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Updated: Jul 11, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Generalized linear least squares algorithms for modeling glucose metabolism in the human brain with corrections for
Weidong Cai1, Dagan Feng, Roger Fulton
1Basser Department of Computer Science, Biomedical and Multimedia Information Technology (BMIT) Group, Madsen Building F09, The University of Sydney, NSW 2006, Australia.
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.
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