SWI post processing using granularity controlled edge-preserved denoising of multichannel GRE images

摘要

The influence of granularities in the background suppressed phase of susceptibility-weighted images (SWI) and susceptibility-weighted angiogram (SWAN) becomes significant when the susceptibility based contrast is enhanced by exponential weighting of the high-pass filtered phase. Furthermore, the effect of noise due to the inherently low signal-to-noise ratio resulting from high-resolution SWI/SWAN acquisition, can be minimized by application of edge-preserved denoising of the channel phase images without loss of venous structural details. Simultaneous reduction of granularity effects with edge-preserved denoising is achieved using the proposed granularity controlled adaptive edge-preserved regularization (GRADER). In this approach, the edge-preserving cost is minimized with respect to the desired channel phase image and an unknown scale parameter that adaptively tunes the high-pass filter. The algorithm is implemented using quasi-Newton type iterations, with the scale parameter updated using a search procedure in each alternating minimization step. The iterations are stopped once the scale parameter converges to a steady state value. Extension of GRADER to parallel MRI (pMRI) by processing the real and imaginary components of complex channel images (IR-GRADER) results in enhanced susceptibility-related contrast-to-noise ratio of the magnitude SWI, leading to improved visualization of superficial veins and deep gray matter structures.