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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SHA256	`50860a538d187dd22fa447f435700a670dfe63cff13197b5334e160b8d400e76`
MD5	`e1dff2e3ca9943f74a3764481073ffb5`
BLAKE2b-256	`20b516add5cf6ca0b911c4bcdcc28a9d8aec9e637b221131981009fe295fc486`

multimodars 0.5.5

Overview

Key Features

Installation

Quick Example

Pipeline

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1 maintainer

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Algorithm	Hash digest
SHA256	`a16290424e22c83f27897e88599c54b3beeafd3250a967cc7e96f9ca827f9078`
MD5	`e2fd7470371bc326ccb513969173e727`
BLAKE2b-256	`135697456ac42e188fe38ac9d25322f5afa83420421a5b1e1c3af65211681ba5`

Algorithm	Hash digest
SHA256	`25d6bbd201c36c2edfa4d655a96363937d57f754cef8aada492cabac6d8669c7`
MD5	`0c5dc5e4a1aa30fb64fcfe8f76b5d509`
BLAKE2b-256	`06579951eea9613d0e61905a3a8046b60226d87c7a908e186f7b435f841977eb`

Algorithm	Hash digest
SHA256	`52f0924da4763c5d34b23574c1c965169a529263d9f1141eff1c345ac3c3c608`
MD5	`239c4d95d514bc894582d110a2cef414`
BLAKE2b-256	`65b72c3dfd1b1d64b0270f8e82609a94ce57fe9b33e869282e21d8116c34f485`

Algorithm	Hash digest
SHA256	`d56cc19467b794ac24a9d088fa940b4021b389e44d94898e4bdf0b73d79c1957`
MD5	`15b86817d1fc133017baad6d67906a68`
BLAKE2b-256	`664195f4823851f71604275239c03bda02f0ec07ddc8d833ab9e3b4c80c7b482`

Algorithm	Hash digest
SHA256	`17dd8d75af08bd268498f3689436119c6c7fd2b26705a639ea1629e694f6913c`
MD5	`64abbbe7412bfaaeacb69a21981066cf`
BLAKE2b-256	`842b9153a100e64a1138cabbf8003a1577f32f6b0b3252960285f60a4fd54f65`

Algorithm	Hash digest
SHA256	`2e9c9a0caefd0517a9ea71bc7eb72249fd2acb9c3f6eb57059cb4a6907a825da`
MD5	`c87a9003b8867647c87499855648a2b5`
BLAKE2b-256	`bf132570ca5ac47a6c1dbb26ef8408699a831a449e8d7afabdd96cef7885f563`

Algorithm	Hash digest
SHA256	`734c77ccc5c6ec399169bc57a81fe8e98e5a65daa11025405cece0af599a30fc`
MD5	`cb065020287d8329189e7615bc5b6028`
BLAKE2b-256	`4f0a241d8a928a1a8544ee74fa195c708bdad61bc16b152ac4179666db6da9a5`

Algorithm	Hash digest
SHA256	`d5549a2699508a581070f1c9a5f4aae3b1f89dfec3c45a5a6cc9c9e2c0f6fce3`
MD5	`707af302cb6498716ca986f34d2425c6`
BLAKE2b-256	`a65dea721ae0c908ac431d1bb04fe82467525c6ee4871444b610240511619dd0`

Algorithm	Hash digest
SHA256	`6e733abc09f55b312e669dd492bfb8dbb9df524ffa9daa94432a43d169e265c4`
MD5	`ee368bd97583d8be1375b10fbf95fbee`
BLAKE2b-256	`64c92730cfc5199d41a031403400a74dcf3c334a9a8bb688fbcfc8fdb7ececda`

Algorithm	Hash digest
SHA256	`acd71d9d11ac8e70c9fa9117f32a07d779538a509d46d37cc31f4dcd0ba75e98`
MD5	`466bdd49a7973aab1f77d93a48143eb0`
BLAKE2b-256	`d3cce19aef5df92751572a00faebad727028ac86740069163acb8064a669c5fe`