8 April 1996 Generalized linear least squares method for estimating myocardial blood flow with N-13 ammonia positron emission tomography
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Abstract
We investigated the use of the Generalized Linear Least Squares (GLLS) method for fast estimation of myocardial blood flow (MBF) with N-13 ammonia Positron Emission Tomography (PET). GLLS was based on a high order integral equation converted from the state variable differential equations describing the kinetics of a PET tracer. Two error sources, spillover and the measurement noise, were studied. The estimation of the spillover coefficients between plasma time activity curve (TAC) and tissue TAC was incorporated into the GLLS. The GLLS procedure was modified accordingly. It was found, in computer simulation, that spillover correction incorporated GLLS provided as reliable MBF estimation as the model fitting method accounting for spillover. Since the linear kinetic model relating the plasma TAC to the tissue TAC was equivalent to the one relating the integral of plasma TAC (accumulated counts) to the integral of the tissue TAC, the GLLS method could be directly applied to the accumulated PET counts. It was found that the direct use of the accumulated counts reduced the random fluctuation observed in TAC data from PET images; and that this noise reduction significantly improved the accuracy of estimated MBF.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kewei Chen, Kewei Chen, Sung-Cheng Huang, Sung-Cheng Huang, Michael Lawson, Michael Lawson, Eric M. Reiman, Eric M. Reiman, Dagan David Feng, Dagan David Feng, Dino Ho, Dino Ho, Daniel Bandy, Daniel Bandy, Langsheng Yun, Langsheng Yun, Karl Sun, Karl Sun, Anita Palant, Anita Palant, } "Generalized linear least squares method for estimating myocardial blood flow with N-13 ammonia positron emission tomography", Proc. SPIE 2709, Medical Imaging 1996: Physiology and Function from Multidimensional Images, (8 April 1996); doi: 10.1117/12.237864; https://doi.org/10.1117/12.237864
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