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Update MR/app.json

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  {
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  "display_name": "Synthesis: MR",
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- "short_description": "<b>Description:</b><br>Supervised MRI (T1-weighted) synthesis model developed as part of the <a href=\"https://synthrad2025.grand-challenge.org/\">SynthRAD 2025 Challenge (Task 1)</a>.<br><br><b>How to cite:</b><br><cite>V. Boussot et al., <i>Why Registration Quality Matters: Enhancing sCT Synthesis with IMPACT-Based Registration</i>, arXiv preprint arXiv:2510.21358, <a href=\"https://doi.org/10.48550/arXiv.2510.21358\">https://doi.org/10.48550/arXiv.2510.21358</a>.</cite>",
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  "description": "<b>Description:</b><br>Supervised MRI (T1-weighted) synthesis model developed as part of the <a href=\"https://synthrad2025.grand-challenge.org/\">SynthRAD 2025 Challenge (Task 1)</a>.<br><br><b>Architecture:</b><br>Based on a 2.5D UNet++ with a ResNet34 encoder, the model was optimized using the <b>IMPACT-Synth loss</b>, a perceptual loss leveraging semantic priors from <b>SAM 2.1-s</b>. Training was conducted with the <b>KonfAI</b> deep learning framework.<br><br><b>Training data:</b><br>Paired CT–MRI (T1-weighted) volumes from the <a href=\"https://synthrad2025.grand-challenge.org/\">SynthRAD 2025 Challenge (Task 1)</a>, <b>aligned using IMPACT-based registration</b>. Corresponding B-spline deformation fields are available in the <a href=\"https://huggingface.co/datasets/VBoussot/synthrad2025-impact-registration\">SynthRAD2025-IMPACT (aligned)</a> dataset repository.<br><br><b>How to cite:</b><br><cite>V. Boussot et al., <i>Why Registration Quality Matters: Enhancing sCT Synthesis with IMPACT-Based Registration</i>, arXiv preprint arXiv:2510.21358, <a href=\"https://doi.org/10.48550/arXiv.2510.21358\">https://doi.org/10.48550/arXiv.2510.21358</a>.</cite>",
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  "tta": 2,
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  "mc_dropout": false,
 
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  {
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  "display_name": "Synthesis: MR",
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+ "short_description": "<b>Description:</b><br>Supervised MRI (T1-weighted) synthesis model developed as part of the <a href=\"https://synthrad2025.grand-challenge.org/\">SynthRAD 2025 Challenge (Task 1)</a>.<br><b>⚠️ Warning:</b> Models were trained with an anatomical mask, but no mask is used at inference. Artifacts may appear outside the anatomy. Future models will be trained without masks.<br><br><b>How to cite:</b><br><cite>V. Boussot et al., <i>Why Registration Quality Matters: Enhancing sCT Synthesis with IMPACT-Based Registration</i>, arXiv preprint arXiv:2510.21358, <a href=\"https://doi.org/10.48550/arXiv.2510.21358\">https://doi.org/10.48550/arXiv.2510.21358</a>.</cite>",
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  "description": "<b>Description:</b><br>Supervised MRI (T1-weighted) synthesis model developed as part of the <a href=\"https://synthrad2025.grand-challenge.org/\">SynthRAD 2025 Challenge (Task 1)</a>.<br><br><b>Architecture:</b><br>Based on a 2.5D UNet++ with a ResNet34 encoder, the model was optimized using the <b>IMPACT-Synth loss</b>, a perceptual loss leveraging semantic priors from <b>SAM 2.1-s</b>. Training was conducted with the <b>KonfAI</b> deep learning framework.<br><br><b>Training data:</b><br>Paired CT–MRI (T1-weighted) volumes from the <a href=\"https://synthrad2025.grand-challenge.org/\">SynthRAD 2025 Challenge (Task 1)</a>, <b>aligned using IMPACT-based registration</b>. Corresponding B-spline deformation fields are available in the <a href=\"https://huggingface.co/datasets/VBoussot/synthrad2025-impact-registration\">SynthRAD2025-IMPACT (aligned)</a> dataset repository.<br><br><b>How to cite:</b><br><cite>V. Boussot et al., <i>Why Registration Quality Matters: Enhancing sCT Synthesis with IMPACT-Based Registration</i>, arXiv preprint arXiv:2510.21358, <a href=\"https://doi.org/10.48550/arXiv.2510.21358\">https://doi.org/10.48550/arXiv.2510.21358</a>.</cite>",
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  "tta": 2,
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  "mc_dropout": false,