---
task_categories:
  - image-to-image
modality:
  - medical-imaging
tags:
  - PET
  - CT
  - MRI
  - generative-ai
  - attenuation-correction
  - medical-imaging
  - challenge
  - MICCAI
pretty_name: BIC-MAC Challenge Dataset
size_categories:
  - 100<n<1K
---

To access this dataset, please register at [http://bic-mac-challenge.github.io/](http://bic-mac-challenge.github.io/). 

# 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

99 healthy volunteers (20 test, 75 train, 4 validation)

| Split | Subjects | Contents |
|-------|----------|----------|
| `train/` (full) | 8 | features + CT labels + sinograms + PET labels |
| `train/` (no recon) | 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 (643 GB):**

```bash
hf download DEPICT-RH/BIC-MAC \
    --repo-type dataset \
    --local-dir ./bic-mac-data
```

**Without sinogram data (45 GB):**

The `recon/` folders contain raw sinogram files and account for 93% of the dataset size. For training the CT-synthesis model, you only need the NIfTI files, and so the sinograms can be omitted to save space. The sinograms are only needed to run the reconstruction pipeline and evaluate PET metrics locally. See the [challenge codebase](https://github.com/bic-mac-challenge/challenge-codebase) for details.

```bash
hf download DEPICT-RH/BIC-MAC \
    --repo-type dataset \
    --local-dir ./bic-mac-data \
    --exclude "*/recon/*"
```

**Sinogram data for óne subject (50gb)**

To save space, you can download the sinogram folders for individual cases.

```bash
hf download DEPICT-RH/BIC-MAC \
    --repo-type dataset \
    --local-dir ./bic-mac-data \
    --include "train/sub-000/recon/*"
```

The following subjects have recon folders:
- `train/`: `sub-000`, `sub-001`, `sub-002`, `sub-005`, `sub-006`, `sub-008` ,`sub-013` ,`sub-014`
- `val/`: `sub-004` ,`sub-009` ,`sub-010` ,`sub-018`