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 = 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	`65b63729fdb06d252ef696eb323c11c9829bd5b9f41da9ad87f21c252d2b2081`
MD5	`d901c8fa92bd52e686c5ea1905689427`
BLAKE2b-256	`af123d1a522f864c4d22da5fc196a94fa4799ab222e5546cce7f2d6e7685e1c3`

multimodars 0.5.1

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1 maintainer

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Algorithm	Hash digest
SHA256	`a525f2ac5b997814214a894c18403e3d3bd90dcbeb36dc8c3ae143754f9bcda0`
MD5	`c123cf2525ca44704364dbf2fd3dce52`
BLAKE2b-256	`3fa4af3d508a0d5eb9d4458d23865ddbe6e35784a59a51a6ac620c88f01da3fc`

Algorithm	Hash digest
SHA256	`2a7302d2a24de3eae51538140b1711e08befc606e2f13419809f31c827486c20`
MD5	`1cb6eb31afe1b83c501002088f3476fe`
BLAKE2b-256	`125f2a34b543b8b2f9aed50eefe86c51fae8fbc2588160e195d66bbae4c93607`

Algorithm	Hash digest
SHA256	`fae792eac0b40fe8d26488af2361f3ebf7755cb9b9fe687fba6fbdcb8a072316`
MD5	`64067fcfbb99899b74c734eb63622602`
BLAKE2b-256	`ad5fe123ec8852d6a7f390d72bd8dbde96e834115b2179117bced6fe9fdf7c5b`

Algorithm	Hash digest
SHA256	`6530984ea834671d42f6cbbf9fd378a9e0c78945ce59b19ae89fa3a116880797`
MD5	`30414015e2477818c260aa593c8315bb`
BLAKE2b-256	`fd6a504ea02450dc802593ef196655941c23bf875f6137123f1717abddabefb6`

Algorithm	Hash digest
SHA256	`2548fea6b15ec198d7c987546a087436374a0b21971840751ed4d215285e55dc`
MD5	`5d29ba4d66491c9718ebdbfce391d959`
BLAKE2b-256	`fcaf783554743c9309c1187caf4d7bc67c82df7fb0f33348cf1812805c3010ad`

Algorithm	Hash digest
SHA256	`122696d814d28a101573d9766bd530f0b005f982df8329b2386bb5ba87704641`
MD5	`4038f10d4fb20674fd36ed191bb8e777`
BLAKE2b-256	`5b92d771b3dd57337745d8c5be103a2a4ad3b80d5f0a2cb7d4d3ebf8df2e4614`

Algorithm	Hash digest
SHA256	`331a59130a024f22d443846da63de9743e0c5c197698b21e20cf47c50c348cf7`
MD5	`50d7719cb0d32eaebe0a0e3421d4d58b`
BLAKE2b-256	`03f896f0ddec30fe2e6979645070b9c8da51711721fe721323d758bcee8af89a`

Algorithm	Hash digest
SHA256	`f4f3b2763328d250ec80c22176ec680bb506d329e7326f91757eee113bec76de`
MD5	`663411b7ab31a0e859177a04968ccdaa`
BLAKE2b-256	`ce5fe219f2c4dce4ac8f6953546d24352db16a80e111e16e77c4ed8d1b439b15`

Algorithm	Hash digest
SHA256	`658017079d58a9699e4cd6d46e41cfb62f90f0400b439e2204116b8e66b6d49e`
MD5	`5fbcaa80f334b16b478846366cc7d8d4`
BLAKE2b-256	`03b14e9da940e30c9b2c7886e4c3d95618b81f4dcda91063e8dcf4963311b0b1`

Algorithm	Hash digest
SHA256	`4da53cda9b53821adbb1fe23cee4641e1a179071ee4e142b0e8df1a86da9bdd6`
MD5	`1193fdfbd035bee30b1d577fe473a454`
BLAKE2b-256	`4b6922d9012fc309f643cf9319c0476a5d6e719fa34bb1f75af07ca1612653d4`