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	`55a25be73c3246eef1d1c9da63ba1325ebf208e4c0ca9340998c0b1fe6d9b582`
MD5	`3f7a8afc2eaadb8bd8cd20c131647dce`
BLAKE2b-256	`ccbe9978a94baddc7860f81420bd1152d9ca1fa358d34c519e9920d1ef83d16c`

multimodars 0.5.8

Overview

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1 maintainer

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Algorithm	Hash digest
SHA256	`e0691786ec3b2b5f1e9953c7588fa43c726851ac2b9c8934fe131117ce3477a8`
MD5	`c66c7d6d2b37f063cef9f45dd02d8631`
BLAKE2b-256	`2cd763aa6fe03ef607e1903e87030e381fe3ccf44760f5045cf38eaacc2a78d1`

Algorithm	Hash digest
SHA256	`7d28f0b4213009f7d59bb994bd5d84289f407bc91908b64f3a3d025f5bf06d73`
MD5	`ddbcbf5fad8f0c7e2a41124bd485b135`
BLAKE2b-256	`4f34d932a4057cdba297274b9349dcfe5603d3959b034334e9efca50aa81573c`

Algorithm	Hash digest
SHA256	`7fce20e394b87989b0bf7ee47b897735142decc75543ece76ef29c6047e335d4`
MD5	`f5f2ea27a98d9db346aa6d92f852c796`
BLAKE2b-256	`b51fc7ef5e989781a934695663a12f224515b9da728c362a221d144f4d9a4061`

Algorithm	Hash digest
SHA256	`ca96c3a58687d51cb8cf59eec7815c253f8f8261fe6087d63139b4bf1909244f`
MD5	`85e44ff96c51e00e2b9e13417197bd02`
BLAKE2b-256	`98d6a2cc3e7eae124b7f9f15f0695bcd4a446d117ddbd271e2dc32d8e663def0`

Algorithm	Hash digest
SHA256	`0aa17ebc26058d30e354345a214c493d55265e543299b0a7c872df8c1d83dfe2`
MD5	`f78b5ec5bd5254d01f5ce239cbdcb938`
BLAKE2b-256	`397bbfd18055739822f17d855593c2a9e3765245d516434fa2d2dd744df56935`

Algorithm	Hash digest
SHA256	`250ba83ca75f0b4f12a847675f43aef128053addd353cda80fd35b376dd9bbc2`
MD5	`e9fb87de3bb7ca090d03b5c9106abf55`
BLAKE2b-256	`a45502d79e1efed358e152880a00294e515baab9de835a0897474edca50da9b5`

Algorithm	Hash digest
SHA256	`c15ef52df59f6f41d178c42aae7bb60a5b012e1c723e44fa584bee7466d2761d`
MD5	`694262131e449dded11a52d2cc06e96c`
BLAKE2b-256	`2e4002217454377d1fc5a8fce995825d3f46c9dcda10580bada1c73f338f36b0`

Algorithm	Hash digest
SHA256	`4098dd50151a91805a6463aa8aaafedd331884d7f023bba4d8e4e21ba9db941c`
MD5	`6c192a0d5b182b1c5b79f119a469b272`
BLAKE2b-256	`06000e1e19410c575233f3e26a92b2095c8f8a9ff21bbed751c526a57286b470`

Algorithm	Hash digest
SHA256	`f8aba87141d56c303570bdb4af0e23d42498b9f00703a6123dae6ecf6501d5a2`
MD5	`569f062a8b5748008f88ca05d26ba547`
BLAKE2b-256	`07f7cf8f3dd68dc4353a5b720b1f856be3d9f3f2eda8d5ebd627086e3f7f6c2c`

Algorithm	Hash digest
SHA256	`4b56a2ce1a34a0eefef3f09cb15ae743ba5d7076caa5f822a00ada984fa90c26`
MD5	`56e66cde39e8555010258228755f7c38`
BLAKE2b-256	`76adad64f07f83bc126c7e6da0db62073bc8b8474508b8cb5e1d63d829f648ff`