Abstract

Motivation: There is a lack of publicly available, raw k-space data for prostate MRI. Goal(s): To compile and release raw k-space data for clinical prostate MRI and demonstrate its utility for development of deep learning methods for image reconstruction and automated diagnosis. Approach: Biparametric MRI data from 312 patients with associated prostate cancer labels were added to the public fastMRI repository. Deep-learning models were trained on the data to reconstruct images from undersampled k-space and perform automated diagnosis of prostate cancer (PCa) on these images. Results: SSIM > 0.866 and AUC > 0.80 (test set) for the deep-learning reconstruction and automated PCa diagnosis respectively. Impact: Raw k-space data with clinical labels from fastMRI prostate will enable researchers to develop clinically relevant deep-learning reconstruction and automated diagnosis models which may ultimately advance the diagnosis and management of prostate cancer.