Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation

TL;DR

This paper introduces Motion Pyramid Networks, a deep learning approach that combines multi-scale motion fields and a cyclic teacher-student training strategy to improve the accuracy and efficiency of cardiac motion estimation in MRI.

Contribution

It proposes a novel pyramid-based motion estimation framework with cyclic teacher-student training, enhancing accuracy and inference speed for cardiac MRI analysis.

Findings

Outperforms baseline models on clinical datasets

Significantly improves motion estimation accuracy

Reduces inference time for practical use

Abstract

Cardiac motion estimation plays a key role in MRI cardiac feature tracking and function assessment such as myocardium strain. In this paper, we propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac motion estimation. We predict and fuse a pyramid of motion fields from multiple scales of feature representations to generate a more refined motion field. We then use a novel cyclic teacher-student training strategy to make the inference end-to-end and further improve the tracking performance. Our teacher model provides more accurate motion estimation as supervision through progressive motion compensations. Our student model learns from the teacher model to estimate motion in a single step while maintaining accuracy. The teacher-student knowledge distillation is performed in a cyclic way for a further performance boost. Our proposed method outperforms a strong baseline model on two public available clinical datasets significantly, evaluated by a variety of metrics and the inference time. New evaluation metrics are also proposed to represent errors in a clinically meaningful manner.