Abstract #3728
Noise-compensated bias correction of MRI via a stochastically fully-connected conditional random field model
Ameneh Boroomand 1 , Mohammad Javad Shafiee, 1 , Alexander Wong 1 , Farzad Khalvati 2 , Paul Fieguth 1 , and Masoom Haider 3
1
System Design Engineering, University of
Waterloo, Waterloo, Ontario, Canada,
2
Medical
Imaging, University of Toronto, Toronto, Ontario,
Canada,
3
Sunnybrook Health Sciences Centre,
Toronto, Ontario, Canada
The bias field inhomogeneity in Magnetic Resonance
Imaging (MRI) often makes difficulties for the
physicians who interpret and analyze the MR images. One
important challenging aspect of the most bias field
correction methods is the presence of MRI noise which
should be handled. Here, we propose a Bayesian based
image reconstruction framework which concurrently
corrects for the MRI bias field as well as compensates
for MRI noise in the final reconstructed MR image.
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