README.md

---
license: cc-by-4.0
task_categories:
  - image-to-image
modality:
  - medical-imaging
tags:
  - PET
  - CT
  - MRI
  - attenuation-correction
  - medical-imaging
  - challenge
pretty_name: BIC-MAC Challenge Dataset
size_categories:
  - 100<n<1K
---

# BIC-MAC Challenge Dataset

Dataset for the **Big Cross-Modal Attenuation Correction (BIC-MAC)** challenge — a medical imaging challenge where participants synthesize pseudo-CT images from non-attenuation-corrected PET (NAC-PET), whole-body MRI, and 2D topograms, enabling CT-less PET reconstruction.

- 🌐 **Challenge website:** [bic-mac-challenge.github.io](https://bic-mac-challenge.github.io/)
- 💻 **Code & documentation:** [github.com/bic-mac-challenge/challenge-codebase](https://github.com/bic-mac-challenge/challenge-codebase)

## Dataset

100 healthy volunteers acquired on a Siemens Biograph Vision Quadra (PET/CT) and MAGNETOM Vida (MRI).

| Split | Subjects | Contents |
|-------|----------|----------|
| `train/` (labeled) | 8 | features + CT labels + sinograms + PET labels |
| `train/` (unlabeled) | 67 | features + CT labels |
| `val/` | 4 | features + sinograms |

Each subject directory contains:
- `features/` — NAC-PET, whole-body DIXON MRI (4 chunks × 2 phases + combined), 2D topogram, demographics
- `ct-label/` — ground-truth CT in HU, body/organ/face segmentations
- `recon/` — STIR sinogram files and reconstruction metadata *(labeled train + val only)*
- `pet-label/` — ground-truth attenuation-corrected PET, body/organ segmentations *(labeled train only)*

## Download

**Full dataset (~650 GB):**

```python
from huggingface_hub import snapshot_download

snapshot_download(repo_id="hinge/BIC-MAC", repo_type="dataset", local_dir="./bic-mac-data")
```

**Without sinogram data (~35 GB):**

The `recon/` folders contain raw sinogram files and account for ~95% of the dataset size. If you only need the imaging data for model training (CT synthesis from NAC-PET/MRI), you can omit them:

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="hinge/BIC-MAC",
    repo_type="dataset",
    local_dir="./bic-mac-data",
    ignore_patterns=["*/recon/*"],
)
```

The sinogram data is required to run the reconstruction pipeline and evaluate PET metrics locally. See the [challenge codebase](https://github.com/bic-mac-challenge/challenge-codebase) for details.