用Rust打造的多模态心脏影像融合工具，提升冠脉建模精度
multimodars: A Rust-powered toolkit for multi-modality cardiac image fusion and registration
- 基于确定性算法实现多模态影像精准对齐
- 支持静息/应激、支架前后等多状态分析，性能高效
- 适合心血管科研与临床影像开发人员使用
融合互补成像模态对构建可靠的3D冠状动脉模型至关重要：血管内成像提供亚毫米级分辨率但缺乏全血管上下文，而CCTA虽提供3D几何结构却存在空间分辨率有限及伪影（如晕影）问题。已有研究实现了血管内成像与CCTA的融合，但尚无开源、灵活的工具包支持多状态分析（静息/应激、支架前后），同时具备确定性行为、高性能和易集成的流水线特性。multimodars通过确定性配准算法、紧凑的以NumPy为中心的数据模型以及优化的Rust后端，填补了这一空白，适用于来自AIVUS-CAA软件生成的CSV/NumPy格式数据输入，支持可扩展、可复现的实验。
原文摘要 · Abstract (English)
Combining complementary imaging modalities is critical to build reliable 3D coronary models: intravascular imaging gives sub-millimetre resolution but limited whole-vessel context, while CCTA supplies 3D geometry but suffers from limited spatial resolution and artefacts (e.g., blooming). Prior work demonstrated intravascular/CCTA fusion, yet no open, flexible toolkit is tailored for multi-state analysis (rest/stress, pre-/post-stenting) while offering deterministic behaviour, high performance, and easy pipeline integration. multimodars addresses this gap with deterministic alignment algorithms, a compact NumPy-centred data model, and an optimised Rust backend suitable for scalable, reproducible experiments. The package accepts CSV/NumPy inputs including data formats produced by the AIVUS-CAA software
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