Classification of hemodynamics from perfusion MR brain images using noiseless independent factor analysis

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 (NIFA) 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 was analyzed and the result illustrates that this method is effective in extracting spatio-temporal blood supply patterns which improves differentiation of pathological and physiological hemodynamics.