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Interplay between bias field correction, intensity standardization, and noise filtering for T2-weighted MRI

Magnetic Resonance Imaging (MRI) is known to be significantly affected by a number of acquisition artifacts, such as intensity non-standardness, bias field, and Gaussian noise. These artifacts degrade MR image quality significantly, obfuscating anatomical and physiological detail and hence need to be corrected for to facilitate application of computerized analysis techniques such as segmentation, registration, and classification. Specifically, algorithms are required to correct for bias field (intensity inhomogeneity), intensity non-standardness (drift in tissue intensities across patient acquisitions), and Gaussian noise, an artifact that significantly affects and blurs tissue boundaries (resulting in poor gradients). While clearly one needs to correct for all these artifacts, the exact sequence in which all three operations need to be applied in order to maximize MR image quality has not been explored. In this paper, we empirically evaluate the interplay between distinct algorithms for bias field correction (BFC), intensity standardization (IS), and noise filtering (NF) to study the effect of these operations on image quality in the context of 3 Tesla T2-weighted (T2w) prostate MRI. 7 different sequences comprising combinations of BFC, IS, and NF were quantitatively evaluated in terms of the percent coefficient of variation (%CV), a statistic which attempts to quantify the intensity inhomogeneity within a region of interest (prostate). The different combinations were also independently evaluated in the context of a classifier scheme for detection of prostate cancer on high resolution in vivo T2w prostate MRI. A secondary contribution of this work is a novel evaluation measure for quantifying the level of intensity non-standardness, called difference of modes (DoM). Experimental evaluation of the different sequences of operations across 22 patient datasets revealed that the sequence of BFC, followed by NF, and IS provided the best image quality in terms of %CV as well as classifier accuracy. The DoM measure was able to accurately capture the level of intensity non-standardness present in the images resulting from the different sequences of operations.