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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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