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A statistical approach for combination of channel phases is developed foroptimizing the Contrast-to-Noise Ratio (CNR) in Susceptibility Weighted Images(SWI) acquired using autocalibrating partially parallel techniques. Theunwrapped phase images of each coil are filtered using local random field basedprobabilistic weights, derived using energy functions representative of noisysensitivity and tissue information pertaining to venous structure in theindividual channel phase images. The channel energy functions are obtained asfunctions of local image intensities, first or second order clique phasedifference and a threshold scaling parameter dependent on the input noiselevel. Whereas the expectation of the individual energy functions with respectto the noise distribution in clique phase differences is to be maximized foroptimal filtering, the expectation of tissue energy function decreases andnoise energy function increases with increase in threshold scale parameter. Theoptimum scaling parameter is shown to occur at the point where expectations ofboth energy functions contribute to the largest possible extent. It is shownthat implementation of the filter in the same lines as that of IteratedConditional Modes (ICM) algorithm provides structural enhancement in the coilcombined phase, with reduced noise amplification. Application to simulated andin vivo multi-channel SWI shows that CNR of combined phase obtained usingMAP-MRF filter is higher as compared to that of coil combination using weightedaverage.

A MAP-MRF filter for phase-sensitive coil combination in autocalibrating partially parallel susceptibility weighted MRI