  Estimating the shape and motion state of the myocardium is essential in
diagnosing cardiovascular diseases.However, cine magnetic resonance (CMR)
imaging is dominated by 2D slices, whose large slice spacing challenges
inter-slice shape reconstruction and motion acquisition.To address this
problem, we propose a 4D reconstruction method that decouples motion and shape,
which can predict the inter-/intra- shape and motion estimation from a given
sparse point cloud sequence obtained from limited slices. Our framework
comprises a neural motion model and an end-diastolic (ED) shape model. The
implicit ED shape model can learn a continuous boundary and encourage the
motion model to predict without the supervision of ground truth deformation,
and the motion model enables canonical input of the shape model by deforming
any point from any phase to the ED phase. Additionally, the constructed
ED-space enables pre-training of the shape model, thereby guiding the motion
model and addressing the issue of data scarcity. We propose the first 4D
myocardial dataset as we know and verify our method on the proposed, public,
and cross-modal datasets, showing superior reconstruction performance and
enabling various clinical applications.
