Paper Info
Title
Generalized linear least squares algorithms for modeling glucose metabolism in the human brain with corrections for vascular effects.
Abstract
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.
Year
DOI
Venue
2002
10.1016/S0169-2607(01)00160-2
Computer Methods and Programs in Biomedicine
Keywords
Field
DocType
Positron emission tomography,Functional imaging,Parameter estimation,Modeling
Least squares,Parametrization,Computer science,Algorithm,Human brain,Parametric statistics,Positron emission tomography,Estimation theory,Linear least squares,Positron
Journal
Volume
Issue
ISSN
68
1
0169-2607
Citations 
PageRank 
References 
2
0.39
5
Authors
4
Name
Order
Citations
PageRank
Weidong Cai193886.65
David Dagan Feng23329413.76
Roger Fulton3625.99
Wan-Chi Siu42016210.10