Inhomogeneity correction and the analytic anisotropic algorithm

Inhomogeneity correction and the analytic anisotropic algorithm

The ability of the analytic anisotropic algorithm (AAA), a superposition- convolution algorithm implemented in the Eclipse (Varian Medical Systems, Palo Alto, CA) treatment planning system (TPS), to accurately account for the presence of inhomogeneities in simple geometries is examined. The goal of 2% accuracy, as set out by the American Association of Physicists in Medicine Task Group 65, serv...

RF Inhomogeneity Correction Algorithm in Magnetic Resonance Imaging

MR images usually present grey level inhomogeneities which are a problem of significant importance. Eliminating these inhomogeneities is not an easy problem and has been studied and discussed in several previous publications. Most of those approaches are based on segmentation processes. The algorithm presented in this paper has the advantage that it does not involve any segmentation step. Inste...

Retrospective Evaluation and Correction of Intensity Inhomogeneity

SOM for intensity inhomogeneity correction in MRI

Given an appropriate imaging resolution, a common Magnetic Resonance Imaging (MRI) model assumes that object under study is composed of piecewise constant materials, so that MRI produces piecewise constant images. The intensity inhomogeneity (IIH) is modeled by a multiplicative inhomogeneity field. It is due to the spatial inhomogeneity in the excitatory Radio Frequency (RF) signal and other ef...

Inhomogeneity correction for magnetic resonance images with fuzzy C-mean algorithm

Abstract: Segmentation of magnetic resonance (MR) images plays an important role in quantitative analysis of brain tissue morphology and pathology. However, the inherent effect of image-intensity inhomogeneity renders a challenging problem and must be considered in any segmentation method. For example, the adaptive fuzzy c-mean (AFCM) image segmentation algorithm proposed by Pham and Prince can...