Abstract：
:Dynamic-susceptibility-contrast (DSC) magnetic resonance imaging records signal changes on images when the injected contrast-agent particles pass through a human brain. The temporal signal changes on different brain tissues manifest distinct blood-supply patterns which are vital for the profound analysis of cerebral hemodynamics. Under the assumption of the spatial independence among these patterns, noiseless independent factor analysis (IFA) was first applied to decompose the DSC-MR data into different independent-factor images with corresponding signal-time curves. A major tissue type, such as artery, gray matter, white matter, vein, sinus, and choroid plexus, etc., on each independent-factor image was further segmented out by an optimal threshold. Based on the averaged signal-time curve on the arterial area, the cerebral hemodynamic parameters, such as relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), and mean transit time (MTT), were computed and their averaged ratios between gray matter and white matter for normal subjects were in good agreement with those in the literature. Data of a stenosis patient before and after treatment were analyzed and the result illustrates that this method is effective in extracting spatiotemporal blood-supply patterns which improves differentiation of pathological and non-pathological hemodynamics.
journal_name
Med Image Analjournal_title
Medical image analysisauthors
Chou YC,Teng MM,Guo WY,Hsieh JC,Wu YTdoi
10.1016/j.media.2007.02.002subject
Has Abstractpub_date
2007-06-01 00:00:00pages
242-53issue
3eissn
1361-8415issn
1361-8423pii
S1361-8415(07)00023-0journal_volume
11pub_type
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