  Myocardial motion tracking stands as an essential clinical tool in the
prevention and detection of Cardiovascular Diseases (CVDs), the foremost cause
of death globally. However, current techniques suffer incomplete and inaccurate
motion estimation of the myocardium both in spatial and temporal dimensions,
hindering the early identification of myocardial dysfunction. In addressing
these challenges, this paper introduces the Neural Cardiac Motion Field
(NeuralCMF). NeuralCMF leverages the implicit neural representation (INR) to
model the 3D structure and the comprehensive 6D forward/backward motion of the
heart. This approach offers memory-efficient storage and continuous capability
to query the precise shape and motion of the myocardium throughout the cardiac
cycle at any specific point. Notably, NeuralCMF operates without the need for
paired datasets, and its optimization is self-supervised through the physics
knowledge priors both in space and time dimensions, ensuring compatibility with
both 2D and 3D echocardiogram video inputs. Experimental validations across
three representative datasets support the robustness and innovative nature of
the NeuralCMF, marking significant advantages over existing state-of-the-arts
in cardiac imaging and motion tracking.

Image and Video Processing;Computer Vision and Pattern Recognition