MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI
This work addresses the problem of 3D cardiac motion tracking for cardiovascular disease assessment, representing an incremental improvement over existing methods by incorporating multi-view data and shape regularization.
The paper tackled the challenge of accurately estimating 3D myocardial motion from 2D cardiac MRI slices by proposing MulViMotion, a multi-view network that integrates short-axis and long-axis images to learn consistent 3D motion fields, and it outperformed competing methods on a dataset of 580 subjects from the UK Biobank study.
Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estimation is challenging because the acquired cine CMR images are usually 2D slices which limit the accurate estimation of through-plane motion. To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion field of the heart. In the proposed method, a hybrid 2D/3D network is built to generate dense 3D motion fields by learning fused representations from multi-view images. To ensure that the motion estimation is consistent in 3D, a shape regularization module is introduced during training, where shape information from multi-view images is exploited to provide weak supervision to 3D motion estimation. We extensively evaluate the proposed method on 2D cine CMR images from 580 subjects of the UK Biobank study for 3D motion tracking of the left ventricular myocardium. Experimental results show that the proposed method quantitatively and qualitatively outperforms competing methods.