Dataset of synthetic whole-body bone scintigraphy scans representing three clinical conditions

We here provide an image dataset consisting of 1,000 synthetic whole-body bone scintigraphy scans (anterior projection) generated by a generative artificial intelligence model. This dataset consists of images representing three different clinical conditions: (1) bone uptake indicative of bone metastases, (2) cardiac uptake indicative of cardiac amyloidosis, and (3) none of the two.

The clinical condition (label) of each image is provided in the csv file:

Label 1: Bone uptake indicative of bone metastases (n=250 scans)

Label 2: Cardiac uptake indicative of cardiac amyloidosis (n=250 scans)

Label 0: None of the two (n=500 scans)

This synthetic dataset does not comprise real patient data. The provided synthetic images were created by a generative artificial intelligence model. The model was trained on bone scintigraphy scans (radiotracer: 99mTc-DPD) from 9,170 patients from the Vienna General Hospital collected as part of the clinical routine. The training data covered a wide range of different pathologies, scanners, and imaging protocols. Hence, the provided synthetic dataset represents real-world data without disclosing patient privacy.

More details about the dataset can be found in the corresponding paper (link added upon publication). Please cite this paper if you use the dataset.