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
@article{arxiv.2510.06241,
title = {multimodars: A Rust-powered toolkit for multi-modality cardiac image fusion and registration},
author = {Anselm W. Stark and Marc Ilic and Ali Mokhtari and Pooya Mohammadi Kazaj and Christoph Graeni and Isaac Shiri},
journal= {arXiv preprint arXiv:2510.06241},
year = {2025}
}