multimodars: A Rust-powered toolkit for multi-modality cardiac image fusion and registration - Ground Truth

This release synchronizes all documentation and examples with the current API. No functional changes -> all features from v0.2.0/v0.2.1 remain:

• Read in contour data from intravascular images (lumen, eem, calcium, sidebranch) and create a pseudo 3D model
• Rotate/Translate neighbouring frames to minimize Hausdorff distance (parallelized)
• Fill holes/smooth and other fixes
• Align between different pullbacks with automated spacing adjustment (different heartrate)
• Save one (or several interpolated) pseudo 3D models to .obj files (with a calculated deformation map)
• Create adventitia (or intramural aortic wall for AAOCA) and catheter .obj files
• Read in CCTA data and label different regions based on the centerlines (aorta, rca, lca)
• Align intravascular images with the CCTA centerline based on landmark points (and Hausdorff distances)
• Label the CCTA region to be replaced and scale different regions to match the intravascular segment

Changed in this release:

• Updated all documentation tutorials to match the current API (fromfilesinglepair, fromarraysinglepair, PyInputData, alignthreepoint, updated numpytogeometry parameters)
• Updated README quickstart example and added figures for centerline alignment, CCTA labeling and scaling
• Fixed broken image links and removed stale references

Additional files:

examples.zip includes updated csv files from different intravascular imaging cases, a CCTA .stl file, and a Jupyter notebook (ivus_to_centerline.ipynb) teaching the usage of multimodars and now fully in sync with the documentation.

Next Release will include the full fusion functionality of intravascular and CCTA geometries.

- Rust
Published by yungselm 4 months ago

multimodars: A Rust-powered toolkit for multi-modality cardiac image fusion and registration - Initial release

This realease includes the following final implementations of functionalities for multimodars: - Read in contour data from intravascular images (lumen, eem, calcium, sidebranch) and create a pseudo 3D model: - - Rotate/Translate neighbouring frames to minimize Hausdorff distance (parallelized) - - Fill holes/smooth and other fixes - - Align between different pullbacks with automated spacing adjustment between them (different heartrate) - - Save one (or several interpolated) of these pseudo 3D models to .obj files (with a calculated deformation map) - - Create adventitia (or intramural aortic wall for AAOCA) and catheter .obj files - Read in CCTA data and label different regions based on the centerlines (aorta, rca, lca) - Align intravascular images with the CCTA centerline based on landmark points (and Hausdorff distances) - Label the CCTA region to be replaced and scale different regions to match the intravascular segment

Additional files: - multimodars_examples.zip includes several csv files from different intravascular images, a ccta stl file and several example scripts (including jupyter notebook) teaching the usage of multimodars

Next release will include: - The full fusion functionality

- Rust
Published by yungselm 6 months ago