ICA-based segmentation of the brain on perfusion data

被引：0

作者：

Tasciyan, TA ^{[1
]}

Beckmann, CF ^{[1
]}

Morris, ED ^{[1
]}

Smith, SM ^{[1
]}

机构：

[1] Sensor Syst, Sterling, VA USA

来源：

PROCEEDINGS OF THE 23RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-4: BUILDING NEW BRIDGES AT THE FRONTIERS OF ENGINEERING AND MEDICINE | 2001年 / 23卷

关键词：

Independent Component Analysis; segmentation; Magnetic Resonance Imaging Perfusion; Dynamic Susceptibility Contrast Imaging;

D O I：

暂无

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

An Independent Component Analysis (ICA) based segmentation technique is presented allowing the quantitative assessment of cerebral blood volume (CBV), cerebral blood flow (CBF) and mean transit time (MTT) from dynamic susceptibility contrast magnetic resonance (MR) images of the brain. Tissue types such as gray matter (GM), white matter (WM), and pathology appear as different ICA components as a result of their distinct temporal response to the first passage of contrast agent through the brain. The average CBV, CBF, and MTT values calculated for each component / tissue type could help evaluate the evolution of pathology and provide the opportunity for intersubject comparisons.

引用

页码：2537 / 2540

页数：4

共 14 条

[1]

BECKMANN C, 2000, NEUROIMAGE, V11, P643

[2]

CARROLL TJ, 2000, P INT SOC MAGN RESON, V8, P746

[3]

COMON P, 1994, SIGNAL PROCESS, V36, P11

[4] Fast and robust fixed-point algorithms for independent component analysis [J].