Bias field correction for improved compressed sensing reconstruction in parallel magnetic resonance imaging

Abstract

Parallel imaging and compressed sensing (PICS) may accelerate magnetic resonance imaging (MRI) acquisition with advanced reconstruction algorithms from under-sampled data set. However, bias field effects are often present in reconstructed MRI images due to hardware limitation and object property, which might lead to reconstruction imperfection with conventional PICS reconstruction due to altered image sparsity. In this paper, the MR signal bias field effects is modeled, and it was found that the bias field effects would induce reconstruction artifacts in the low-signal-intensity area. Then it was proposed to add a bias correction step into the PICS reconstruction framework to improve the reconstruction, and the proposed method was evaluated the proposed method via both simulation and in vivo study. It was then shown that the proposed method leads to improved image reconstruction in the low-signal-intensity area, with little extra computational effort needed compared to standard PICS reconstruction. This method could be readily extended to other signal processing area where the signal inhomogeneity problem is present and signal sparsity is exploited.