Fourier domain closed-form formulas for estimation of kinetic parameters in reversible multi-compartment models
- Univ. of Utah, Salt Lake City, UT (United States)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Background: Compared with static imaging, dynamic emission computed tomographic imaging with compartment modeling can quantify in vivo physiologic processes, providing useful information about molecular disease processes. Dynamic imaging involves estimation of kinetic rate parameters. For multi-compartment models, kinetic parameter estimation can be computationally demanding and problematic with local minima. Methods: This paper offers a new perspective to the compartment model fitting problem where Fourier linear system theory is applied to derive closed-form formulas for estimating kinetic parameters for the two-compartment model. The proposed Fourier domain estimation method provides a unique solution, and offers very different noise response as compared to traditional non-linear chi-squared minimization techniques. Results: The unique feature of the proposed Fourier domain method is that only low frequency components are used for kinetic parameter estimation, where the DC (i.e., the zero frequency) component in the data is treated as the most important information, and high frequency components that tend to be corrupted by statistical noise are discarded. Computer simulations show that the proposed method is robust without having to specify the initial condition. The resultant solution can be fine tuned using the traditional iterative method. Conclusions: The proposed Fourier-domain estimation method has closed-form formulas. The proposed Fourier-domain curve-fitting method does not require an initial condition, it minimizes a quadratic objective function and a closed-form solution can be obtained. The noise is easier to control, simply by discarding the high frequency components, and emphasizing the DC component.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- Ben B. and Iris M. Margolis Foundation; National Institutes of Health (NIH); USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Grant/Contract Number:
- AC02-05CH11231; R01 HL108350; R01 CA135556; R01 HL50663; R01 EB007219
- OSTI ID:
- 1626567
- Journal Information:
- Biomedical Engineering Online, Vol. 11, Issue 1; ISSN 1475-925X
- Publisher:
- BioMed CentralCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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