Retrospective correction of intensity inhomogeneities in MRI

Abstract

Medical imaging data sets are often corrupted by multiplicative inhomogeneities, often referred to as nonuniformities or intensity variations, that hamper the use of quantitative analyses. The authors describe an automatic technique that not only improves the worst situations, such as those encountered with magnetic resonance imaging (MRI) surface coils, but also corrects typical inhomogeneities encountered in routine volume data sets, such as MRI head scans, without generating additional artifact. Because the technique uses only the patient data set, the technique can be applied retrospectively to all data sets, and corrects both patient independent effects, such as rf coil design, and patient dependent effects, such as attenuation of overlying tissue experienced both in high field MRI and X-ray computed tomography (CT). The authors show results for several MRI imaging situations including thorax, head, and breast. Following such corrections, region of interest analyses, volume histograms, and thresholding techniques are more meaningful. The value of such correction algorithms may increase dramatically with increased use of high field strength magnets and associated patient-dependent rf attenuation in overlying tissues.

A Variational Level Set Approach to Segmentation and Bias Correction of Images with Intensity Inhomogeneity

“…Therefore, we define an energy , i.e. (5) Directly minimizing the energy with the partition as a variable is not convenient. We will use one or multiple level set functions to represent a partition .…”

“…Therefore, they can also remove intensity inhomogeneities regardless of their sources. Early retrospective methods include those based on filtering [3], surface fitting [4,5], and histogram [6]. Segmentation based methods [1,7,8,9] are more attractive, as they unify segmentation and bias correction within a single framework.…”

Automatic Segmentation of Adipose Tissue from Thigh Magnetic Resonance Images

“…To obtain decent samples, manual selection and segmentation has been largely involved in many works, which were trivial and less accurate [6]. The literature is quite sparse on automatic methods to select training pixels [7].…”

MRI Tissue Classification and Bias Field Estimation Based on Coherent Local Intensity Clustering: A Unified Energy Minimization Framework

“…In contrast, retrospective methods rely only on the information in the acquired images, and thus they can remove intensity inhomogeneities regardless of their sources. Retrospective methods include those based on filtering [1,2,3,4], surface fitting [5,6,7,8], histogram [9,10], and segmentation [11,12,13,14,15,16,17].…”