multimodars

[!warning] Currently under heavy construction. Real world data proved the existing labeling algorithms to be insufficient for many cases. Brace for breaking changes!

"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 (HolOrama 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	`3c0e71016513f914b9d14df7e96289b0d1f5d39be655cc897a0fbcc7b75f2ad7`
MD5	`67fb4ad88b9037116c1237597dd84d41`
BLAKE2b-256	`ebfce77bc342a45557bfc1dfa5e2b7afb11352b7b16a81016729eacbff15ab52`

multimodars 0.7.1

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1 maintainer

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Algorithm	Hash digest
SHA256	`5c11f07c701247fadfa25bd3366bad5519a62bfdd8db9163da474e388558f936`
MD5	`dbe94f7b7bb4edffeb2ecc9e4585ffe8`
BLAKE2b-256	`11ef9414b143a657d48eab109a544367de8d78b22b273bcfa956436b954a6068`

Algorithm	Hash digest
SHA256	`3a6a30948ffd853194d426cb34cf9eb4cc63b605586e89ed3167d73a5a489284`
MD5	`271ec47fc1dd77293210a28dcbc839e9`
BLAKE2b-256	`6cb8ca352150e8b630e9dae5f8f7c1963f922cdef5aded140915e964ca235546`

Algorithm	Hash digest
SHA256	`25a3e5b8f7dc10016d9ef020bf5e66277f3d4684ab86b7d28866aaa98da477ee`
MD5	`f41341cf1d4a75dc50e28786c15aaaea`
BLAKE2b-256	`a3c2036d609f9520c669052391e7ffd8bdc939d2b7590671dd8d29fbfdb129b6`

Algorithm	Hash digest
SHA256	`3ca114782a4a31d3dc4e5808e494008430a112e5e0efe2471059a9cf4da35c88`
MD5	`862eec467a26b49021443a4ea7cf21f3`
BLAKE2b-256	`29b743853f642fd500dbc2afc4780127640fd318f9025e9ff8acfbffb988c7c0`

Algorithm	Hash digest
SHA256	`d8195099a36803d335d85cd431dae79e9d011b985002b5322645354518e7bc60`
MD5	`1d0a76f3179988e28aa4b778d64b8172`
BLAKE2b-256	`b739af03b16a1cb09a47c7b7dd72204fb94f93199d3822ed39dc0119dd4094fe`

Algorithm	Hash digest
SHA256	`73db05168c7c8cc8aae0c77af24d203a344c722d4356d6274683a2b7ae471bd8`
MD5	`100cbec4650b79edffcb3f2d1c322c48`
BLAKE2b-256	`a186586e20fe6fc8d6fa9a6cfd0ece14eec34367b91d2cc1809d76b6d0c8e247`

Algorithm	Hash digest
SHA256	`1ef7f62fd04ae009f189e6d3de2afb2b1bbe73e394b838a72d771563120ec6bb`
MD5	`89f338b0037504165115750a02b85f8d`
BLAKE2b-256	`ca596457cee07156ecbab565f7a6ed7e3178a8f4fe7988c2bdace5478843eb61`

Algorithm	Hash digest
SHA256	`dcb944d4204966bd0e82586418857f3548bdecf97ff34ce27e3094554a56c34d`
MD5	`d3d1f420642c9b585e81bf3998c9a3ce`
BLAKE2b-256	`b469bd64821ae01bee94825b5ac69a4ce3b45614fa42e03a13089ca08d03ff71`

Algorithm	Hash digest
SHA256	`581279b1540e71ef1e9b85145eb7612b5901312ad7fa0293a8c449e3d657e6f1`
MD5	`b7eaa08cb0d992a8c86bfc76dddf6e8c`
BLAKE2b-256	`4c7dca6bdd267e27fc9d01bb95aa58d3680bd044b4c280c794de50cdb6773b63`

Algorithm	Hash digest
SHA256	`13e93f0dc5dea5f13f03d78e36c9a447d3b08a34a6464400fb67101915ebbdef`
MD5	`1b9415558a66ac0edb5142bc8331641d`
BLAKE2b-256	`66c524b5598cb5c26f250930239172d3a4c95197cd3e2ccc20d0f6d54b5a08b5`