UltraTwin: Towards Cardiac Anatomical Twin Generation from Multi-view 2D Ultrasound
This work addresses the need for precise treatment planning and clinical quantification in cardiac care by enabling 3D modeling from widely available 2D ultrasound, though it is incremental in improving reconstruction methods.
The study tackled the problem of generating 3D cardiac anatomical twins from sparse multi-view 2D ultrasound images, which is challenging due to rare paired data and noise, and achieved high-quality reconstructions compared to strong competitors.
Echocardiography is routine for cardiac examination. However, 2D ultrasound (US) struggles with accurate metric calculation and direct observation of 3D cardiac structures. Moreover, 3D US is limited by low resolution, small field of view and scarce availability in practice. Constructing the cardiac anatomical twin from 2D images is promising to provide precise treatment planning and clinical quantification. However, it remains challenging due to the rare paired data, complex structures, and US noises. In this study, we introduce a novel generative framework UltraTwin, to obtain cardiac anatomical twin from sparse multi-view 2D US. Our contribution is three-fold. First, pioneered the construction of a real-world and high-quality dataset containing strictly paired multi-view 2D US and CT, and pseudo-paired data. Second, we propose a coarse-to-fine scheme to achieve hierarchical reconstruction optimization. Last, we introduce an implicit autoencoder for topology-aware constraints. Extensive experiments show that UltraTwin reconstructs high-quality anatomical twins versus strong competitors. We believe it advances anatomical twin modeling for potential applications in personalized cardiac care.