{"ID":2860836,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.06241","arxiv_id":"2510.06241","title":"multimodars: A Rust-powered toolkit for multi-modality cardiac image fusion and registration","abstract":"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","short_abstract":"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 intrava...","url_abs":"https://arxiv.org/abs/2510.06241","url_pdf":"https://arxiv.org/pdf/2510.06241v1","authors":"[\"Anselm W. Stark\",\"Marc Ilic\",\"Ali Mokhtari\",\"Pooya Mohammadi Kazaj\",\"Christoph Graeni\",\"Isaac Shiri\"]","published":"2025-10-03T08:09:35Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"physics.med-ph\"]","methods":"[]","has_code":false}