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	`6e586ec5b4d537437081b25c23802b446d1af2cb7b8fd57029f78891f052b600`
MD5	`031f1998df4cf699e7df4940ef68f882`
BLAKE2b-256	`c2c9dfaa365a8e825d2269612df10a45fb50c9affbc3aaa190bd802f3150dce1`

multimodars 0.5.3

Overview

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1 maintainer

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Algorithm	Hash digest
SHA256	`de9b42a97146ddad23773a6c70037a10cb0a26e7c3bd35858173393a0e9ed1d2`
MD5	`db9e1dd0379ff54082fcbe1d801b3047`
BLAKE2b-256	`09909be64135a66b0923aa857718868e6b86344b4c7acf4319316891b09081fb`

Algorithm	Hash digest
SHA256	`22f4fbcf509c2d6e9e26ea18f52fe444dc4e316d62a43859544157cf70023726`
MD5	`d5102f50a1228aaa6dd5a247ba36aa98`
BLAKE2b-256	`312ec6f3a4ac1e3a8e678c3dd03163d0fe792a6bf7d3838e69ea1a46e24a6ebe`

Algorithm	Hash digest
SHA256	`650b93c313da4e6e42c3f02a3dd33e3e8019c8eab9939e2d057a1e0bcdf6b1ee`
MD5	`33cdad58f47a56f67791bcce6b7c596c`
BLAKE2b-256	`e2435635894f8f825a7e2125d71d706c825af31f0e13b66b52056dbf08897ff7`

Algorithm	Hash digest
SHA256	`458a7be2c2aa32fbdc1c31f8372437c54e0f6dc5e69fc6f07ea99925e4dcb85d`
MD5	`04693bb67d0bb25bd40dad47e371b37f`
BLAKE2b-256	`79de6e83ebc6c7272fa60c59c3c8dea8bef4143dbbbfcbf5abd57db43d7fec6e`

Algorithm	Hash digest
SHA256	`4e0395c5221a0da1c39e77d69977bcae849a421661de9cdb0c204e89cde7c593`
MD5	`64f7d96985206ee6e572bbf1f98b055e`
BLAKE2b-256	`3dc9ef8b3f234776bd830261bd1f23b3834bab24ff8c6e63d172c2f26dab0942`

Algorithm	Hash digest
SHA256	`5cb696df3f1c6cdc8560e5ec7c90c12c491df98c42513b3c581ba86949cd2a14`
MD5	`23105f3aa877443404d31a35f9725560`
BLAKE2b-256	`715e681d5764066050f1398aa6d6deba8ad7ce8c4d44a162444f3130d058ff21`

Algorithm	Hash digest
SHA256	`1c69c3f8b93546f16ac7bfd7154eebc812f8c7a3420e4f1e108b373a06bc425b`
MD5	`82db3823791578fb3f34f479cb1d8f05`
BLAKE2b-256	`a72bd99513748c8a377a52651ca716cec9211546ef5a64bec8873b611e8e9d23`

Algorithm	Hash digest
SHA256	`89b3bfade1f486b72253da9225edb1303392f1c155370b6597c783d786937016`
MD5	`eddbe3049fea39ae65f35f4db12b2c95`
BLAKE2b-256	`7468061e25c7025cfb9e8c43ca7c9bdbebe3782875fbf905195cc08f0792769a`

Algorithm	Hash digest
SHA256	`ea2b2ccd30472e5b4e5648a3a95768f320de451e9123413828b21af7d86d2b53`
MD5	`75c04e92d45576263b4b922d7fa4a968`
BLAKE2b-256	`ec8065eeb91bb1f2f67e78a7c1db51ccc3f063dc6a44679d2ef20915ebcff9e7`

Algorithm	Hash digest
SHA256	`b6a4b1312790663e96e2b2812feede83feed278cba0114a432e0df629c1fef28`
MD5	`330c6429f4f57aaa0c6eb9b63abf8f96`
BLAKE2b-256	`6715229bcd09054f75213aadaddf761b2cf90ad716dae923aa73f2f295f47940`