Comparing MR image intensity standardization against tissue characterizability of magnetization transfer ratio imaging.

作者信息

Madabhushi Anant, Udupa Jayaram K, Moonis Gul

机构信息

Department of Biomedical Engineering, Rutgers University, New Brunswick, New Jersey, USA.

出版信息

J Magn Reson Imaging. 2006 Sep;24(3):667-75. doi: 10.1002/jmri.20658.

Abstract

PURPOSE

To evaluate existing methods of standardization by exploiting the well-known tissue characterizing property of magnetization transfer ratio (MTR) values obtained from MT imaging, and compare the tissue characterizability of standardized T2, proton density (PD), and T1 images against the MTR images.

MATERIALS AND METHODS

Image intensity standardization is a postprocessing method that was designed to correct for acquisition-to-acquisition signal intensity variations (nonstandardness) inherent in magnetic resonance (MR) images. The main idea of this technique is to deform the volume image histogram of each study to match a standard histogram, and to utilize the resulting transformations to map the image intensities into a standard scale. The method has been shown to produce a significant gain in similarity of resulting images and to achieve numeric tissue characterization. In this work we compared PD-, T2-, and T1-weighted images before and after standardization with the corresponding MT images for 10 patient MRI studies of the brain, in terms of the normalized median values on the corresponding image histograms.

RESULTS

No statistically significant difference was observed between the standardized PD-, T2-, and T1-weighted images and the corresponding MTR images. However, a statistically significant difference was found between the pre- and poststandardized PD-, T2-, and T1-weighted images, and between the prestandardized PD-, T2-, and T1-weighted images and the corresponding MTR images.

CONCLUSION

These results suggest that standardized T2, PD, and T1 images and their tissue-specific intensity signatures may be useful for characterizing disease.

摘要

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