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