Echo4DIR: 4D Implicit Heart Reconstruction from 2D Echocardiography Videos
Yanan Liu, Qinya Li, Hao Zhang, Kangjian He, Xuan Yang, Hao Li, Dan Xu, and Lei Li

TL;DR
Echo4DIR is a novel framework that reconstructs 4D cardiac geometry from 2D echocardiography videos using implicit shape priors, domain adaptation, and physically consistent shape evolution, achieving state-of-the-art accuracy.
Contribution
It introduces a 4D implicit reconstruction method with a cardiac SDF, epipolar feature fusion, self-supervised domain adaptation, and shape evolution locking, advancing 4D cardiac imaging.
Findings
Achieves up to 98.35% Dice overlap in clinical datasets.
Outperforms existing methods in 4D cardiac mesh reconstruction.
Ensures anatomically reliable geometry at arbitrary resolutions.
Abstract
Reconstructing 4D (3D+t) cardiac geometry from sparse 2D echocardiography is highly desirable yet fundamentally challenged by geometric ambiguity and temporal discontinuity. To tackle these issues, we propose Echo4DIR, a novel test-time 4D implicit reconstruction framework. Specifically, we learn robust 3D shape priors from statistical shape models (SSMs) via a cardiac conditional SDF, constructing an Epipolar Mask Encoder module with epipolar cross attention to effectively fuse multi-view features. To bridge the synthetic-to-real domain gap, we introduce a self-supervised SDF-tailored differentiable rendering strategy for patient-specific 3D shape adaptation using uncalibrated clinical masks without requiring 3D ground truth. Crucially, the inherent continuity of implicit representation overcomes sparse observations, enabling anatomically reliable geometry at arbitrary resolutions.…
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