multimodars

"One package to fuse them all."

— The Lord of the Rings (probably)

A high‑performance, Rust‑accelerated toolkit for multi‑modality cardiac image fusion and registration ﮩ٨ـﮩﮩ٨ـ♡ﮩ٨ـﮩﮩ٨ـ.

Overview

multimoda-rs aligns and fuses diverse cardiac imaging modalities — IVUS, OCT and CCTA — into unified high-resolution 3D models. Originally developed to quantify dynamic lumen deformation in coronary artery anomalies (CAAs), it is equally applicable to longitudinal studies (e.g., pre/post-stenting) and general coronary artery disease workflows. The Rust backend parallelizes computationally intensive registration steps for speeds well beyond pure Python.

Key Features

Intravascular Registration: align pullback sequences (rest/stress, diastole/systole) using Hausdorff distance on vessel contours and catheter centroids; four modes: full, double-pair, single-pair, single.
Centerline Alignment: register intravascular geometries onto a CCTA-derived centerline via three-point landmark or manual rotation.
CCTA Fusion: automatically label CCTA geometries by vessel region and morph them to match intravascular measurements.
Flexible Input: accepts CSV files (AIVUS format) or raw NumPy arrays.

Installation

pip install multimodars

Optional extras:

pip install "multimodars[viz]"      # mesh visualisation (pyglet)
pip install "multimodars[meshlab]"  # MeshLab integration

For building from source or the full developer setup (tests, linting, docs), see the Installation guide.

Quick Example

Checkout our Documentation for in-depth explanations of the functionality and preloaded notebooks including 3D plots. Otherwise clone the repo to follow along the different examples:

import multimodars as mm
import numpy as np

# Align four intravascular phases (rest-dia, rest-sys, stress-dia, stress-sys)
rest, stress, dia, sys, _ = mm.from_file_full(
    "examples/data/ivus_rest",    # input_path_ab
    "examples/data/ivus_stress",  # input_path_cd
    write_obj=True,
    output_path_ab="output/rest",
    output_path_cd="output/stress",
    output_path_ac="output/diastole",
    output_path_bd="output/systole",
)

# Align geometry onto a CCTA-derived centerline
cl_raw = np.genfromtxt("examples/data/centerline_raw.csv", delimiter=",")
centerline = mm.numpy_to_centerline(cl_raw)
aligned_pair, cl_resampled, total_rotation_deg = mm.align_three_point(
    centerline, rest,
    main_ref_pt=(12.2605, -201.3643, 1751.0554),
    counterclockwise_ref_pt=(11.7567, -202.1920, 1754.7975),
    clockwise_ref_pt=(15.6605, -202.1920, 1749.9655),
    write=True,
    output_dir="output/aligned",
)

Pipeline

1. Intrapullback alignment — frames within each pullback are co-registered to remove cardiac-motion artefacts, yielding clean diastolic and systolic geometries:

Dynamic lumen changes

2. Inter-pullback alignment — registered pullbacks (rest vs. stress, diastole vs. systole) are aligned against each other to reveal stress-induced and pulsatile deformation:

Stress-induced diastolic lumen deformation

3. CCTA labeling — the CCTA-derived geometry is automatically segmented by vessel region (aorta, RCA, LCA, intramural) to prepare it for fusion:

Initial CCTA labeling

4. CCTA–morphing — the labeled CCTA geometry is morphed along the centerline to match the high-resolution intravascular geometry:

CCTA scaling

5. CCTA/intravascular fusio — the morphed CCTA geometry is stitched to the intravascular geometry, replacing a section with a high resolution verison:

CCTA fusion

Documentation

Full documentation — installation, step-by-step tutorials, interactive (preloaded) Jupyter notebooks, and API reference — is available at multimoda-rs.readthedocs.io.

License

Distributed under the MIT License. See LICENSE for details.

Citation

Please kindly cite the following paper if you use this repository.

@article{Stark2026, 
  doi = {10.21105/joss.10131}, 
  url = {https://doi.org/10.21105/joss.10131}, 
  year = {2026}, 
  publisher = {The Open Journal}, 
  volume = {11}, 
  number = {121}, 
  pages = {10131}, 
  author = {Stark, Anselm W. and Ilic, Marc and Mokhtari, Ali and Kazaj, Pooya Mohammadi and Gräni, Christoph and Shiri, Isaac}, 
  title = {multimodars: A Rust-powered toolkit for multi-modality cardiac image fusion and registration}, 
  journal = {Journal of Open Source Software} 
}

Stark et al., (2026). multimodars: A Rust-powered toolkit for multi-modality cardiac image fusion and registration. Journal of Open Source Software, 11(121), 10131, https://doi.org/10.21105/joss.10131.

