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

arXiv: https://arxiv.org/abs/2304.09254 · HTML: https://mlsanity.com/paper/2304.09254/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 18 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [MoDL: Model Based Deep Learning Architecture for Inverse Problems](https://mlsanity.com/paper/1712.02862/) (2017)
- [Learning a Variational Network for Reconstruction of Accelerated MRI Data](https://mlsanity.com/paper/1704.00447/) (2017)
- [SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation](https://mlsanity.com/paper/2203.06823/) (2022)

## Built on by (most cited first, 9 of 9)

- [Generative Artificial Intelligence: A Systematic Review and Applications](https://mlsanity.com/paper/2405.11029/) (2024)
- [Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data](https://mlsanity.com/paper/2403.08728/) (2024)
- [CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction](https://mlsanity.com/paper/2309.10836/) (2023)
- [A study of why we need to reassess full reference image quality assessment with medical images](https://mlsanity.com/paper/2405.19097/) (2024)
- [Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data](https://mlsanity.com/paper/2312.10271/) (2023)
- [vSHARP: variable Splitting Half-quadratic Admm algorithm for Reconstruction of inverse-Problems](https://mlsanity.com/paper/2309.09954/) (2023)
- [PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation](https://mlsanity.com/paper/2504.07560/) (2025)
- [Resolution-Robust 3D MRI Reconstruction with 2D Diffusion Priors: Diverse-Resolution Training Outperforms Interpolation](https://mlsanity.com/paper/2412.18584/) (2024)
- [LUND-PROBE -- LUND Prostate Radiotherapy Open Benchmarking and Evaluation dataset](https://mlsanity.com/paper/2502.04493/) (2025)

## Abstract (arXiv)

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.