Keywords: Prostate, Prostate, k-space data, public
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.
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