"One package to fuse them all."
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:
2. Inter-pullback alignment — registered pullbacks (rest vs. stress, diastole vs. systole) are aligned against each other to reveal stress-induced and pulsatile deformation:
3. CCTA labeling — the CCTA-derived geometry is automatically segmented by vessel region (aorta, RCA, LCA, intramural) to prepare it for fusion:
4. CCTA–morphing — the labeled CCTA geometry is morphed along the centerline to match the high-resolution intravascular geometry:
5. CCTA/intravascular fusio — the morphed CCTA geometry is stitched to the intravascular geometry, replacing a section with a high resolution verison:
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.
1 maintainer
Metadata
1 maintainer
Release files for multimodars 0.7.2
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Source distribution (sdist)
| File | Size | Uploaded | |
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| multimodars-0.7.2.tar.gz | 12.3 MB | Details |
Built distributions (wheels)
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Release files / multimodars-0.7.2-cp313-cp313-macosx_10_12_x86_64.whl
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Release files / multimodars-0.7.2-cp312-cp312-win_amd64.whl
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Release files / multimodars-0.7.2-cp312-cp312-musllinux_1_2_i686.whl
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Release files / multimodars-0.7.2-cp311-cp311-win_amd64.whl
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Release files / multimodars-0.7.2-cp311-cp311-musllinux_1_2_x86_64.whl
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Release files / multimodars-0.7.2-cp311-cp311-musllinux_1_2_aarch64.whl
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Release files / multimodars-0.7.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
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Release files / multimodars-0.7.2-cp311-cp311-macosx_11_0_arm64.whl
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Release files / multimodars-0.7.2-cp311-cp311-macosx_10_12_x86_64.whl
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Release files / multimodars-0.7.2-cp310-cp310-win_amd64.whl
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No |
| Uploaded via |
maturin/1.15.0
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Release files / multimodars-0.7.2-cp310-cp310-musllinux_1_2_x86_64.whl
| Download URL | multimodars-0.7.2-cp310-cp310-musllinux_1_2_x86_64.whl |
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| Size | 3.4 MB |
| Tags | CPython 3.10 Linux musl 1.2+ x86-64 |
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No |
| Uploaded via |
maturin/1.15.0
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Release files / multimodars-0.7.2-cp310-cp310-musllinux_1_2_i686.whl
| Download URL | multimodars-0.7.2-cp310-cp310-musllinux_1_2_i686.whl |
|---|---|
| Size | 3.4 MB |
| Tags | CPython 3.10 Linux musl 1.2+ x86-32 |
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No |
| Uploaded via |
maturin/1.15.0
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Release files / multimodars-0.7.2-cp310-cp310-musllinux_1_2_armv7l.whl
| Download URL | multimodars-0.7.2-cp310-cp310-musllinux_1_2_armv7l.whl |
|---|---|
| Size | 3.3 MB |
| Tags | CPython 3.10 Linux musl 1.2+ ARMv7l |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
maturin/1.15.0
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Release files / multimodars-0.7.2-cp310-cp310-musllinux_1_2_aarch64.whl
| Download URL | multimodars-0.7.2-cp310-cp310-musllinux_1_2_aarch64.whl |
|---|---|
| Size | 3.2 MB |
| Tags | CPython 3.10 Linux musl 1.2+ ARM64 |
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SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
maturin/1.15.0
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Release files / multimodars-0.7.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
| Download URL | multimodars-0.7.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 3.1 MB |
| Tags | CPython 3.10 Linux glibc 2.17+ x86-64 |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
maturin/1.15.0
|
Release files / multimodars-0.7.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
| Download URL | multimodars-0.7.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl |
|---|---|
| Size | 3.0 MB |
| Tags | CPython 3.10 Linux glibc 2.17+ ARM64 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
maturin/1.15.0
|
