac motion phases; determining a plurality of cardiac motion parameters corresponding to at least a portion of the plurality of cardiac motion phases based on the plurality of projection data; determining a phase of interest based on the plurality of cardiac motion parameters; and/or reconstructing the one or more target cardiac images of the phase of interest. ## Claims ### Claim at position 1 1-20. (canceled) ### Claim 21 A system, comprising: at least one storage device storing executable instructions, and at least one processor in communication with the at least one storage device, when executing the executable instructions, causing the system to perform operations including: obtaining projection data of a subject; determining one or more adaptive reconstruction parameters associated with personalized data of the subject based on the projection data; and reconstructing, according to the one or more adaptive reconstruction parameters, one or more target images of a target subject based on at least a portion of the projection data. ### Claim 22 (depends on 21) The system of claim 21, wherein the determining one or more adaptive reconstruction parameters based on the projection data includes: determining the one or more adaptive reconstruction parameters by inputting the projection data into a first trained machine learning model. ### Claim 23 (depends on 22) The system of claim 22, wherein the first trained machine learning model is obtained by: inputting sample projection data into a machine learning model; generating one or more estimated adaptive reconstruction parameters using the machine learning model to process the sample projection data; and adjusting parameters of the machine learning model based on the one or more estimated adaptive reconstruction parameters and one or more reference adaptive reconstruction parameters. ### Claim 24 (depends on 21) The system of claim 21, wherein the determining one or more adaptive reconstruction parameters based on the projection data includes: reconstructing at least one preview image based on the projection data; and determining the one or more adaptive reconstruction parameters based on the at least one preview image. ### Claim 25 (depends on 24) The system of claim 24, wherein the determining the one or more adaptive reconstruction parameters based on the at least one preview image includes: determining the one or more adaptive reconstruction parameters by inputting the at least one preview image into a second trained machine learning model. ### Claim 26 (depends on 25) The system of claim 25, wherein the second trained machine learning model is obtained by: inputting a sample image into a machine learning model; generating one or more estimated adaptive reconstruction parameters using the machine learning model to process the sample image; and adjusting parameters of the machine learning model based on the one or more estimated adaptive reconstruction parameters and one or more reference adaptive reconstruction parameters. ### Claim 27 (depends on 25) The system of claim 25, wherein the determining the one or more adaptive reconstruction parameters based on the at least one preview image includes: determining personalized data of the subject based on the at least one preview image; and determining the one or more adaptive reconstruction parameters based on the personalized data of the subject. ### Claim 28 (depends on 24) The system of claim 24, wherein the at least one preview image reflects a structure of at least a portion of thoracic cavity of the target subject. ### Claim 29 (depends on 24) The system of claim 24, wherein a field of view (FOV) for reconstructing the one or more target images is smaller than an FOV for reconstructing the at least one preview image. ### Claim 30 (depends on 21) The system of claim 21, wherein the reconstructing, according to the one or more adaptive reconstruction parameters, one or more target images of a target subject based on at least a portion of the projection data includes: reconstructing multiple candidate images of the subject based on the projection data corresponding to a plurality of motion phases and the one or more adaptive reconstruction parameters; determining a phase of interest from the plurality of motion phases based on the multiple candidate images; and reconstructing the one or more target images of the target subject in the phase of interest based on at least a portion of the projection data. ### Claim 31 (depends on 30) The system of claim 30, wherein the determining a phase of interest based on the multiple candidate images includes: determining the phase of interest based on the multiple candidate images by inputting the multiple candidate images into a trained machine learning model. ### Claim 32 (depends on 30) The system of claim 30, wherein the determining a phase of interest based on the multiple candidate images includes: determining one or more motion parameters corresponding to each of the plurality of motion phases based on the multiple candidate images; and determining the phase of interest based on the one or more motion parameters. ### Claim 33 (depends on 30) The system of claim 30, wherein the one or more target images are reconstructed based on one or more target adaptive reconstruction parameters, the one or more target adaptive reconstruction parameters include a first portion and a second portion, a target adaptive reconstruction parameter in the first portion is same as one of the one or more adaptive reconstruction parameter and a target adaptive reconstruction parameter in the second portion is different from each of the one or more adaptive reconstruction parameters. ### Claim 34 (depends on 21) The system of claim 21, wherein the one or more adaptive reconstruction parameters include at least one of an FOV, a reconstruction center, a thickness of a reconstruction slice, a phase of interest, or a reconstruction kernel. ### Claim 35 (depends on 21) The system of claim 21, wherein the projection data includes a plurality of sets of projection data each of which corresponds to one of a plurality of motion phase, and the one or more reconstruction parameters include a plurality of sets of reconstruction parameters, each of the plurality of sets of reconstruction parameters is determined based on one of the plurality of sets of projection data, and each of the one or more target images is reconstructed based on one of the plurality of sets of reconstruction parameters and one of the plurality of sets of projection data. ### Claim 36 (depends on 24) The system of claim 24, wherein the determining the one or more adaptive reconstruction parameters based on at least one preview image includes: obtaining a contour image by performing a maximum intensity projection on the at least one preview image; and determining a reconstruction center based on the contour image. ### Claim 37 (depends on 36) The system of claim 36, wherein the determining the reconstruction center based on the contour image includes: determining one or more positions of a contour boundary in the contour image; and determining the reconstruction center based on the one or more positions of the contour boundary. ### Claim 38 (depends on 21) The system of claim 21, wherein the personalized data indicates whether the subject includes an abnormal portion, and the one or more adaptive reconstruction parameters include a reconstruction kernel adapted to the abnormal portion in response to determining the subject includes the abnormal portion, and the reconstructing, according to the one or more adaptive reconstruction parameters, one or more target images of a target subject based on at least a portion of the projection data includes: processing the one or more target images of the target subject by using the reconstruction kernel to perform a calibration on an artifact caused by the abnormal portion. ### Claim 39 A method implemented on a computing device having one or more processors and a computer-readable storage medium, the method comprising: obtaining projection data of a subject; determining one or more adaptive reconstruction parameters associated with personalized data of the subject based on the projection data; and reconstructing, according to the one or more adaptive reconstruction parameters, one or more target images of a target subject based on at least a portion of the projection data. ### Claim 40 A non-transitory computer readable medium storing instructions, the instructions, when executed by at least one processor, causing the at least one processor to implement a method comprising: obtaining projection data of a subject; determining one or more adaptive reconstruction parameters associated with personalized data of the subject based on the projection data; and reconstructing, according to the one or more adaptive reconstruction parameters, one or more target images of a target subject based on at least a portion of the projection data.