# US 2024/0197262 A1 · Methods and Systems for Intramyocardial Tissue Displacement and Motion Measurement > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2024/0197262 A1 - Document ID: US20240197262A1 - Status: Published application - Kind: A1 - Application number: 18/472,215 - Filed: 2023-09-21 - Published: 2024-06-20 - Earliest claimed: 2022-09-21 - Assignees: University of Virginia Patent Foundation - Applicants: University of Virginia Patent Foundation - Inventors: Frederick H. Epstein; Yu Wang; Changyu Sun - Web page: https://patentgrep.com/patent/US20240197262A1 ## CPC - A61B 5/7267: {involving training the classification device} - G06V 10/44: Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract An exemplary method and system are disclosed that employ deep learning neural-network(s) trained with displacement-encoded imaging data (i.e., DENSE data) to estimate intramyocardial motion from cine MRI images retrieved with balanced steady state free precession sequences (bFSSP) and other cardiac medical imaging modalities, including standard cardiac computer tomography (CT) images, magnetic resonance imaging (MRI) images, echocardiogram images, heart ultrasound images, among other medical imaging modalities described herein. The deep learning neural-network(s) can be trained using (i) contour motion data from displacement-encoded imaging magnitude data as inputs to the neural network and (ii) displacement maps derived from displacement-encoded imaging phase images for comparison to the outputs of the neural network for neural network adjustments during the training. The DENSE trained neural network can be used to calculate tissue displacement from bFSSP cine images. ## Claims ### Claim 1 A computer implemented method of measuring intramyocardial tissue displacement with image data, the method comprising retrieving a medical image of a subject with a magnetic resonance imaging (MRI) system, having at least one processor, wherein the MRI system retrieves the medical image with a balanced steady-state free precession (bSSFP) pulse sequence; and determining, by the processor, intramyocardial tissue displacement within the medical image by using a neural network, wherein the neural network has been trained with training data calculated from Displacement-ENcoding with Stimulated Echoes (DENSE) image data. ### Claim 2 (depends on 1) The computer implemented method of claim 1, wherein training the neural network comprises using a training computer to perform computerized steps comprising: (vi) generating a set of contour motion image data from magnitude data from the DENSE image data; (vii) using the neural network to calculate estimated displacement image data from the contour motion image data; (viii) generating a set of ground truth displacement image data with phase data from the DENSE image data; (ix) calculating error data by comparing the estimated displacement image data with the ground truth displacement image data; and (x) updating parameters of the neural network with the error data. ### Claim 3 (depends on 1) The computer implemented method of claim 1, wherein retrieving a medical image of a subject comprises retrieving a two dimensional (2D) bSSFP medical image of the subject. ### Claim 4 (depends on 1) The computer implemented method of claim 1, wherein retrieving the medical image comprises retrieving cine image data with the MRI system using the bSSFP pulse. ### Claim 5 (depends on 4) The computer implemented method of claim 4, further comprising using the processor to generate frames of test image magnitude data from the cine image data and segment the test image magnitude data to track pixels of the medical image corresponding to endocardial contours and epicardial contours of a myocardium of the subject. ### Claim 6 (depends on 5) The computer implemented method of claim 5, further comprising applying morphological dilation to a bSSFP binary mask to binarize segmented test image magnitude data to generate test contour motion data for the medical image. ### Claim 7 (depends on 6) The computer implemented method of claim 6, further comprising under sampling the segmented test image magnitude data to generate the test contour motion data with spatial resolution matching between the DENSE image data and the test contour motion data. ### Claim 8 (depends on 6) The computer implemented method of claim 6, further comprising using the test contour motion data as an input to the neural network to calculate intramyocardial tissue displacement from the cine image data. ### Claim 9 (depends on 1) The computer implemented method of claim 1, further comprising using a convolutional neural network as the neural network. ### Claim 10 (depends on 9) The computer implemented method of claim 9, further comprising using a 3D U-Net neural network as the convolutional neural network. ### Claim 11 A system comprising: a processor; and a memory having instructions stored thereon to calculate intramyocardial displacement in medical image scans, wherein execution of the instructions by the processor causes the processor to: retrieve a medical image of a subject with a magnetic resonance imaging (MRI) system in communication with the processor, wherein the MRI system retrieves the medical image with a balanced steady-state free precession (bSSFP) pulse sequence; and determining, by the processor, intramyocardial tissue displacement within the medical image by using a neural network, wherein the neural network has been trained with training data calculated from Displacement-ENcoding with Stimulated Echoes (DENSE) image data. ### Claim 12 (depends on 12) The system of claim 12, wherein the instructions in the memory implement a neural network to calculate the intramyocardial displacement. ### Claim 13 (depends on 12) The system of claim 12, wherein the neural network is a previously trained neural network trained by a training computer to perform computerized steps comprising: (i) generating a set of contour motion image data from magnitude data from the DENSE image data; (ii) using the neural network to calculate estimated displacement image data from the contour motion image data; (iii) generating a set of ground truth displacement image data with phase data from the DENSE image data; (iv) calculating error data by comparing the estimated displacement image data with the ground truth displacement image data; and (v) updating parameters of the neural network with the error data. ### Claim 14 A non-transitory computer readable medium having instructions stored thereon to calculate intramyocardial displacement in medical image scans, wherein execution of the instructions by a computer with a processor causes the computer to: retrieve a medical image of a subject with a magnetic resonance imaging (MRI) system in communication with the processor, wherein the MRI system retrieves the medical image with a balanced steady-state free precession (bSSFP) pulse sequence; and determining, by the processor, intramyocardial tissue displacement within the medical image by using a neural network, wherein the neural network has been trained with training data calculated from Displacement-ENcoding with Stimulated Echoes (DENSE) image data.