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Abstract #3405

Enhanced reconstruction of compressive sensing MRI via cross-domain stochastically fully-connected random field model

Edward Li 1 , Mohammad Javad Shafiee 1 , Audrey Chung 1 , Farzad Khalvati 2 , Alexander Wong 1 , and Masoom A Haider 3

1 Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada, 2 Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada, 3 Sunnybrook Health Sciences Center, Toronto, Ontario, Canada

Compressive sensing reduces MRI acquisition times but requires advanced sparse reconstruction algorithm to produce high-quality MR images. We propose a novel sparse reconstruction method using a cross-domain stochastically fully-connected random field (CD-SFCRF) for improved reconstruction from compressive sensing MRI data. Peak-to-peak signal-to-noise ratio (PSNR) analysis of CD-SFCRF and other methods using a prostate training phantom demonstrate that CD-SFCRF has the highest PSNR across all under-sampling ratios of radial MRI acquisitions. A visual comparison using real patient cases illustrate that CD-SFCRF can improve fine tissue detail and contrast preservation while eliminating under-sampling artifacts.

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