A Review of Methods for Correction of Intensity Inhomogeneity in MRI

Abstract

Medical image acquisition devices provide a vast amount of anatomical and functional information, which facilitate and improve diagnosis and patient treatment, especially when supported by modern quantitative image analysis methods. However, modality specific image artifacts, such as the phenomena of intensity inhomogeneity in magnetic resonance images (MRI), are still prominent and can adversely affect quantitative image analysis. In this paper, numerous methods that have been developed to reduce or eliminate intensity inhomogeneities in MRI are reviewed. First, the methods are classified according to the inhomogeneity correction strategy. Next, different qualitative and quantitative evaluation approaches are reviewed. Third, 60 relevant publications are categorized according to several features and analyzed so as to reveal major trends, popularity, evaluation strategies and applications. Finally, key evaluation issues and future development of the inhomogeneity correction field, supported by the results of the analysis, are discussed.

Accurate segmentation of subcutaneous and intermuscular adipose tissue from MR images of the thigh

“…The quality of these maps is critical, because they represent the first step in the algorithm and will affect all the subsequent steps. The map quality mainly depends on the presence of intensity inhomogeneities, which could eventually be corrected by several preprocessing algorithms (23). The well-established N3 algorithm (24) was successfully used for thigh images preprocessing by Manini et al (6).…”

Segmentation of Brain MRI

“…Although intensity inhomogeneity is usually hardly noticeable to a human observer, many medical image analysis methods, such as segmentation and registration, are highly sensitive to the spurious variations of image intensities. This is why a large number of methods for the correction of intensity inhomogeneity in MR images have been proposed in the past (Vovk et al, 2007). Early publications on MRI intensity inhomogeneity correction date back to 1986 (Haselgrove & Prammer, 1986;McVeigh et al, 1986).…”

“…Since then, sources of intensity inhomogeneity in MRI have been studied extensively (Alecci et al, 2001;Keiper et al, 1998;Liang & Lauterbur, 2000;Simmons et al, 1994) and can be generally divided into two groups: prospective methods and retrospective methods. According to the classification proposed by U. Vovk (Vovk et al, 2007), we may further classify the prospective methods into those that are based on phantoms, multi-coils, and special sequences. The retrospective methods are further classified into filtering, surface fitting, segmentation-based, and histogram-based, etc.…”

Discriminative, Semantic Segmentation of Brain Tissue in MR Images

“…This MR image formation model has been used frequently [16,17] as it is simple and known to be consistent with the inhomogeneous sensitivity of the reception coil. Since the bias field is smoothly varying in space, we adopt a low-order polynomial model:…”