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J Nucl Med. 2008; 49 (Supplement 1):23P
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Instrumentation & Data Analysis: Data Analysis & Management

Quantitative Methods

Comparison of multiple linear regression methods for the quantification of irreversible radiotracers

Su Jin Kim1, Jae Sung Lee1, Yu Kyeong Kim1, James Frost2, Gary Wand3, Mary McCaul3 and Dong Soo Lee1

1 Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, South Korea; 2 Department of Diagnostic Radiology, Yale University, New Haven, Connecticut; 3 Department of Psychiatry and Medicine, Johns Hopkins University, Baltimore, Maryland

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Objectives: Previously, we suggested an alternative approach (MLAIR: Multiple Linear Analysis for Irreversible Radiotracers) to the Patlak graphical analysis (PGA) for the quantification of the net accumulations (Ki) of irreversible radioligands (SNM, 2007). The aim of this study was to compare two possible formula of the MLAIR in terms of their statistical properties in the parameter estimation.

Methods: The first equation (MLAIR1) has a desirable feature for ordinary least square estimations because only the dependent variable CT(t) is noisy. The second equation (MLAIR2) provides Ki from direct estimates of the coefficients of independent variables without the mediation of a division operation. Simulations were performed to assess their statistical properties in parameter estimations. [11C]MeNTI PET data, which binds {delta}-opioid receptor irreversibly, were analyzed to assess their usefulness for VOI analysis and parametric image generation.

Results: During simulations, MLAIR1 provided less biased Ki estimates than the other linear methods, but showed a high uncertainty level for noisy data, whereas MLAIR2 increased the robustness of estimation, but at the expense of increased bias. Both methods showed good correlations with NLS estimates. MLAIR2 parametric images showed remarkable image quality as compared with PGA and showed improved statistical power for SPM analysis.

Conclusions: MLAIR1 showed unbiased parameter estimations but high levels of uncertainty for noisy data, and thus, would be useful for VOI analysis. MLAIR2, which showed lowest parameter estimating variability, would be suitable for voxel-based data analysis.





This Article
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Right arrow Articles by Kim, S. J.
Right arrow Articles by Lee, D. S.