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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BLAKE2b-256	`cb0f5db8f6b608e0adde2aee7c7527bf83347593de8ba0ad6637c528d1071dff`

Algorithm	Hash digest
SHA256	`621a2bcaccc353ee533dcbea6cf0cb925aff5acda0e5e35947121df5077ed482`
MD5	`546161b1a0282634b858f3a8ef0763cb`
BLAKE2b-256	`e4c46ee3bee6e2ce4910488c7c544bdd3d04bcfc25d72e54349095af003d14d7`

multimodars 0.7.0

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1 maintainer

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Algorithm	Hash digest
SHA256	`1bf7c789f5f3c750d5155dff06ba16294cfee357c100af072835e90a2b4d3553`
MD5	`4175f0e053ab3750bced053e6f6215f5`
BLAKE2b-256	`1217304864abafce3bb4d51c08621803f6bb91c8f3d45fee91a4270f2a077047`

Algorithm	Hash digest
SHA256	`b3bb5537dce172377d3fa841828e2e215b4fd576c93807ff1bd4082fdc406be2`
MD5	`c0d280951d6e204cc5a6a33c34f4ca36`
BLAKE2b-256	`2b8b42d3f096f35589eb1e33e7a9538e606f4e904db437f9a6f1699aed3420a4`

Algorithm	Hash digest
SHA256	`be8ac036eaa466ec2a04821cc7b7d27a2a5ea7c8c00c7f700f778ea14435c437`
MD5	`bd062c7801054b1b7cc8316db34c01fd`
BLAKE2b-256	`0bda13edd89525cf7fb47440fdd444b5c7783158f5624baad448164bffd2ae60`

Algorithm	Hash digest
SHA256	`5edd2e3fecbbba2e82c4fae456a2c1407d8a50d47446dfe37ff9c4450fac5e44`
MD5	`7a02950e497b174341c81e8000d3e63f`
BLAKE2b-256	`306fe3e3de698797bd0cb7dcf896b25e394d7af1467bba0e80ac79f5e4645cb8`

Algorithm	Hash digest
SHA256	`47cea1e0f6fba3279be10a7191aa550937170df9361fd4c558d5cc3cef193c05`
MD5	`53b8f6f647077effc76fe00708197083`
BLAKE2b-256	`6c2c28046a63b4a8f0a09c9c2f212b653709ddd1077ee901a3fbd91602e4c7c0`

Algorithm	Hash digest
SHA256	`1d60b4f58069882c9aeb93870250b56b5d3fe1b6829575daee99dadff13c5e74`
MD5	`923197862fba0e8f238038ee836ad51c`
BLAKE2b-256	`278b9cfe6014d4eeb5d31a8570be8e6a7a92f636277e8fdb1a745a5886c7da6e`

Algorithm	Hash digest
SHA256	`4078e78e0d9fa69d4b5b1728ead9265e89a2d603b579318812aad3b21cc81a41`
MD5	`e5d87980cc167e0136ba65c7ea8f9cce`
BLAKE2b-256	`a5e1827dc370a15ac969135b50d4a33280f2a9bf80481f89f48fba616b04ff1e`

Algorithm	Hash digest
SHA256	`5e885343382529e16651a47c9d34aed11579cdc279a12566c3c60d35b167fcd5`
MD5	`badbb3672122dbe703f496759e013329`
BLAKE2b-256	`0a07c42bd058607f05467eebc4ed79ffe30a698b7ec955a0907048fdaa652dc9`

Algorithm	Hash digest
SHA256	`a2ac1fdb6366ca2ee658847a92e8fda5ff16f48b59fd1753a9431fe7049fb778`
MD5	`6140b85e4564d2213be2ecec6f77ceab`
BLAKE2b-256	`5b95a5fd687081b8fa40c51ed8b77d71eeceab2e490b7d89fae4436f23312165`

Algorithm	Hash digest
SHA256	`5d7fcf153c9fa5e4c9050f9c36e04399deb761b90fa79d0e2ec8231b7390505e`
MD5	`b45a5a90a75d0409645af6155de470f3`
BLAKE2b-256	`dc6ad80a41bd3c2a0f84dd4be4f96a6205b6279b7460ec058f85f51b61fb1b08`