Hybrid input function estimation using a single-input-multiple-output (SIMO) approach

Yi Su; Kooresh I. Shoghi

doi:10.1117/12.811164

27 February 2009 Hybrid input function estimation using a single-input-multiple-output (SIMO) approach

Yi Su, Kooresh I. Shoghi

Proceedings Volume 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging; 726221 (2009) https://doi.org/10.1117/12.811164
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

A hybrid blood input function (BIF) model that incorporates region of interests (ROIs) based peak estimation and a two exponential tail model was proposed to describe the blood input function. The hybrid BIF model was applied to the single-input-multiple-output (SIMO) optimization based approach for BIF estimation using time activity curves (TACs) obtained from ROIs defined at left ventricle (LV) blood pool and myocardium regions of dynamic PET images. The proposed BIF estimation method was applied with 0, 1 and 2 blood samples as constraints for BIF estimation using simulated small animal PET data. Relative percentage difference of the area-under-curve (AUC) measurement between the estimated BIF and the true BIF was calculated to evaluate the BIF estimation accuracy. SIMO based BIF estimation using Feng's input function model was also applied for comparison. The hybrid method provided improved BIF estimation in terms of both mean accuracy and variability compared to Feng's model based BIF estimation in our simulation study. When two blood samples were used as constraints, the percentage BIF estimation error was 0.82 ± 4.32% for the hybrid approach and 4.63 ± 10.67% for the Feng's model based approach. Using hybrid BIF, improved kinetic parameter estimation was also obtained.

Citation Download Citation

Yi Su and Kooresh I. Shoghi "Hybrid input function estimation using a single-input-multiple-output (SIMO) approach", Proc. SPIE 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 726221 (27 February 2009); https://doi.org/10.1117/12.811164

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KEYWORDS

Blood

Positron emission tomography

Statistical analysis

Error analysis

Animal model studies

Monte Carlo methods

Tissues

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