Chinese Journal of Tissue Engineering Research ›› 2010, Vol. 14 ›› Issue (52): 9744-9747.doi: 10.3969/j.issn.1673-8225.2010. 52.014

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A bias field fitting method based on MR entropy for cerebral infarction rats

Dong Jie 1,2, Zhou Zhen3, Liu Chun-hong2, Wang Ying4, Zhang Yu1   

  1. 1 Department of Radiology, Fuxing Hospital of Capital Medical University, Beijing  100038, China; 2 Department of Radiology, Anding Hospital of Capital Medical University, Beijing  100088, China; 3 College of Biomedical Engineering, Capital Medical University, Beijing  100069, China; 4 Institute of Neuroscience, Capital Medical University, Beijing  100069, China
  • Online:2010-12-24 Published:2010-12-24
  • Contact: Zhou Zhen, Doctor, Lecturer, College of Biomedical Engineering, Capital Medical University, Beijing 100069, China zhouzhenbme@gmail.com
  • About author:Dong Jie, Attending physician, Department of Radiology, Fuxing Hospital of Capital Medical University, Beijing 100038, China; Department of Radiology, Anding Hospital of Capital Medical University, Beijing 100088, China dongjiedong1@163.com
  • Supported by:

    the Basic Clinical Scientific Research Program of Capital Medical University, No. 10JL63*

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Abstract:

BACKGROUND: In MR imaging, especially at high field strength, non-uniform RF coil response is a difficult problem to avoid. It is reflected as a smoothly but non-linearly varying bias field, which modulates tissue intensities across the acquired image. Such inhomogeneties degrade image quality in general and impede segmentation.
OBJECTIVE: To propose a model independent correction method of complex image signal bias field based on image data entropy.
METHODS: Data were collected by MR device (GE Signa 1.5T). MR images of the cerebral infarction rats were obtained (imaging slice thickness 6 mm, gap 1 mm), except for 3D T2WI (imaging slice thickness 2 mm, gap 0.5 mm). The method was to optimize and remove the bias field depending on 2D entropy.
RESULTS AND CONCLUSION: This method was compared to other methods on a number of simulated and real MR images. Compared with traditional methods, the tissues distribution and comparison were more obvious in the histogram, which ease segmentation and extraction. Compared with traditional methods, this method is more effective under low-density Delaunay triangulation, but costs more time under high-density Delaunay triangulation.

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