从多视角超声图生成心脏解剖孪生模型，助力精准诊疗
UltraTwin: Towards Cardiac Anatomical Twin Generation from Multi-view 2D Ultrasound
- 分阶段优化重建，先粗后细提升结构精度
- 在真实配对数据上实现高保真心脏三维重建
- 适合心血管影像与个性化医疗研究者参考
超声心动图是心脏检查的常规手段，但二维超声难以精确测量和直观观察三维心脏结构。三维超声受限于分辨率低、视野小且临床可用性差。从二维图像构建心脏解剖孪生体具有潜力，可支持精准治疗规划与临床量化分析。然而，受限于配对数据稀少、结构复杂及超声噪声，仍具挑战。本文提出新型生成框架UltraTwin，从稀疏多视角二维超声图中重建心脏解剖孪生体。贡献有三：其一，构建首个真实世界高质量数据集，包含严格配对的多视角二维超声与CT数据，以及伪配对数据；其二，提出粗到精的分层重建优化策略；其三，引入隐式自编码器实现拓扑感知约束。大量实验表明，UltraTwin在多个指标上优于现有方法，能生成高质量解剖孪生体。该工作推动了解剖孪生建模发展，为个性化心脏医疗应用奠定基础。
原文摘要 · Abstract (English)
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.
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