Adaptive Compressed Sensing MRI with Unsupervised Learning

Dalca, and Mert R. Sabuncu In compressed sensing MRI, k-space measurements are under-sampled to achieve accelerated scan times. There are two fundamental problems in compressed sensing MRI: (1) where to sample and (2) how to reconstruct. In this paper, we tackle both problems simultaneously, using a novel unsupervised, end-to-end learning framework, called LOUPE. Our method trains a neural network model on a set of full-resolution MRI scans, which are retrospectively under-sampled and forwarded to an antialiasing model that computes a reconstruction, which is in turn compared with the input. In our experiments, we demonstrate that LOUPEoptimized under-sampling masks are data-dependent, varying significantly with the imaged anatomy, and perform well with different reconstruction methods. We present empirical results obtained with a large-scale, publicly available knee MRI dataset, where LOUPE offered the most superior reconstruction quality across different conditions. Even with an aggressive 8-fold acceleration rate, LOUPE's reconstructions contained much of the anatomical detail that was missed by alternative masks and reconstruction methods. Our experiments also show how LOUPE yielded optimal under-sampling patterns that were significantly different for brain vs knee MRI scans. I NTRODUCTION M AGNETIC Resonance Imaging (MRI) is a ubiquitous, noninvasive, and versatile biomedical imaging technology. A central challenge in MRI is long scan times, which constrains accessibility and increases costs. One remedy is to accelerate MRI via compressed sensing [1], [2]. In compressed sensing MRI, k-space data (i.e., the Fourier transform of the image) is sampled below the Nyquist-Shannon rate [1], which is often referred to as "under-sampling." Given an under-sampled set of measurements, the objective is to "reconstruct" the full-resolution MRI.