Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15307
Title: Generalized linear least squares algorithms for modeling glucose metabolism in the human brain with corrections for vascular effects
Authors: Cai, W
Feng, D
Fulton, R
Siu, WC 
Keywords: Functional imaging
Modeling
Parameter estimation
Positron emission tomography
Issue Date: 2002
Source: Computer methods and programs in biomedicine, 2002, v. 68, no. 1, p. 1-14 How to cite?
Journal: Computer Methods and Programs in Biomedicine 
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.
URI: http://hdl.handle.net/10397/15307
ISSN: 0169-2607
DOI: 10.1016/S0169-2607(01)00160-2
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