FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging

Radhika Tibrewala, Tarun Dutt, Angela Tong, Luke Ginocchio, Mahesh B Keerthivasan +6 more
4/18/2023
physics.med-phcs.LGeess.IV

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Abstract

The fastMRI brain and knee dataset has enabled significant advances in exploring reconstruction methods for improving speed and image quality for Magnetic Resonance Imaging (MRI) via novel, clinically relevant reconstruction approaches. In this study, we describe the April 2023 expansion of the fastMRI dataset to include biparametric prostate MRI data acquired on a clinical population. The dataset consists of raw k-space and reconstructed images for T2-weighted and diffusion-weighted sequences along with slice-level labels that indicate the presence and grade of prostate cancer. As has been the case with fastMRI, increasing accessibility to raw prostate MRI data will further facilitate research in MR image reconstruction and evaluation with the larger goal of improving the utility of MRI for prostate cancer detection and evaluation. The dataset is available at https://fastmri.med.nyu.edu.

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Cite this paper

@article{tibrewala2023fastmri,
  title  = {FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging},
  author = {Radhika Tibrewala and Tarun Dutt and Angela Tong and Luke Ginocchio and Mahesh B Keerthivasan and Steven H Baete and Sumit Chopra and Yvonne W Lui and Daniel K Sodickson and Hersh Chandarana and Patricia M Johnson},
  year   = {2023},
  eprint = {2304.09254},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.09254v1}
}

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