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Proceedings Paper

Segmentation and quantification of ischemic lesions on DWI images with different B-value at high magnetic field (3.0T)
Author(s): Wu Li; Jie Tian; Jianping Dai
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Paper Abstract

Diffusion weighted imaging (DWI) is the gold standard for imaging of acute stroke. Today, high-field systems operating at 3T become increasingly available in clinical settings. But, with b-value increasing, lesion SNR of DWI image descends, and anisotropy increases significantly. Aim of the study is to develop an automatic volumetric measure method of ischemic lesions on diffusion weighted imaging (DWI) images at high magnetic field, without the disturbance of anisotropy. Using a home-built interactive platform, we rated SNR and anisotropy. The extent of anisotropy was evaluated by the intensity ratio of white matter versus gray matter. Based on this knowledge, we developed an automatic segmentation method, involving firstly non-linear anisotropic diffusion filtering, secondly expert pieces of information applied to determine the scopes of parameters according to different b-value, and finally multi-scale adaptive statistical classification with intensity inhomogeneity correction. Results of the automatic segmentation are compared with lesion delineations by experts, showing the rapid identification of ischemic lesion with accuracy and reproducibility, even in the presence of radio frequency (RF) inhomogeneity. There has been considerable interest in using DWI at 3T to detect ischemic lesion in stroke patients. The proposed method is promising for rapid, accurate, and quantitatively diagnosis of ischemic stroke.

Paper Details

Date Published: 30 April 2004
PDF: 10 pages
Proc. SPIE 5369, Medical Imaging 2004: Physiology, Function, and Structure from Medical Images, (30 April 2004); doi: 10.1117/12.536102
Show Author Affiliations
Wu Li, Institute of Automation (China)
Jie Tian, Institute of Automation (China)
Jianping Dai, Beijing Tiantan Hopital (China)

Published in SPIE Proceedings Vol. 5369:
Medical Imaging 2004: Physiology, Function, and Structure from Medical Images
Amir A. Amini; Armando Manduca, Editor(s)

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