SynthRAD2025 – Task 1 (🥉 3rd place)

This repository provides everything needed to build the Docker image and reproduce our solution ranked 3rd in the SynthRAD 2025 – Task 1 challenge on synthetic CT generation from MRI.

Our approach is based on a 2.5D U-Net++ with a ResNet-34 encoder, trained in two phases:

• Phase 1: joint pretraining on all anatomical regions (AB, TH, HN)
• Phase 2: fine-tuning separately on AB-TH and HN

The method was implemented using KonfAI, our modular deep learning framework. Training combines pixel-wise L1 loss with perceptual losses derived from SAM features.

Final predictions use test-time augmentation and 5-fold ensembling, with a total of 10 models:
5 trained for Abdomen/Thorax (AB-TH) and 5 for Head & Neck (HN).
Models were selected based on validation MAE.

🏆 3rd place overall (Related leaderboard: SynthRAD Task 1 leaderboard)

Rank	MAE ↓	PSNR ↑	MS-SSIM ↑	DICE ↑	HD95 ↓	Dose MAE photon ↓	Dose MAE proton ↓	DVH error photon ↓	DVH error proton ↓	GPR 2mm/2% photon ↑	GPR 2mm/2% proton ↑
3	67.241 ± 22.874 (3)	29.957 ± 2.658 (2)	0.935 ± 0.046 (2)	0.737 ± 0.117 (4)	7.512 ± 4.070 (4)	0.006 ± 0.009 (3)	0.027 ± 0.067 (5)	0.013 ± 0.031 (3)	0.067 ± 0.031 (3)	98.880 ± 4.556 (1)	82.191 ± 10.164 (3)

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📐 Registration (IMPACT vs Baseline)

Accurate sCT synthesis depends on good inter-modal alignment. We provide precomputed IMPACT registrations (MR↔CT and CBCT↔CT) to ensure consistent training/evaluation.

IMPACT setup used in this work

The following IMPACT configuration was used for Task 1 (MR→CT synthesis):

• Feature extractor: TS/M730
• Layers: 7-Layers (high-level features)
• Mode: Static + MIND
• Multi-resolution: 3-level pyramid
• Final B-spline grid spacing: 10 mm

Why it matters

• 🧭 Alignment quality drives supervised sCT performance
• 🧩 IMPACT → better anatomical alignment than Elastix-MI
  • Local set (75 pts): MAE 63.37 → 60.28 HU, ↑ PSNR / ↑ SSIM
  • Sharper, more realistic CTs
• 📊 Public set (148 pts): Elastix-MI lower MAE (68.20 vs 75.82 HU)
  → due to pipeline bias (leaderboard uses Elastix registrations)

Get the registrations

• 👉 Hugging Face (prealigned pairs): https://huggingface.co/datasets/VBoussot/synthrad2025-impact-registration

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🚀 Inference instructions

For more implementation details and a minimal working example, see the KonfAI synthesis example:
https://github.com/vboussot/KonfAI/tree/main/examples/Synthesis

1. Install KonfAI

pip install konfai==1.5.4

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2. Download pretrained weights

Download the pretrained models from Hugging Face:

👉 https://huggingface.co/VBoussot/Synthrad2025

You should obtain:

Task_1/
├── AB-TH/
│   ├── CV_0.pt
│   ├── CV_1.pt
│   ├── CV_2.pt
│   ├── CV_3.pt
│   ├── CV_4.pt
│   └── Prediction.yml
│
└── HN/
    ├── CV_0.pt
    ├── CV_1.pt
    ├── CV_2.pt
    ├── CV_3.pt
    ├── CV_4.pt
    └── Prediction.yml

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3. Dataset structure

Your dataset should be structured as follows:

./Dataset/
├── AB/
│   ├── 1ABA002/
│   │   ├── MR.mha
│   │   └── MASK.mha
│   ├── 1ABA003/
│   │   ├── MR.mha
│   │   └── MASK.mha
│   └── ...
├── TH/
│   └── ...
├── HN/
│   ├── 1HNA001/
│   │   ├── MR.mha
│   │   └── MASK.mha
│   └── ...

Required Folder Structure Before Inference

Your directory must look like this:

.
├── Dataset/
├── Task_1/
├── UNetpp.py
├── UnNormalize.py
└── Prediction.yml

Copy UNetpp.py and UnNormalize.py from:

KonfAI/UNetpp.py 
KonfAI/UnNormalize.py 

Copy Prediction.yml from:

Task_1/AB-TH/Prediction.yml

(Use the HN version if running Head & Neck.)

3. Run inference (AB-TH example)

konfai PREDICTION -y --gpu 0 \
  --models Task_1/AB-TH/CV_0.pt Task_1/AB-TH/CV_1.pt Task_1/AB-TH/CV_2.pt Task_1/AB-TH/CV_3.pt Task_1/AB-TH/CV_4.pt

For HN, replace the path accordingly:

--models Task_1/HN/CV_0.pt Task_1/HN/CV_1.pt Task_1/HN/CV_2.pt Task_1/HN/CV_3.pt Task_1/HN/CV_4.pt

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🛠️ How to Reproduce Training

Training is performed in two phases:

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🔹 Phase 1 — Pretraining on all regions

Train a generic model on the full dataset (AB, TH, HN combined) (Fold 0 example):

konfai TRAIN -y --gpu 0 --config KonfAI/Plan/Phase_1/Config0.yml

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🔹 Phase 2 — Region-specific fine-tuning

Fine-tune the Phase 1 model separately for each anatomical region.

Abdomen/Thorax (AB-TH) — Fold 0 example:

konfai RESUME -y --gpu 0 \
  --config KonfAI/Plan/Phase_2/AB-TH/Config0.yml \
  --model Phase1.pt

Head & Neck (HN) — Fold 0 example:

konfai RESUME -y --gpu 0 \
  --config KonfAI/Plan/Phase_2/HN/Config0.yml \
  --model Phase1.pt

Replace Phase1.pt with the checkpoint from Phase 1 (best model from Fold 0).

📌 Poster presented at MICCAI 2025, Daejeon