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	`6f299673cb222a52fde25a3a619e40cef7e360ecf7782419e5b5ab1e7fde2ee4`

Algorithm	Hash digest
SHA256	`1209fd7276f1e0e5a03b8233d04e63696f1e58c939697c3fd1823fa15f2ae3aa`
MD5	`9eba25eb861bd7e2cddf9a00bbfcb674`
BLAKE2b-256	`29f5eef9d0f5e9ce56dc83fa8fcf9c5350cd81c16ecfc607d3bde326ec0ada58`

multimodars 0.6.1

Overview

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1 maintainer

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Algorithm	Hash digest
SHA256	`5077dfa0649155a68f6a89aef8a156deaddf7f24bf680357e5687ce11aa4e336`
MD5	`c42136cc8cb544a33cac20929817c27d`
BLAKE2b-256	`1e1c545a9d5cd3e5be62969c93e05c39b6b7d07cedfc23ea2cf38eb457ed1776`

Algorithm	Hash digest
SHA256	`1332a4e9ac0d5571b898de9a91999b61640ac6ec0f0033e8ec9d1c56c6afc482`
MD5	`f11ae903603e06e481df3bbee80c0986`
BLAKE2b-256	`ef06096976239a5e5d5ee69ee1dc9e8e1b45bcd098ba42440565d3931cff315e`

Algorithm	Hash digest
SHA256	`39b610789ab9e22310b8a9cf75e3755547a3af4878abcdf1d45ccb9f4831a3a4`
MD5	`e7c9eb92c4d1615c89c41aeb1c565c6f`
BLAKE2b-256	`f6701d6106a6aa04afa8244daf7d9c68f00b55446f746b8fef998413d4940e63`

Algorithm	Hash digest
SHA256	`c3ca52b25d1a85c3d5fe28fd1b87ab631e4a58ac8509ce7f0e12b5f321e7efe3`
MD5	`87b54c7d859698bbfb909edb2f902ad2`
BLAKE2b-256	`8d15bd6fa791d2b9e7907f36c4df89d5a9dc82cc00abad62026b2c3ab6981f38`

Algorithm	Hash digest
SHA256	`75602c23a16a70f6d0059cfcc0b9111631f7e1355d7e5a98e023b21ca6b04d28`
MD5	`66330998573e1b5b291b9d741144837f`
BLAKE2b-256	`32a1292d73b0c741bb55279772dab4edf4ddadf1742430b647533bd80c2f85ba`

Algorithm	Hash digest
SHA256	`d1ccbe76b093a95f341302179dc51901dda6e9ef3f0c874bdfdab6c97bfaebb8`
MD5	`0e03c13c494779bb82c36a45c7dfc8a0`
BLAKE2b-256	`cba635ffebfff1a3eff30a7809b8b7dadbd2f30bcd39ec894932391204f62a1e`

Algorithm	Hash digest
SHA256	`6caa82e14169ba25aad2daae941b8ca7bdaf04c8ff999d8361294500d5b928e8`
MD5	`4176ba1face5a9d87e957f79d9fc0411`
BLAKE2b-256	`5b0c584dfd9132780a95499dafc22baa68874cd5075c0c729e537945700ffc0c`

Algorithm	Hash digest
SHA256	`14ed3c24a3230178a63cc4fe9737774147f26260d8e6203f9b9618ba268035cb`
MD5	`9bb8b49560e3bc642199788cffcf7c22`
BLAKE2b-256	`fb42d84bba983fccedcbd7ecbc7a76b45ad552cbe8504140c76b5a92c8e62d09`

Algorithm	Hash digest
SHA256	`b4d91dd0cc7916dd3d3795c2d191d6c8876f4fb412f89ea718a16da7a2877db4`
MD5	`71fd8892c5ccb8e59c53885d0b8e42c1`
BLAKE2b-256	`07dbb434364c9684838b050f884b934b505d0fe4157cc47926d2b90713f06eaa`

Algorithm	Hash digest
SHA256	`b58dee75a71e4f5b6cf659471f5492ba3f12fa7bdcabd0eb56e41b87688144fe`
MD5	`b59bf6a105bfe2385d0682e03b519de3`
BLAKE2b-256	`18599334016bd942fb95a35c6674b5abfd988ad49c25cc8121a6c0543fa102b6`