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Algorithm	Hash digest
SHA256	`5d8d9af6e1563d6a45f149bb3798ba53ffa920ca22c8055b4316dec58d7ede5f`
MD5	`27666d762ba1a504a891e37b26eaf551`
BLAKE2b-256	`ede07ef10aa14b54c6af0c197777351025eff5dc72ea5cd9c4b351f5249f5bfd`

multimodars 0.5.6

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1 maintainer

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Algorithm	Hash digest
SHA256	`bd6528f1405e184a75bacef95e38de0495c5dbc7eb479884cf1cad7d5231e201`
MD5	`f763504866ae76d6e8eb0b410713d69c`
BLAKE2b-256	`32663ce34a4b533544d7294fec1aadca87758de784604086600bd7698e268186`

Algorithm	Hash digest
SHA256	`0324a7c9c5db175db11bd009cde35dcb94b7367bd2e55f5a124a9e4c34cdec0d`
MD5	`cfa36b437814e1a5348d77302b364a3a`
BLAKE2b-256	`825a817dbdf2227be010574400bda1b7a6fe7422a066f799f6a8273911b2222f`

Algorithm	Hash digest
SHA256	`cebf6128d562f9ed6fcbe05932fff3d8566b829864f59ebc4071a55d70bd3d58`
MD5	`6e6929a23e4ae4c714f7a81c1f3d4b05`
BLAKE2b-256	`066be53aebed4bfb5576bd06ecb38ed51033ea6c5bb8a9bb15c5d3b2b5bdaf9e`

Algorithm	Hash digest
SHA256	`41bb7ac548271831826dfd8c096442d8556095b70f49a92751090dda7344db2f`
MD5	`b20b31402111902284ccff5bf324c167`
BLAKE2b-256	`53984268c16615e7f040797a6b82860fb4d908de500a8506f41d225f8d1732cd`

Algorithm	Hash digest
SHA256	`ec065de65b4f78b8437b23a659e954f64006351202a724f306f02e19199cc749`
MD5	`1bb80a7d0bddc09faaebb4064c0136d4`
BLAKE2b-256	`1aebaa1fa1da91e564887912cf58a94159c93f437820982f2f9b098f03f72f25`

Algorithm	Hash digest
SHA256	`5e6962e033ff8338cc233ac6c8ce78b70b4c7714880be3e4f0a39135c1e7bf3b`
MD5	`1208b6005a0337c27be41984547b89b8`
BLAKE2b-256	`18a8a3e837bebbe9958ed76f7a171d293e34800f6df99208e92d893fa9ffff65`

Algorithm	Hash digest
SHA256	`8afcd20b0eba17fec59c36df1ec930c53f648a52b8aeb46a8fabd8174ab352cd`
MD5	`413e008cd452be47006f7b669acf0272`
BLAKE2b-256	`2eb19a94aaa44780b2a4a64dd36f9c4535fec4df925d28cb252af41496403a80`

Algorithm	Hash digest
SHA256	`d20dca1b2bb2a0d5dddd8ec10ca1e195743f26aead01a8d42af0b6083645415d`
MD5	`29e4de9c8436a73fc62688fd2d2abcd5`
BLAKE2b-256	`97c18f3e50f141348492c6c9bbaf87e5a152b1950493e4c1d00873e74a32dfbb`

Algorithm	Hash digest
SHA256	`81bbf4cd9ab3d9c86a48ea029100924fffc5a500643a4151bfc4322f08fb09aa`
MD5	`261b8ab0c2a4f2d37459de080513ad77`
BLAKE2b-256	`1d17c83b3b5c966814be7416490caee74e07d4f645a18770f2c0d886f8579977`

Algorithm	Hash digest
SHA256	`6921ebf1d524e6ef767907a40c72be91a2fa1ca3a0793da4fc74a8cc67865072`
MD5	`b742ae223d3b30656e4d03735ad11088`
BLAKE2b-256	`e4a5092de3a8c9a545ee46670787308a14ea2fd6f1453af8c4f7733fcff3a067`