Classifying hemodynamics of MR brain perfusion images using independent component analysis (ICA)

Dynamic-susceptibility-contrast MR imaging is a widely used perfusion imaging technique that 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 signal changes over time on different brain tissues represent distinct blood supply patterns and are crucial for studying cerebral hemodynamics. By assuming the spatial independence among these patterns, independent component analysis (ICA) was applied to classify different tissues, i.e., artery, gray matter, white matter, vein and sinus and choroid 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 blood volume (CBV), relative cerebral blood flow (CBF), and relative mean transit time (MTT), were computed and their averaged ratios between gray matter and white matter were in good agreement with those in the literature.