We present a novel approach to reconstruction of 3D cardiac motion from sparse intraoperative data. While existing methods can accurately reconstruct 3D organ geometries from full 3D volumetric imaging, they cannot be used during surgical interventions where usually limited observed data, such as a few 2D frames or 1D signals, is available in real-time. We propose a versatile framework for reconstructing 3D motion from such partial data. It discretizes the 3D space into a deformable tetrahedral grid with signed distance values, providing implicit unlimited resolution while maintaining explicit control over motion dynamics. Given an initial 3D model reconstructed from pre-operative full volumetric data, our system, equipped with an universal observation encoder, can reconstruct coherent 3D cardiac motion from full 3D volumes, a few 2D MRI slices or even 1D signals. Extensive experiments on cardiac intervention scenarios demonstrate our ability to generate plausible and anatomically consistent 3D motion reconstructions from various sparse real-time observations, highlighting its potential for multimodal cardiac imaging. Our code and model will be made available at https://github.com/Scalsol/MedTet.

翻译：本文提出了一种从稀疏术中数据重建三维心脏运动的新方法。现有方法虽能从完整三维容积成像中精确重建三维器官几何结构，但无法应用于通常仅能实时获取有限观测数据（如少量二维帧或一维信号）的外科手术场景。我们提出了一种通用框架，可从此类部分数据中重建三维运动。该框架将三维空间离散化为带符号距离值的可变形四面体网格，在保持对运动动力学显式控制的同时提供隐式无限分辨率。给定通过术前完整容积数据重建的初始三维模型，本系统配备通用观测编码器，能够从完整三维容积、少量二维MRI切片甚至一维信号中重建连贯的三维心脏运动。在心脏介入场景中的大量实验表明，本方法能从各类稀疏实时观测中生成合理且解剖学一致的三维运动重建，凸显了其在多模态心脏成像中的应用潜力。代码与模型将在 https://github.com/Scalsol/MedTet 公开。