Calculation of cerebral perfusion parameters using regional arterial input functions identified by factor analysis

Abstract

To calculate regional cerebral blood volume (rCBV), regional cerebral blood flow (rCBF), and regional mean transit time (rMTT) accurately, an arterial input function (AIF) is required. In this study we identified a number of AIFs using factor analysis of dynamic studies (FADS), and performed the cerebral perfusion calculation pixel by pixel using the AIF that was located geometrically closest to a certain voxel. To verify the robustness of the method, simulated images were generated in which dispersion or delay was added in some arteries and in the corresponding cerebral gray matter (GM), white matter (WM), and ischemic tissue. Thereafter, AIFs were determined using the FADS method and simulations were performed using different signal-to-noise ratios (SNRs). Simulations were also carried out using an AIF from a single pixel that was manually selected. In vivo results were obtained from normal volunteers and patients. The FADS method reduced the underestimation of rCBF due to dispersion or delay that often occurs when only one AIF represents the entire brain. This study indicates that the use of FADS and the nearest-AIF method is preferable to manual selection of one single AIF.