Background-Suppressed MR Venography of the Brain Using Magnitude Data: A High-Pass Filtering ApproachReportar como inadecuado




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Computational and Mathematical Methods in Medicine - Volume 2014 2014, Article ID 812785, 9 pages -

Research Article

Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China

Key Laboratory for Biomedical Engineering of Education Ministry of China, Hangzhou, China

College of Automation, Hangzhou Dianzi University, Hangzhou, China

Department of Radiology, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, China

Received 24 October 2013; Revised 10 February 2014; Accepted 14 February 2014; Published 10 June 2014

Academic Editor: Yuanjie Zheng

Copyright © 2014 Zhaoyang Jin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Conventional susceptibility-weighted imaging SWI uses both phase and magnitude data for the enhancement of venous vasculature and, thus, is subject to signal loss in regions with severe field inhomogeneity and in the peripheral regions of the brain in the minimum-intensity projection. The purpose of this study is to enhance the visibility of the venous vasculature and reduce the artifacts in the venography by suppressing the background signal in postprocessing. A high-pass filter with an inverted Hamming window or an inverted Fermi window was applied to the Fourier domain of the magnitude images to enhance the visibility of the venous vasculature in the brain after data acquisition. The high-pass filtering approach has the advantages of enhancing the visibility of small veins, diminishing the off-resonance artifact, reducing signal loss in the peripheral regions of the brain in projection, and nearly completely suppressing the background signal. The proposed postprocessing technique is effective for the visualization of small venous vasculature using the magnitude data alone.





Autor: Zhaoyang Jin, Ling Xia, Minming Zhang, and Yiping P. Du

Fuente: https://www.hindawi.com/



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