Synthetic Velocity Mapping Cardiac MRI Coupled with Automated Left Ventricle Segmentation

TL;DR

This paper introduces a novel frame synthesis algorithm that enhances the temporal resolution of 3D myocardial velocity mapping MRI data while simultaneously providing myocardium segmentation, addressing limitations of long acquisition times.

Contribution

The study presents an attention-based neural network architecture that jointly improves temporal resolution and segmentation accuracy in cardiac MRI analysis.

Findings

Increased temporal resolution of 3Dir MVM data.

Simultaneous myocardium segmentation during frame interpolation.

Effective handling of breathing artifacts in cardiac MRI.

Abstract

Temporal patterns of cardiac motion provide important information for cardiac disease diagnosis. This pattern could be obtained by three-directional CINE multi-slice left ventricular myocardial velocity mapping (3Dir MVM), which is a cardiac MR technique providing magnitude and phase information of the myocardial motion simultaneously. However, long acquisition time limits the usage of this technique by causing breathing artifacts, while shortening the time causes low temporal resolution and may provide an inaccurate assessment of cardiac motion. In this study, we proposed a frame synthesis algorithm to increase the temporal resolution of 3Dir MVM data. Our algorithm is featured by 1) three attention-based encoders which accept magnitude images, phase images, and myocardium segmentation masks respectively as inputs; 2) three decoders that output the interpolated frames and corresponding myocardium segmentation results; and 3) loss functions highlighting myocardium pixels. Our algorithm can not only increase the temporal resolution 3Dir MVMs, but can also generates the myocardium segmentation results at the same time.