ImpactSynth — Synthetic CT (sCT) from MR / CBCT

Whole-body synthetic CT generation from MR or CBCT, built with KonfAI. Top-ranking models from the SynthRAD challenge (Tasks 1 & 2).

🧩 Models

Model	Input	Output	Description	Ensemble
MR	MR	sCT	MR → sCT (SynthRAD Task 1)	5
CBCT	CBCT	sCT	CBCT → sCT (SynthRAD Task 2)	5
MR_CBCT	MR / CBCT	sCT	Joint MR/CBCT → sCT	5
Finetune	CBCT	sCT	BICMAC fine-tuned CBCT → sCT	5

2.5D UNet++ · patch [1, 512, 512] · 5-model ensemble.

🚀 Usage

pip install impact_synth_konfai
impact-synth-konfai synthesize MR -i input_mr.nii.gz -o output/

• Generic runner: konfai-apps infer VBoussot/ImpactSynth:MR -i input_mr.nii.gz -o output/
• Interactive: SlicerKonfAI — the ⚙ Advanced dialog overrides patch size and batch size.

⚡ Performance & VRAM

Benchmarked on a single NVIDIA RTX PRO 5000 (24 GB) with a real whole-body MR (295 × 259 × 219, 2 mm). The batch size is auto-selected from your free GPU VRAM.

Free VRAM	Batch (auto)	Peak VRAM	Time / case
8 GB	16	~7.6 GB	—
16 GB	28	~15 GB	—
24 GB	32	~16 GB	~24 s

sCT generation keeps system RAM ~2 GB. The plan leaves memory headroom — filling the card with a larger batch saturates the allocator and slows inference. A full 5-model ensemble runs in ~82 s on the same card. Override with --patch-size / --batch-size.

🔗 Links & Citation

• 🧠 KonfAI: github.com/vboussot/KonfAI
• 📦 PyPI: impact_synth_konfai
• 📄 Paper: arXiv:2510.21358