Methods and Systems for Intramyocardial Tissue Displacement and Motion Measurement

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.

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