STCMT-Net: A spatiotemporal consistency motion transfer network for enhancing cardiac motion estimation

Cardiac motion estimation is critical for assessing cardiac function and understanding cardiac mechanics. However, the complex and subject-specific characteristics of cardiac motion pose substantial challenges for modeling the spatiotemporal dynamics of the heart from four-dimensional (4D) cardiac images. Existing methods often exhibit limited capacity for spatial representation of cardiac anatomy and inadequate modeling of inter-structural correlations across images, resulting in inaccurate motion estimation throughout the cardiac cycle. To address these challenges, we propose an unsupervised Spatiotemporal Consistency Motion Transfer Network (STCMT-Net) to enhance motion estimation in 4D cardiac images. Cardiac motion is explicitly modeled in a set of keypoints, which are unsupervisedly extracted from continuous images via a keypoint detector. The detected keypoints encode essential motion patterns and serve as structural anchors tightly coupled with the dense motion estimation. The keypoint extraction and matching process are regularized by constraints that ensure spatial distribution uniformity and preserve temporal local anatomical correspondence around keypoints across images. For dense motion reconstruction, the cardiac motion field is formulated as a weighted combination of basis motion vectors derived from paired keypoints. A dense motion estimation module predicts the combination coefficients and incorporates residual refinements to capture both global and fine-grained motions with improved accuracy. We validated the proposed method on 4D computed tomography and cine magnetic resonance imaging datasets. Cardiac strain analysis further demonstrated the effectiveness of STCMT-Net in distinguishing patients under different cardiac conditions, highlighting its potential for clinical application.