  3D motion estimation from cine cardiac magnetic resonance (CMR) images is
important for the assessment of cardiac function and diagnosis of
cardiovascular diseases. Most of the previous methods focus on estimating
pixel-/voxel-wise motion fields in the full image space, which ignore the fact
that motion estimation is mainly relevant and useful within the object of
interest, e.g., the heart. In this work, we model the heart as a 3D geometric
mesh and propose a novel deep learning-based method that can estimate 3D motion
of the heart mesh from 2D short- and long-axis CMR images. By developing a
differentiable mesh-to-image rasterizer, the method is able to leverage the
anatomical shape information from 2D multi-view CMR images for 3D motion
estimation. The differentiability of the rasterizer enables us to train the
method end-to-end. One advantage of the proposed method is that by tracking the
motion of each vertex, it is able to keep the vertex correspondence of 3D
meshes between time frames, which is important for quantitative assessment of
the cardiac function on the mesh. We evaluate the proposed method on CMR images
acquired from the UK Biobank study. Experimental results show that the proposed
method quantitatively and qualitatively outperforms both conventional and
learning-based cardiac motion tracking methods.
