FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging

Radhika Tibrewala, Tarun Dutt, Angela Tong, Luke Ginocchio, Riccardo Lattanzi, Mahesh B. Keerthivasan, Steven H. Baete, Sumit Chopra, Yvonne W. Lui, Daniel K. Sodickson, Hersh Chandarana, Patricia M. Johnson

Research output: Contribution to journalArticlepeer-review


Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use. In May 2023, fastMRI was expanded to include biparametric (T2- and diffusion-weighted) prostate MRI data from a clinical population. Biparametric MRI plays a vital role in the diagnosis and management of prostate cancer. Advances in imaging methods, such as reconstructing under-sampled data from accelerated acquisitions, can improve cost-effectiveness and accessibility of prostate MRI. Raw k-space data, reconstructed images and slice, volume and exam level annotations for likelihood of prostate cancer are provided in this dataset for 47468 slices corresponding to 1560 volumes from 312 patients. This dataset facilitates AI and algorithm development for prostate image reconstruction, with the ultimate goal of enhancing prostate cancer diagnosis.

Original languageEnglish (US)
Article number404
JournalScientific Data
Issue number1
StatePublished - Dec 2024

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences


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