Fast parametric imaging algorithm for dual-input biomedical system parameter estimation

• Published in: Computer methods and programs in biomedicine Vol. 81; no. 1; pp. 49 - 55
• Main Authors: Choi, Hon-Chit, Chen, Sirong, Feng, Dagan, Wong, Koon-Pong
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier Ireland Ltd 2006
• Subjects: Algorithms; Computer Simulation; Dual-input biomedical system; Humans; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; Kinetics; Least-Squares Analysis; Linear Models; Liver - diagnostic imaging; Liver - metabolism; Liver - pathology; Models, Biological; Models, Chemical; Models, Statistical; Monte Carlo Method; Normal Distribution; Parameter estimation; Positron emission tomography (PET); Positron-Emission Tomography - methods; Radiography; System identification; Tomography, Emission-Computed; Parameter estimation; Positron emission tomography (PET); System identification; Dual-input biomedical system
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2005.11.003

Medical parametric imaging with dynamic positron emission tomography (PET) plays an increasingly potential role in modern biomedical research and clinical diagnosis. The key issue in parametric imaging is to estimate parameters based on sampled data at the pixel-by-pixel level from certain dynamic processes described by valid mathematical models. Classic nonlinear least squares (NLS) algorithm requires a “good” initial guess and the computational time-complexity is high, which is impractical for image-wide parameter estimation. Although a variety of fast parametric imaging techniques have been developed, most of them focus on single input systems, which do not provide an optimal solution for dual-input biomedical system parameter estimation, which is the case of liver metabolism. In this study, a dual-input-generalized linear least squares (D-I-GLLS) algorithm was proposed to identify the model parameters including the parameter in the dual-input function. Monte Carlo simulation was conducted to examine this novel fast algorithm. The results of the quantitative analysis suggested that the proposed technique could provide comparable reliability of the parameter estimation with NLS fitting and accurately identify the parameter in the dual-input function. This method may be potentially applicable to other dual-input biomedical system parameter estimation as well.