  Diffusion weighted magnetic resonance imaging (DW-MR) is a powerful tool in
imaging-based prostate cancer screening and detection. Endorectal coils are
commonly used in DW-MR imaging to improve the signal-to-noise ratio (SNR) of
the acquisition, at the expense of significant intensity inhomogeneities (bias
field) that worsens as we move away from the endorectal coil. The presence of
bias field can have a significant negative impact on the accuracy of different
image analysis tasks, as well as prostate tumor localization, thus leading to
increased inter- and intra-observer variability. Retrospective bias correction
approaches are introduced as a more efficient way of bias correction compared
to the prospective methods such that they correct for both of the scanner and
anatomy-related bias fields in MR imaging. Previously proposed retrospective
bias field correction methods suffer from undesired noise amplification that
can reduce the quality of bias-corrected DW-MR image. Here, we propose a
unified data reconstruction approach that enables joint compensation of bias
field as well as data noise in DW-MR imaging. The proposed noise-compensated,
bias-corrected (NCBC) data reconstruction method takes advantage of a novel
stochastically fully connected joint conditional random field (SFC-JCRF) model
to mitigate the effects of data noise and bias field in the reconstructed MR
data. The proposed NCBC reconstruction method was tested on synthetic DW-MR
data, physical DW-phantom as well as real DW-MR data all acquired using
endorectal MR coil. Both qualitative and quantitative analysis illustrated that
the proposed NCBC method can achieve improved image quality when compared to
other tested bias correction methods. As such, the proposed NCBC method may
have potential as a useful retrospective approach for improving the consistency
of image interpretations.
