TY - JOUR T1 - Evaluation of a modeling-based plasma input function estimation algorithm for unbiased quantification of ligand receptor dynamic PET JF - Journal of Nuclear Medicine JO - J Nucl Med SP - 157 LP - 157 VL - 52 IS - supplement 1 AU - Yun Zhou AU - James Brasic AU - Arman Rahmim AU - Andrew Horti AU - Dean Wong Y1 - 2011/05/01 UR - http://jnm.snmjournals.org/content/52/supplement_1/157.abstract N2 - 157 Objectives To develop and validate noninvasive method for unbiased quantification of ligand receptor dynamic PET. Methods A non-parametric PIF based (i.e., not based on analytic formulation of the PIF) iterative estimation algorithm was derived by applying deconvolution operation to a two-tissue compartment model. The algorithm was evaluated by 55 [11C]raclopride (D2 receptor; cerebellum as reference tissue), 3 baboon and 1 human [18F]AZAN (α4β2-nAChR receptor) dynamic PET studies, where there is no reference tissue for [18F]AZAN study. The measured PIF was obtained in each PET study by arterial blood sampling. Fifteen ROIs were manually drawn on the co-registered MRI images and then copied to dynamic PET images to extract ROI kinetics. For comparison, both estimated and measured PIFs were normalized by area under curve. The Logan plot using PIF was used for ROI kinetic based DV estimation. To avoid noise-induced underestimation in the Logan plot, a bi-graphical plot developed by Zhou et al. (Neuroimage 2010, 49(4):2947-57) was used for generating DV images. The binding potential (BPND) was also calculated as BPND = DV(ROI)/DV(cerebellum) -1 after DV estimation in [11C]raclopride studies. Results The estimated PIFs (estPIF) were similar to the measured PIFs (mPIF) in terms of curve shape. Linear correlations between the DVs from estPIF and mPIF were obtained as: DV(estPIF) = 1.01DV(mPIF) + 0.01, R2=1.00, and BP(estPIF) = 1.00BP(mPIF) + 0.00, R2=1.00 for [11C]raclopride; and DV(estPIF) = 1.03DV(mPIF) - 1.57, R2=1.00 for [18F]AZAN. The DV images generated by estPIF were virtually identical to those obtained by mPIF for both [11C]raclopride and [18F]AZAN studies. Conclusions The modeling-based plasma input function estimation algorithm is validated by this study for unbiased noninvasive estimation of DV and/or BPND in [11C]raclopride and [18F]AZAN dynamic PET studies. The algorithm is robust to distinct tracer kinetic properties. The methodology has potential to facilitate PET applications in neuroimaging and drug development ER -