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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