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	`f219f3d6d57800eb25a4b32df69d95ead1b97d9f6391626e2f24800296897610`
MD5	`aa37f7588d6cbc2c25aba8f16fce4282`
BLAKE2b-256	`c8c9aaa776307be61db5764e64e98dd2dd33b3e283b295f2ac7674dfcff08ab7`

multimodars 0.5.7

Overview

Key Features

Installation

Quick Example

Pipeline

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1 maintainer

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Algorithm	Hash digest
SHA256	`87ae6197ad55dd283b47c70dc511ee71a94d0949967461943a776983e6ba2463`
MD5	`1a71b28623a1cef98e1e42fa80388bae`
BLAKE2b-256	`62455a3e950b1befa8f1ba0e2457db5e6000a0a62a63d00425e3322154bada1b`

Algorithm	Hash digest
SHA256	`ad8e16355b0ce8b74d9cd698761ae72f0ceef9dc8ed4d048f79ffc391a1f3401`
MD5	`d60617c3e97b2df93a46d29cb3beb1b0`
BLAKE2b-256	`20f2c92ec0ed683b140fa7781e6d1b575eaa19ce1071914283cab8d4f4fd47e1`

Algorithm	Hash digest
SHA256	`dd9357cb519e6814e2888e6b7aa3acdea9cdbf524994f4e0469bb8ee40be0052`
MD5	`aae3420e5d5cbf4c86fd415b8a84e999`
BLAKE2b-256	`5f2e0b4bddb044b9d40278d25e982301c630f88e626c9a273e4e8ee8645c619e`

Algorithm	Hash digest
SHA256	`ce919c04c700892ac4c7df57173a48ed195fdf36a3d76a5e1f12f72d2f067ee9`
MD5	`9e0a939ca771e416dc4d49736004e039`
BLAKE2b-256	`46ab1354d4c0707a2b2fe81f0c04032251fff4b1e6ff30a85e3addb6fdbf9904`

Algorithm	Hash digest
SHA256	`d72f5352c91cc42d9529a369c108e49e20f815aba8ab137ea1b142ee26724f98`
MD5	`f635104696567f8a91ab30753005ac30`
BLAKE2b-256	`190030a017efba1e71226524bdcd446d6abca97cc1a540a1cae386dfaa6f9dd0`

Algorithm	Hash digest
SHA256	`904c38ae81a30a3cf7210e0445da7d5ad267f1222527c06e7887f2a01580c536`
MD5	`6f98d877deb2c10b3ec5afa0f635a45d`
BLAKE2b-256	`ddbecc62641792a8696126024a698a4d481a99dc7a5e9999b6787f1e88126592`

Algorithm	Hash digest
SHA256	`448f00536531a817d7588cc5df00e4a6441412c2ac29c29f0b133a2ed1ef5d42`
MD5	`275509938f49b90f4007be01d5f45b4a`
BLAKE2b-256	`654b17f0d691cda1fee7e69b5b8207ee20871d0f92d3476b2aa286ad22e122b7`

Algorithm	Hash digest
SHA256	`4fcf9fd33b9009f34fee3c871a9e684c8f1aab103046e6146d58219649494165`
MD5	`73a99392ccfbdb09bf6430bcc8f66e77`
BLAKE2b-256	`07e6009a0f3647793dd17dd24176d77d9eae1afb7a9a41683f026ad2ce9c011e`

Algorithm	Hash digest
SHA256	`196341183d773d4fe8f28e176ca983207a21a80c49714c062f0b63079df43502`
MD5	`1b0155e14729d9fa62c1801d89cc375e`
BLAKE2b-256	`dc3634ff177c75868a1e91ef4a51798bdf7215d1346d2542fb0877e653190ef5`

Algorithm	Hash digest
SHA256	`3616e3c09876ddfbebb009744d8ddafb487aacc873be00a603e9d57c8c266b70`
MD5	`3bed3eb30aa2bd3458afa519ca3a2c64`
BLAKE2b-256	`0c9e9493ffece0d9516ce370507286fc1ebaf2b2f62d62cb0c40aac398022c03`