Segmentation of Hemodynamics from Dynamic- Susceptibility-Contrast Magnetic Resonance Brain Images Using Sequential Independent Component Analysis

Abstract

Dynamic-susceptibility-contrast magnetic resonance imaging, a popular perfusion imaging technique, records signal changes on images caused by the passage of contrast-agent particles in the human brain after a bolus injection of contrast agent. The temporal signal changes on different brain tissues characterize distinct blood supply patterns which are critical for the profound analysis of cerebral hemodynamics. Under the assumption of the spatial independence among these patterns, independent component analysis (ICA) was applied to segment different tissues, i.e., artery, gray matter, white matter, vein and sinus and choroids plexus, so that the spatio- temporal hemodynamics of these tissues were decomposed and analyzed. An arterial input function was modeled using the concentration-time curve of the arterial area for the deconvolution calculation of relative cerebral blood flow. The cerebral hemodynamic parameters, such as relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), and relative mean transit time (rMTT), were computed and their averaged ratios between gray matter and white matter were in good agreement with those in the literature.