Single-pass Object-adaptive Data Undersampling and Reconstruction for MRI
This work addresses the need for faster MRI scans with maintained image quality, offering an incremental improvement through adaptive sampling and joint training.
The authors tackled the problem of accelerating MRI data acquisition by proposing a data-driven sampler, MNet, which uses limited low-frequency k-space data to predict object-specific undersampling patterns in a single pass, achieving superior image reconstruction performance at fourfold and eightfold acceleration compared to existing schemes.
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.