System and Method of Using DENSE Deep Networks to Predict Myocardial Strain
AssigneeUniversity of Virginia Patent Foundation
InventorsMiaomiao Zhang, Frederick H. Epstein, Pengcheng Lei, Sona Ghadimi
Life of this patent
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Abstract
A neural network predicts a sequence of myocardial motions from cardiac image data of a heart of a subject. DENSE cardiac magnetic resonance contour videos of cine data are used as input, and the output provides predicted displacement fields, supervised by the DENSE displacements. In the testing phase, DENSE data are no longer required; the trained network takes standard cine CMR bSSFP contour videos as input and predicts the final displacements.
Claims10
A computer implemented method to train a neural network to predict a sequence of myocardial motions from cardiac image data of a heart of a subject, comprising:
- using a computer with computer memory connected to a processor to execute software stored in the computer memory and executing computerized steps comprising:
- acquiring a time series of cardiac magnetic resonance (CMR) contour videos of cine data formatted according to displacement encoding with stimulated echoes (DENSE);
- processing the cine data with a rotation estimation sub-network that models rotational dynamics of a left ventricle of the heart and storing a rotation output in the computer memory;
- processing the cine data with a radial motion prediction sub-network that performs deformable image registration on the cine data and stores a registration output in the computer memory;
- applying the rotation output and the registration output to a fusion network that predicts myocardial displacements present in the cine data;
- supervising the fusion network with ground truth DENSE data to learn the myocardial displacements for diverse sets of cine data.
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