  3D motion estimation from cine cardiac magnetic resonance (CMR) images is
important for the assessment of cardiac function and the diagnosis of
cardiovascular diseases. Current state-of-the art methods focus on estimating
dense pixel-/voxel-wise motion fields in image space, which ignores the fact
that motion estimation is only relevant and useful within the anatomical
objects of interest, e.g., the heart. In this work, we model the heart as a 3D
mesh consisting of epi- and endocardial surfaces. We propose a novel learning
framework, DeepMesh, which propagates a template heart mesh to a subject space
and estimates the 3D motion of the heart mesh from CMR images for individual
subjects. In DeepMesh, the heart mesh of the end-diastolic frame of an
individual subject is first reconstructed from the template mesh. Mesh-based 3D
motion fields with respect to the end-diastolic frame are then estimated from
2D short- and long-axis CMR images. By developing a differentiable
mesh-to-image rasterizer, DeepMesh is able to leverage 2D shape information
from multiple anatomical views for 3D mesh reconstruction and mesh motion
estimation. The proposed method estimates vertex-wise displacement and thus
maintains vertex correspondences between time frames, which is important for
the quantitative assessment of cardiac function across different subjects and
populations. We evaluate DeepMesh on CMR images acquired from the UK Biobank.
We focus on 3D motion estimation of the left ventricle in this work.
Experimental results show that the proposed method quantitatively and
qualitatively outperforms other image-based and mesh-based cardiac motion
tracking methods.
