{
"display_name": "Synthesis: MR/CBCT",
"short_description": "Supervised whole-body MR/CBCT-to-sCT model.
Training data:
1734 paired cases from Task 1 + Task 2.
How to cite:
V. Boussot et al., Why Registration Quality Matters: Enhancing sCT Synthesis with IMPACT-Based Registration, arXiv preprint arXiv:2510.21358, https://doi.org/10.48550/arXiv.2510.21358.",
"description": "Description:
Supervised whole-body synthesis model distributed as a single checkpoint (CV_0.pt) for MR/CBCT-style inputs. The model follows the same 2.5D synthesis setup used in the ImpactSynth family and produces an sCT volume from a single input volume after automatic body-mask estimation and intensity normalization.
Architecture and loss:
2.5D UNet++ with a ResNet34 encoder, trained with KonfAI and optimized using IMPACT-Synth, a perceptual loss leveraging semantic priors from SAM 2.1-s.
Training data:
The model is trained on 1734 IMPACTReg-aligned paired synthesis cases spanning both Task 1 and Task 2 cohorts, combining MR and CBCT supervision in a unified synthesis setup.
Input handling:
The prediction pipeline accepts a single input volume exposed here as MR or CBCT, estimates a body mask automatically, resamples to the working resolution, applies normalization, and writes an averaged sCT output using test-time augmentation.
How to cite:
V. Boussot et al., Why Registration Quality Matters: Enhancing sCT Synthesis with IMPACT-Based Registration, arXiv preprint arXiv:2510.21358, https://doi.org/10.48550/arXiv.2510.21358.",
"tta": 2,
"mc_dropout": false,
"models": [
"CV_0.pt",
"CV_1.pt",
"CV_2.pt",
"CV_3.pt",
"CV_4.pt"
],
"inputs": {
"MR": {
"display_name": "MR or CBCT",
"volume_type": "VOLUME",
"required": true
}
},
"outputs": {
"sCT": {
"display_name": "sCT",
"volume_type": "VOLUME",
"required": true
}
},
"inputs_evaluations": {
"Image": {
"Evaluation.yml": {
"sCT": {
"display_name": "sCT",
"volume_type": "VOLUME",
"required": true
},
"CT": {
"display_name": "CT",
"volume_type": "VOLUME",
"required": true
}
}
}
},
"vram_plan": {
"8": {
"patch_size": [
1,
512,
512
],
"batch_size": 16
},
"16": {
"patch_size": [
1,
512,
512
],
"batch_size": 28
},
"24": {
"patch_size": [
1,
512,
512
],
"batch_size": 48
}
}
}