{
"display_name": "Synthesis: MR",
"short_description": "Description:
Supervised MRI (T1-weighted) synthesis model developed as part of the SynthRAD 2025 Challenge (Task 1).
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 MRI (T1-weighted) synthesis model developed as part of the SynthRAD 2025 Challenge (Task 1).
Architecture:
Based on a 2.5D UNet++ with a ResNet34 encoder, the model was optimized using the IMPACT-Synth loss, a perceptual loss leveraging semantic priors from SAM 2.1-s. Training was conducted with the KonfAI deep learning framework.
Training data:
Paired CT–MRI (T1-weighted) volumes from the SynthRAD 2025 Challenge (Task 1), aligned using IMPACT-based registration. Corresponding B-spline deformation fields are available in the SynthRAD2025-IMPACT (aligned) dataset repository.
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",
"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
}
}
}
}
}