Maximum likelihood estimation of cerebral blood flow in dynamic susceptibility contrast MRI.

摘要: For quantification of cerebral blood flow (CBF) using dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI), knowledge the tissue response function is necessary. To obtain this, passage measurement must be corrected for arterial input. This study proposes an iterative maximum likelihood expectation maximization (ML-EM) algorithm this correction, which takes into account noise in T2- or T*2-weighted image sequences. The ML-EM does not assume a priori shape function; it automatically corrects arrival time offsets and inherently yields positive values. results on synthetic sequences are presented, recovered values functions good agreement with their method illustrated by calculating gray white matter clinical example. Magn Reson Med 41:343–350, 1999. © 1999 Wiley-Liss, Inc.