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.

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

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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BLAKE2b-256	`9c55767f2dcf94aae723505ce016b31cc53b3947aef13f67ec4f0a7e866cd203`

Algorithm	Hash digest
SHA256	`36f42e071c8090ec261f44172c9171fb46bb1b2b6bd28c3eb2bc658e2bf2c01c`
MD5	`74fd459f0ceff83e140c42542e9bdc2b`
BLAKE2b-256	`55e94e28a3add52213e6895be5187e5f21689d925f79312379d8f2b1e80cce7e`

multimodars 0.6.0

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1 maintainer

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Algorithm	Hash digest
SHA256	`675103fa1ce1cdfce4cc6ae458492d0b6d390a0080b08453396a743195be0e95`
MD5	`e32541e7644f2f8bf9a60c6d96f7354d`
BLAKE2b-256	`09f92a9ca92953864ff173e64fc561889a4d5e4745be9a9dd04d2762c13282c3`

Algorithm	Hash digest
SHA256	`81f68620e24bd37f5df97bf0e7cc02f0e232f37c86d12ae2c6ca743e02b0234b`
MD5	`026fddd2ada63fcd25cb2b6e51d46880`
BLAKE2b-256	`52489dbd4d78e265af59921783ca3b919b3cdf9195923d59fda1de635d1302d3`

Algorithm	Hash digest
SHA256	`8152f439079718324bcd615e4c6be46c7c1ed728aad4e88f43cde2b1a2c04680`
MD5	`048d2510f0751351f9aa683e1ca84cf5`
BLAKE2b-256	`3837c4782a006b4290ecfc9a1c55e3c4e8a9e2ed25684b45136aee2067661d6a`

Algorithm	Hash digest
SHA256	`120926ab51d4191d0a6635e9956ad11f1e98b2a1cdbb3562a80261ee324f3933`
MD5	`3bd54a8a01ecf45c5ee328995512f846`
BLAKE2b-256	`df12a5ac39db3cba97564ad2cfa18aabd38205c73f4ef7257e4455492371efa4`

Algorithm	Hash digest
SHA256	`f76c81b095da17a66dc1c5f2a8a3cf9339b374c5d8efb6ae04820155acfdfb8e`
MD5	`6c5b408bbf39450966d28eeabe36408e`
BLAKE2b-256	`2a0b825e75e3735ec2546031a9a9668d9d82c0d6cd20889366b2c4018b6370ca`

Algorithm	Hash digest
SHA256	`6ece60f5c0014d4b72227ed560bc44d3d827bb2666bfa852084abdf374997dc4`
MD5	`a043d971287dd658ed12d38f7cd800e2`
BLAKE2b-256	`6126e923377ff800f5ebefa42c868d093f0e8dc457440948f38e693f64dd8e8e`

Algorithm	Hash digest
SHA256	`dee50079692f1c178abeb9507bcfc81e35b2ee6db1e8aff6ef9ad46138725ffb`
MD5	`cf68537a8d04f0e0d855aac95f993f1e`
BLAKE2b-256	`619917bcbc1956d79f84aef8e1965f0b3eb87e6a17f38cc9dcba8185869a5ba6`

Algorithm	Hash digest
SHA256	`4f3e52b606f16063274ab84b9cb90e9e3bb114a1a6382f267f98cd42d17a3ccb`
MD5	`9f806ddaa884646ef94bfff0e113970f`
BLAKE2b-256	`a749eaf3cf147a356e093a05acf1e033e70bbdf07efe5da75c6d2d061c4e737f`

Algorithm	Hash digest
SHA256	`a5a1dc3f6308f5bf9d3a3cd00e7a5550932422a4fd5c51c59ddd3e317e412e08`
MD5	`f57de418eb8e2bcc6131a2d64276257b`
BLAKE2b-256	`10792737515beed7e35743ed48c3a5764ddfe1efd216c7ef8c93220fec092e9f`

Algorithm	Hash digest
SHA256	`356fb71841c73258f2924fdc9376a24ee84b694cfdb4f7b93b405bcaf24b2735`
MD5	`eb6e085c824ddedcd164b0dd87f10840`
BLAKE2b-256	`61af6d93147345d25e538097a4d7d67932480666ac58a076df3cf9a941228bac`