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	`a8addcac941268f707c4acb287440948f22fbad49b3bcff95f79ef2db0dbe015`
MD5	`1b36fc3745cbbf74f6e41e12a663ec0d`
BLAKE2b-256	`92caa5f752b927fa82f177667a5a774ca017a9164dc4d7d16af5ff5f20fe68a3`

multimodars 0.5.2

Overview

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1 maintainer

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Algorithm	Hash digest
SHA256	`dff8826d3b14f0b30ad0a1b38c68836b575f2f06f98e9b7186c12c7f6fae3592`
MD5	`111e34c68848e947864e1c2c4f0a79dd`
BLAKE2b-256	`f365effd93f2488758136f1d8052392f5a7d497c332a619b9b410238b30fdf77`

Algorithm	Hash digest
SHA256	`1cfff9c5189beb2b4a1e9ff9db9eca83b6d6495da23698c36a4dcacd6fdddae2`
MD5	`203f0608696892d062590726fd1616ce`
BLAKE2b-256	`2696a16bc15d09185f83844903107bdef9ea386f52e00eade4de15af21f73f74`

Algorithm	Hash digest
SHA256	`d5ba9b5a5ec588ffdd877672f028a1b84da64210f6e66ec33664b1bddd77592c`
MD5	`0cb76b91ff12d0296f6e1192f9864ec2`
BLAKE2b-256	`4d87e20586e1ec975896eba0567d396718c0c91073e959bd2edbb3d098257be4`

Algorithm	Hash digest
SHA256	`44602af316166e62ec1d4e05e431469e93929e03ddfbf00e1c5089d16a838a01`
MD5	`fc75fa70a688a343da2b2d28f9c92755`
BLAKE2b-256	`e46228da6079a0a60376e7e94bc7e20347c713235558ae1cb1ce93cec2d4633f`

Algorithm	Hash digest
SHA256	`0600b09942f66777d79c5fadbe0986899a26e2e2fe60bbe3072e6b818010f5cc`
MD5	`3b4856486b75fa3b53d8115cb90b6d8a`
BLAKE2b-256	`00b27783bde6809d415fba659595e51b42e632185a3b8751190a6be758da1978`

Algorithm	Hash digest
SHA256	`e0a721b08113340ac0df06ea7ea7ac83d797497b38cc36f30aa356381d6da249`
MD5	`7de38d6ab8b4087f30e8a2d4e240ec3e`
BLAKE2b-256	`b62d385b9f840e529002722c171fcaf643f3685d0fcfe12ee5dca18f98d7de98`

Algorithm	Hash digest
SHA256	`e9384cf5e80a6a8ad501c23ed0ed5de01b3f8ebcea08238c504aec6020d6fdab`
MD5	`2b83c1020805cabbd3f095fea0ce6148`
BLAKE2b-256	`9dda74439122e7b641514b92eaaf79ca04e9d3a44bdb14db42700d85177b0665`

Algorithm	Hash digest
SHA256	`1110030e049ee3fb2471de15d4cdcbcbe2d050d34f9a08c0c75923baef0c33ea`
MD5	`1eafce0b0592d39fca0b216fdd071147`
BLAKE2b-256	`d1009bd5c72bf016df46a3af5f008b6b7c4260bc89278a5e489bd734100fca03`

Algorithm	Hash digest
SHA256	`f3b79881804a639aaef6a0ee296ff64288d105ffd5c3cf1b2cf3379c5e5d55ee`
MD5	`a4ea4e9c6c9d952c108955799b233c0e`
BLAKE2b-256	`d69faa127b7648c155f962fae582b73ae2000cdb8d755c4587af71720bc72940`

Algorithm	Hash digest
SHA256	`65a0ee1a4ada9003c4c7f948fd6d3dae24fc06dae72bf72d3f7081c65be10a76`
MD5	`1ea72d8ecd1d34638be86eed320e7f34`
BLAKE2b-256	`6871da40ebf6a45bd6d671d8bb0f2e8ea0fb8359a28849b54644af1b9d3fb940`