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(BSplineTransformSplineOrder 3)
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(Direction 1 0 0 0 1 0 0 0 1)
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(FixedImageDimension 3)
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(FixedInternalImagePixelType "float")
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(GridDirection 1 0 0 0 1 0 0 0 1)
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(GridIndex 0 0 0)
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(GridOrigin -133 -84.5 -137.5)
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(GridSize 27 28 24)
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(GridSpacing 10 10 10)
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(HowToCombineTransforms "Compose")
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(Index 0 0 0)
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(InitialTransformParameterFileName "NoInitialTransform")
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(MovingImageDimension 3)
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(MovingInternalImagePixelType "float")
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(NumberOfParameters 54432)
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(Origin -118 -71 -124)
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(Size 231 244 204)
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(Transform "RecursiveBSplineTransform")
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π§ SynthRAD2023 IMPACT Registrations (BSpline Transforms)
This repository provides Elastix B-spline transformation parameter files generated using the IMPACT method on the SynthRAD2023 dataset.
Each file corresponds to a non-rigid registration between a reference CT and another modality (MRI or CBCT), aligned into CT space using Elastix with the IMPACT similarity metric.
- Task 1: 315 transforms (45 excluded cases)
- Task 2: Not available, transforms will be released soon
π Overview
High-quality multimodal registration is essential for supervised sCT generation.
Inaccurate alignment between MRI/CBCT and CT images can lead to blurred, anatomically inconsistent, or artifact-prone synthetic CTs.
By leveraging features from pretrained segmentation models, IMPACT improves the anatomical consistency of cross-modality alignments, ensuring that each voxel correspondence reflects a true anatomical match.
The B-spline transforms provided here can be directly applied to warp MRI or CBCT images into CT space for training or evaluation of sCT generation models.
π B-spline Transform Details
All registrations were performed using a 3rd-order B-spline transform with a final grid spacing of 10 mm across 4 resolution levels. The IMPACT loss was configured as a multi-metric combination of MIND and M730 features extracted from the final network layers.
π§ Usage
To apply a transformation, use Transformix (from Elastix):
transformix -in Task1/brain/1BA001/mr.mha -tp Task_1/brain/1BA001.txt -out output/
Where:
mr.mhaβ Input image (MRI or CBCT) from the SynthRAD2023 dataset (not included here)Task_1/brain/1BA001.txtβ B-spline transformation file from this repositoryoutput/β Directory where the warped image will be saved
π Repository Structure
SynthRAD2023_IMPACT_Registrations/
βββ Task_1/
β βββ brain/
β β βββ 1BA001.txt
β β βββ 1BA005.txt
β β βββ ...
β βββ pelvis/
β β βββ 1PA001.txt
β β βββ ...
β βββ Exclude.txt
βββ Task_2/
βββ brain/
βββ pelvis/
βββ Exclude.txt
- Task 1: MRI β CT registrations
- Task 2: CBCT β CT registrations
- All transforms are in standard Elastix parameter file format (
.txt)
β οΈ Restrictions
ποΈ Excluded Cases
45 cases were excluded from Task 1 due to poor image quality. The list of excluded cases is provided in Task_1/Exclude.txt.
π References
If you use these transformations, please cite the following works:
1. IMPACT Method
Boussot V., HΓ©mon C., Nunes J.-C., Dowling J., RouzΓ© S., Lafond C., Barateau A., Dillenseger J.-L.
IMPACT: A Generic Semantic Loss for Multimodal Medical Image Registration.
arXiv:2503.24121, 2025.
https://arxiv.org/abs/2503.24121
2. SynthRAD2023 Dataset
Thummerer A., van der Bijl E., Galapon A. Jr., Verhoeff J.J.C., Langendijk J.A., Both S., van den Berg C.A.T. (Nico), Maspero M.
SynthRAD2023 Grand Challenge Dataset: Generating Synthetic CT for Radiotherapy.
Medical Physics, 50(7):4664β4674, 2023. Wiley Online Library.
https://doi.org/10.1002/mp.16884
3. Registration for sCT Synthesis
Boussot V., HΓ©mon C., Nunes J.-C., Dillenseger J.-L.
Why Registration Quality Matters: Enhancing sCT Synthesis with IMPACT-Based Registration.
arXiv:2510.21358, 2025.
https://arxiv.org/abs/2510.21358
π§ License
All transformation files are released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
You may reuse, modify, and redistribute them for non-commercial research purposes only, with appropriate attribution.
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