Beyond the g-factor limit in sensitivity encoding using joint histogram entropy.

Abstract

The maximum practical speed-up that can be achieved using parallel imaging methods is widely accepted to be limited by g-factor noise. An approximate expression for the g-factor noise as a function of the principal eigenvector of the inverse sensitivity matrix is derived. This formulation allows g-factor enhanced noise to be reduced by a constrained optimization procedure with joint image histogram entropy between a reference image and a SENSE image as an image quality metric. The reference image does not need to have identical resolution or contrast. The reference image may also be used for coil calibration. The limits of the method are explored using simulated and real array coil data with high g-factor using a variety of contrast and resolution combinations. The method preserves image structure, contrast, and lesions even when these were not observable in the reference data. In all cases g-factor was dramatically reduced.