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Improving speed and image quality of Magnetic Resonance Imaging (MRI) vianovel reconstruction approaches remains one of the highest impact applicationsfor deep learning in medical imaging. The fastMRI dataset, unique in that itcontains large volumes of raw MRI data, has enabled significant advances inaccelerating MRI using deep learning-based reconstruction methods. While theimpact of the fastMRI dataset on the field of medical imaging is unquestioned,the dataset currently lacks clinical expert pathology annotations, critical toaddressing clinically relevant reconstruction frameworks and exploringimportant questions regarding rendering of specific pathology using such novelapproaches. This work introduces fastMRI+, which consists of 16154subspecialist expert bounding box annotations and 13 study-level labels for 22different pathology categories on the fastMRI knee dataset, and 7570subspecialist expert bounding box annotations and 643 study-level labels for 30different pathology categories for the fastMRI brain dataset. The fastMRI+dataset is open access and aims to support further research and advancement ofmedical imaging in MRI reconstruction and beyond.

fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Data