Cheukkai Hui1, Yu Xiang Zhou, Ponnada Narayana. 1. Department of Diagnostic and Interventional Imaging, University of Texas Medical School at Houston, Houston, Texas, USA.
Abstract
PURPOSE: To develop and implement a new approach for correcting the intensity inhomogeneity in magnetic resonance imaging (MRI) data. MATERIALS AND METHODS: The algorithm is based on the assumption that intensity inhomogeneity in MR data is multiplicative and smoothly varying. Using a statistically stable method, the algorithm first calculates the partial derivative of the inhomogeneity gradient across the data. The algorithm then solves for the gradient field and fits it to a parametric surface. It was tested on both simulated and real human and animal MRI data. RESULTS: The algorithm is shown to restore the homogeneity in all images that were tested. On real human brain images the algorithm demonstrated superior or comparable performance relative to some of the commonly used intensity inhomogeneity correction methods such as SPM, BrainSuite, and N3. CONCLUSION: The proposed algorithm provides an alternative method for correcting the intensity inhomogeneity in MR images. It is shown to be fast and its performance is superior or comparable to algorithms described in the published literature. Due to its generality, this algorithm is applicable to MR images of both humans and animals.
PURPOSE: To develop and implement a new approach for correcting the intensity inhomogeneity in magnetic resonance imaging (MRI) data. MATERIALS AND METHODS: The algorithm is based on the assumption that intensity inhomogeneity in MR data is multiplicative and smoothly varying. Using a statistically stable method, the algorithm first calculates the partial derivative of the inhomogeneity gradient across the data. The algorithm then solves for the gradient field and fits it to a parametric surface. It was tested on both simulated and real human and animal MRI data. RESULTS: The algorithm is shown to restore the homogeneity in all images that were tested. On real human brain images the algorithm demonstrated superior or comparable performance relative to some of the commonly used intensity inhomogeneity correction methods such as SPM, BrainSuite, and N3. CONCLUSION: The proposed algorithm provides an alternative method for correcting the intensity inhomogeneity in MR images. It is shown to be fast and its performance is superior or comparable to algorithms described in the published literature. Due to its generality, this algorithm is applicable to MR images of both humans and animals.
Authors: Mohamed N Ahmed; Sameh M Yamany; Nevin Mohamed; Aly A Farag; Thomas Moriarty Journal: IEEE Trans Med Imaging Date: 2002-03 Impact factor: 10.048
Authors: Amir Reza Sadri; Andrew Janowczyk; Ren Zhou; Ruchika Verma; Niha Beig; Jacob Antunes; Anant Madabhushi; Pallavi Tiwari; Satish E Viswanath Journal: Med Phys Date: 2020-11-27 Impact factor: 4.071