Rapid algorithms for the construction of cerebral blood flow and oxygen utilization images with oxygen-15 and dynamic...

Two rapid estimation algorithms for construction of cerebral blood flow (CBF) and oxygen utilization (CMRO) images with dynamic positron emission tomography (PET) are presented. These algorithms are based on the linear least squares (LLS) and generalized linear least squares (GLLS) methodologies. Using the conventional two-compartmental model and multiple tracer studies, we derived a linear relationship for brain tissue activity to arterial blood activity, time-integrated arterial blood activity and time-integrated brain tissue activity. The LLS technique is computationally efficient as no regression analysis is required, while GLLS is used to refine the estimates obtained from LLS. A comparative study using non-linear least squares regression (NLS) revealed excellent correlation between the new algorithms for various noise levels expected in clinical applications. A sensitivity analysis was performed to examine reliability and identifiability of the parameter estimates. In view of the results, LLS and GLLS provide rapid and reliable estimates of CBF and CMRO when applied to dynamic PET data. These algorithms are particularly suitable for pixel-by-pixel construction of high resolution and highly accurate PET functional images.