---
license: apache-2.0
library_name: konfai
pipeline_tag: image-to-image
tags:
- medical-imaging
- synthetic-ct
- image-synthesis
- radiotherapy
- mri
- cbct
- konfai
---

# ImpactSynth — Synthetic CT (sCT) from MR / CBCT

Whole-body **synthetic CT** generation from **MR** or **CBCT**, built with
[**KonfAI**](https://github.com/vboussot/KonfAI). Top-ranking models from the
**[SynthRAD](https://synthrad2023.grand-challenge.org/)** 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

```bash
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**](https://github.com/vboussot/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](https://github.com/vboussot/KonfAI)
- 📦 **PyPI:** [impact_synth_konfai](https://pypi.org/project/impact_synth_konfai/)
- 📄 **Paper:** [arXiv:2510.21358](https://arxiv.org/abs/2510.21358)