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Abstract #2549

Implementation of Compressed Sensing for Online Reconstruction

Cheng Ouyang1,2, Tobia Wech1,3, Li Pan1,4

1Center for Applied Medical Imaging, Siemens Corporate Research, Baltimore, MD, United States; 2Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States; 3Institute of Radiology, University of Wuerzburg, Wuerzburg, Bavaria, Germany; 4Department of Radiology & Radiological Science, Johns Hopkins University, Baltimore, MD, United States

Compressed sensing (CS) has been proposed as a technique to enable acquisition and reconstruction of images that are sparse or compressible with little to no loss of image quality. However, the existing work in the CS literature has been focused on offline reconstruction and simulation, partially due to the concern of the time-consuming steps of the sparsifying transform and non-linear iterative reconstruction. In this work, we demonstrated the feasibility of an online implementation of compressed sensing to achieve real-time image reconstruction on a clinical scanner. The performance of the TVCMRI algorithm used in the online implementation was effective in producing reconstructed images close to ground truth with rapid reconstruction speed.