The study compares Bayesian estimation algorithms with singular value decomposition (SVD) in analyzing CT perfusion (CTP) data from patients with acute cerebral infarction.
CTP data from 13 patients were used to assess visual clarity of ischemic areas and calculate ratios between healthy and affected brain regions using both methods.
Results indicated that Bayesian estimation outperformed SVD, particularly in measuring cerebral blood flow (CBF) and mean transit time (MTT), making it more useful for evaluating acute stroke patients.