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
• 💻 Code & documentation: 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):

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:

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 for details.