Noise-Compensated, Bias-Corrected Diffusion Weighted Endorectal Magnetic Resonance Imaging via a Stochastically Fully-Connected Joint Conditional Random Field Model

Abstract

Abstract-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, biascorrected (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.

Quality Comparison of 3 Tesla multiparametric MRI of the prostate using a flexible surface receiver coil versus conventional surface coil plus endorectal coil setup

“…These observations have been confirmed by several published studies [9,10,13]. Different correction algorithms have been developed to compensate signal inhomogeneity, but these may lead to noise level variation and an increase of acquisition time [11,12,29]. It has to be considered that rectal gas if present may lead to similar susceptibility artifacts as gas in ERC.…”

A review of Methods for Bias Correction in Medical Images

“…This model is useful in in spin-echo T(1) images, but need some special parameters and references, and the results is still unreliable. Recently, various state-of-the-art techniques sill depend on the three simple image formulation models (Seshamani, Cheng, Fogtmann, Thomason, & Studholme, 2014;Nascimento, Frery, & Cintra, 2014;Lui, Modhafar, Glaister, Wong, & Haider, 2014;Boroomand et al, 2015;Banerjee & Maji, 2015;Miao et al, 2016;Chang et al, 2016), further validates the general applicability of these three models.…”

Two-centre comparative experimental study of biparametric MRI at 3.0 T with and without endorectal coil using kiwifruit (Actinidia deliciosa) as a phantom for human prostate

“…Also, the assumption that signal-to-noise ratio (SNR) would improve with ERC application, due to closer proximity of the receiving coil to the prostate, has been challenged in a recent clinical study [6]. In addition, ERCs induce significant inhomogeneity in signal intensity [12, 13]. Correction algorithms applied within the scanner may lead to noise amplification and noise level variation [12].…”