provides an open platform for researchers to share magnetic resonance imaging (MRI) raw datasets. The website was first created through a collaboration between Prof. Michael Lustig
's group at UC Berkeley and Prof. Shreyas Vasanawala
's group at Stanford's Lucile Packard Children's Hospital. Datasets on this website can be used for a wide variety of applications. Here, we highlight two applications we have in mind: compressed sensing, and machine learning.
Compressed sensing in MRI reconstruction is used to speed up acquisition by exploiting image sparsity in transform domains. The tradeoff in using these techniques is that the image reconstruction step becomes computationally intensive, with a wide variety of noise-like artifacts arising when pushing the limits of acceleration. Different reconstruction method produces different tradeoffs. Researcher can use datasets on this website to validate and compare their reconstruction methods.
Machine learning in MRI reconstruction has the potential to learn the underlying image prior to improve reconstruction quality, and/or the direct mapping from raw data to images. Current machine learning techniques require large number of datasets for training, yet the number of public MRI raw datasets is limited. Using preprocessed MR magnitude images, or small number of datasets for training can result in much inferior reconstructions than training directly from raw data. With contributions from many researchers, we hope that this website can provide more datasets to train accurate machine learning models.
Anita Flynn, Frank Ong, Gabriel Nahum, Joseph Cheng, Michael Lustig, Joyce Toh, Patrick Virtue, Shahab Amin, Shreyas Vasanawala, and Umar Tariq.
We thank the support of NIH R01-EB009690 and an AWS research grant.