  There is much recent interest in techniques to accelerate the data
acquisition process in MRI by acquiring limited measurements. Often
sophisticated reconstruction algorithms are deployed to maintain high image
quality in such settings. In this work, we propose a data-driven sampler using
a convolutional neural network, MNet, to provide object-specific sampling
patterns adaptive to each scanned object. The network observes very limited
low-frequency k-space data for each object and rapidly predicts the desired
undersampling pattern in one go that achieves high image reconstruction
quality. We propose an accompanying alternating-type training framework with a
mask-backward procedure that efficiently generates training labels for the
sampler network and jointly trains an image reconstruction network.
Experimental results on the fastMRI knee dataset demonstrate the ability of the
proposed learned undersampling network to generate object-specific masks at
fourfold and eightfold acceleration that achieve superior image reconstruction
performance than several existing schemes. The source code for the proposed
joint sampling and reconstruction learning framework is available at
https://github.com/zhishenhuang/mri.
