  In the past few years, deep learning-based methods have demonstrated enormous
success for solving inverse problems in medical imaging. In this work, we
address the following question:\textit{Given a set of measurements obtained
from real imaging experiments, what is the best way to use a learnable model
and the physics of the modality to solve the inverse problem and reconstruct
the latent image?} Standard supervised learning based methods approach this
problem by collecting data sets of known latent images and their corresponding
measurements. However, these methods are often impractical due to the lack of
availability of appropriately sized training sets, and, more generally, due to
the inherent difficulty in measuring the "groundtruth" latent image. In light
of this, we propose a self-supervised approach to training inverse models in
medical imaging in the absence of aligned data. Our method only requiring
access to the measurements and the forward model at training. We showcase its
effectiveness on inverse problems arising in accelerated magnetic resonance
imaging (MRI).
