Keywords: Machine Learning/Artificial Intelligence, Cancer
Motivation: There is a lack of publicly available k-space data of breast dynamic contrast-enhanced (DCE) MRI that can be used for development of image reconstruction and machine learning methods for breast MRI.
Goal(s): We aim to make a publicly available radial k-space dataset of breast DCE-MRI which will promote development of fast and quantitative breast imaging methods.
Approach: Data of women undergoing routine diagnostic breast DCE-MRI exams have been acquired using a stack-of-stars radial imaging at 3T.
Results: Our fastMRI breast dataset includes radial k-space data and case-level labels for 275 cases (70 malignant, 158 benign and 47 no-lesion cases).
Impact: This work introduces the first large-scale dataset of radial k-space data for breast DCE-MRI acquired in diagnostic breast MRI exams. Having this dataset and accompanying reconstruction code publicly available, will support research and development of fast and quantitative breast DCE-MRI.
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