SinoCT
Dataset Description
This dataset contains over 9,000 head CT scans, each labeled as normal or abnormal. Each scan contains a reconstructed image (stored in our institution’s PACS and saved as DICOMs) and a corresponding sinogram (simulated via GE’s CatSim software and saved as numpy arrays). The reconstructed images are 512x512 pixels with a variable number of axial slices per scan. The sinograms are 984x888 pixels with a variable number of axial slices per scan. The full dataset is 1.3T. We retrospectively collected the head CT scans (acquired between 2001 – 2014) from our institution’s PACS, selected according to the following criteria: non-contrast CT of the head acquired in axial mode on a GE scanner and pixel spacing of 0.49 or 0.60 mm in the axial plane. The reading radiologist designated each CT scan as normal or abnormal at the time of original image interpretation; these designations were given as part of standard clinical procedure and not modified during dataset curation. We used GE’s CatSim, a validated simulation software for GE machines, to simulate high-fidelity sinograms of each head CT scan. If you use this dataset, please cite our paper (https://pubs.rsna.org/doi/abs/10.1148/ryai.2021200229). Additionally, part of this dataset was used in the RSNA Intracranial Hemorrhage Detection Challenge (https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection). Labels for hemorrhage can be found in the Kaggle download.