Generalized linear least squares algorithms for modeling glucose metabolism in the human brain with corrections for vascular effects.
The generalized linear least squares (GLLS) algorithm has been found useful in image-wide parameter estimation for the generation of parametric images with positron emission tomography (PET) as it is computationally efficient and statistically reliable. However, the original algorithm was designed for parameter estimation with non-uniformly sampled instantaneous measurements. When dynamic PET data are sampled with the optimal image sampling schedule (OISS) to reduce memory and storage space, only a few temporal image frames are recorded. As a result, the direct application of GLLS is no longer appropriate. In this paper, we extend the GLLS algorithm to a five parameter model for the study of human brain metabolism, which accounts for the effect of cerebral blood volume (CBV), using OISS sampled data, with as few as five temporal samples. The formulation for this new GLLS algorithm is developed, and its computational efficiency and statistical reliability are investigated and validated using computer simulations and clinical PET [18F]-2-fluoro-2-deoxy-D-glucose (FDG) data.
Cai
Weidong
W
Basser Department of Computer Science, Biomedical and Multimedia Information Technology (BMIT) Group, Madsen Building F09, The University of Sydney, NSW 2006, Australia.
Feng
Dagan
D
Fulton
Roger
R
Siu
Wan-Chi
WC
D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging / Q000378:metabolism*; D002560:Cerebrovascular Circulation; D019295:Computational Biology; D003198:Computer Simulation; D005462:Fluorine Radioisotopes; D019788:Fluorodeoxyglucose F18; D005947:Glucose / Q000378:metabolism*; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D008959:Models, Neurological; D014055:Tomography, Emission-Computed
D005462:Fluorine Radioisotopes; D019788:Fluorodeoxyglucose F18; D005947:Glucose