Please use this identifier to cite or link to this item:
Title: Novel parameter estimation methods for ¹¹C-acetate dual-input liver model with dynamic PET
Authors: Chen, Sirong
Feng, D. David
Subjects: Parameter estimation
¹¹C-acetate dual-input liver model
Issue Date: May-2006
Publisher: IEEE
Source: IEEE transactions on biomedical engineering, May 2006, v. 53, no. 5, p. 967-973.
Abstract: The successful investigation of ¹¹C-acetate in positron emission tomography (PET) imaging for marking hepatocellular carcinoma (HCC) has been validated by both clinical and quantitative modeling studies. In the previous quantitative studies, all the individual model parameters were estimated by the weighted nonlinear least squares (NLS) algorithm. However, five parameters need to be estimated simultaneously, therefore, the computational time-complexity is high and some estimates are not quite reliable, which limits its application in clinical environment. In addition, liver system modeling with dual-input function is very different from the widespread single-input system modeling. Therefore, most of the currently developed estimation techniques are not applicable. In this paper, two parameter estimation techniques: graphed NLS (GNLS) and graphed dual-input generalized linear least squares (GDGLLS) algorithms were presented for ¹¹C-acetate dual-input liver model. Clinical and simulated data were utilized to test the proposed algorithms by a systematic statistical analysis. Compared to NLS fitting, these two novel methods achieve better estimation reliability and are computationally efficient, and they are extremely powerful for the estimation of the two potential HCC indicators: local hepatic metabolic rate-constant of acetate and relative portal venous contribution to the hepatic blood flow.
Rights: © 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Type: Journal/Magazine Article
DOI: 10.1109/TBME.2006.872817
ISSN: 0018-9294
Appears in Collections:EIE Journal/Magazine Articles

Files in This Item:
File Description SizeFormat 
Chen_Feng_Parameter_Estimation_Methods.pdf3.4 MBAdobe PDFView/Open

All items in the PolyU Institutional Repository are protected by copyright, with all rights reserved, unless otherwise indicated. No item in the PolyU IR may be reproduced for commercial or resale purposes.