Development of the algorithm for quantitative neuroreceptor imaging using PET without serial arterial blood sampling and reference region

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This study aims at developing algorithms for fully quantitative neuroreceptor imaging using PET without serial arterial blood sampling and reference regions. For omission of serial arterial blood sampling, variational Bayesian approach and an intersectional searching approach were investigated based on Logan graphical analysis (LGA). As a result, dynamic studies using FDG, MPDX and TMSX were accomplished without serial arterial blood sampling. The variational Bayesian approach introduces nonnegative constraints to an algorithm based on independent component analysis which has been developed by my group. The approaches improved an estimation performance of a time history of radioactivity in arterial blood (pTAC), and it made an estimated blood volume image, which was another outcome of the algorithm, physiologically reliable. Other algorithm based on LGA is to estimate pTAC using the operational equation of LGA, for which the terms including pTAC were canceled out using pTAC is common among regions. This algorithm was sensitive for noise in PET data due to its mathematical framework. In order to realize reliable pTAC estimation, some data preprocessing procedures were developed using clustering based on a kinetics of administered radiopharmaceutical For the omission of reference region, a feasibility of voxel-based compartment model analysis was investigated. However, it was not practical due to bad noise statistics in voxel-based PET data. Then, total distribution volumes (VT) was estimated for each voxels to decrease the number of estimated rate constants using LGA. The VT estimates by LGA is underestimated under the existence of large noise in PET data. MAP based estimation approach was incorporated with LGA, and fast and statistically reliable voxel-based VT estimation was archived.