multimodars: A Rust-powered toolkit for multi-modality cardiac image fusion and registration
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916995846373376 |
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| author | Stark, Anselm W. Ilic, Marc Mokhtari, Ali Kazaj, Pooya Mohammadi Graeni, Christoph Shiri, Isaac |
| author_facet | Stark, Anselm W. Ilic, Marc Mokhtari, Ali Kazaj, Pooya Mohammadi Graeni, Christoph Shiri, Isaac |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06241 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | multimodars: A Rust-powered toolkit for multi-modality cardiac image fusion and registration Stark, Anselm W. Ilic, Marc Mokhtari, Ali Kazaj, Pooya Mohammadi Graeni, Christoph Shiri, Isaac Computer Vision and Pattern Recognition Medical Physics 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 |
| title | multimodars: A Rust-powered toolkit for multi-modality cardiac image fusion and registration |
| topic | Computer Vision and Pattern Recognition Medical Physics |
| url | https://arxiv.org/abs/2510.06241 |