fastMRI Prostate: A Public, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging

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.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/3650
Primary Topic
Prostate Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
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article

fastMRI Prostate: A Public, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging

Daniel K. Sodickson, Luke Ginocchio, Steven H. Baete, Riccardo Lattanzi et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Prostate Cancer Diagnosis and Treatment
article

fastMRI Prostate: A Public, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging

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

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.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 60%
Prostate Cancer Diagnosis and Treatment
DOISourceOpenAlex
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fastMRI Prostate: A Public, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging — Daniel K. Sodickson, Luke Ginocchio, et al. · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (2024) | TGRS Research Map | TGRS