FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging
Abstract 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 rest...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-03252-w |
| _version_ | 1797199721268248576 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Radhika Tibrewala |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-24T07:20:15Z |
| format | Article |
| id | doaj.art-ecf34d858c24409eb260e6fc114f54ee |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-04-24T07:20:15Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-ecf34d858c24409eb260e6fc114f54ee2024-04-21T11:08:10ZengNature PortfolioScientific Data2052-44632024-04-011111910.1038/s41597-024-03252-wFastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imagingRadhika Tibrewala0Tarun Dutt1Angela Tong2Luke Ginocchio3Riccardo Lattanzi4Mahesh B. Keerthivasan5Steven H. Baete6Sumit Chopra7Yvonne W. Lui8Daniel K. Sodickson9Hersh Chandarana10Patricia M. Johnson11Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of MedicineBernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of MedicineCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of MedicineBernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of MedicineCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of MedicineBernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of MedicineCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of MedicineBernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of MedicineCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of MedicineCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of MedicineCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of MedicineCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of MedicineAbstract 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.https://doi.org/10.1038/s41597-024-03252-w |
| spellingShingle | 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 FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging Scientific Data |
| title | FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging |
| title_full | FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging |
| title_fullStr | FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging |
| title_full_unstemmed | FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging |
| title_short | FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging |
| title_sort | fastmri prostate a public biparametric mri dataset to advance machine learning for prostate cancer imaging |
| url | https://doi.org/10.1038/s41597-024-03252-w |
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