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<p>Three-dimensional (3D) structural information of cardiac vessels is crucial for the diagnosis and treatment of cardiovascular disease. In clinical practice, interventionalists have to empirically infer 3D cardiovascular topology from multi-view X-ray angiography images, which is time-consuming and requires extensive experience. Owing to the dynamic nature of heartbeats and sparse-view observations in clinical practice, accurate and efficient reconstruction of 3D cardiovascular structures from X-ray angiography images remains challenging. Here we introduce AutoCAR, a fully automated transfer learning-based algorithm for dynamic 3D cardiovascular reconstruction. AutoCAR comprises three main components: pose domain adaptation, sparse backwards projection and vascular graph optimization. By merging the X-ray angiography imaging parameter statistics of over 1,000 clinical cases into synthetic data generation, and exploiting the intrinsic spatial sparsity of cardiac vessels for computational design, AutoCAR outperforms state-of-the-art methods in both qualitative and quantitative evaluations, enabling dynamic cardiovascular reconstruction in real-world clinical settings. We envision that AutoCAR will facilitate current diagnostic and intervention procedures and pave the way for real-time visual guidance and autonomous catheter navigation in cardiac intervention.</p>

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Sparse and transferable three-dimensional dynamic vascular reconstruction for instantaneous diagnosis

  • 期刊论文

  • 发表日期：

    2025年4月21日

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  • Yinheng Zhu,
  • Yong Wang,
  • Chunxia Di,
  • Hanghang Liu,
  • Fangzhou Liao,
  • Shaohua Ma

摘要

Three-dimensional (3D) structural information of cardiac vessels is crucial for the diagnosis and treatment of cardiovascular disease. In clinical practice, interventionalists have to empirically infer 3D cardiovascular topology from multi-view X-ray angiography images, which is time-consuming and requires extensive experience. Owing to the dynamic nature of heartbeats and sparse-view observations in clinical practice, accurate and efficient reconstruction of 3D cardiovascular structures from X-ray angiography images remains challenging. Here we introduce AutoCAR, a fully automated transfer learning-based algorithm for dynamic 3D cardiovascular reconstruction. AutoCAR comprises three main components: pose domain adaptation, sparse backwards projection and vascular graph optimization. By merging the X-ray angiography imaging parameter statistics of over 1,000 clinical cases into synthetic data generation, and exploiting the intrinsic spatial sparsity of cardiac vessels for computational design, AutoCAR outperforms state-of-the-art methods in both qualitative and quantitative evaluations, enabling dynamic cardiovascular reconstruction in real-world clinical settings. We envision that AutoCAR will facilitate current diagnostic and intervention procedures and pave the way for real-time visual guidance and autonomous catheter navigation in cardiac intervention.