@misc{oai:repo.qst.go.jp:00064212,
 author = {Hoshino, Naoki and Hontani, Hidekata and Sakaguchi, Kazuya and Sakata, Muneyuki and Ishiwata, Kiichi and Kimura, Yuichi and 本谷 秀堅 and 坂田 宗之 and 石渡 喜一 and 木村 裕一},
 month = {Feb},
 note = {We propose a MAP-based denoising method for PET functional imaging. 
In PET, time activity curves in tissue (tTAC) is analyzed using LoganGraphical Analysis (LGA) as distribution volume (V). In the method, a prior distribution of tTACs is computed based on a set of simulated ones, which are outputs from a compartment model that describes the behavior of administered radioligand in tissue. Drawing a set of rate constants, that is a system parameter of the model, from a uniform distribution covering physiologically feasible range, we can obtain a corresponding simulated tTACs, which compose a manifold in a space of tTACs. Given a measured tTAC, we compute the posterior probability distribution at each point on this manifold. The denoised tTAC is derived as the point on the manifold where the computed posterior probability is the maximum.
The purpose of this study was to experimentally analyze the relationship between the prior probability and the resultant estimates of V. For this analysis, we selected [11C]SA4503 as a radioligand and computed three prior probability distributions of the tTACs. Using each of these priors, we denoised a set of synthetic noisy tTACs and a set of clinical ones, and evaluated the estimation errors of V. 
The results showed that the estimated V became most accurate when the manifold was enough large that the maximum of the posterior probability was never located at the boundary of the manifold., SPIE Medical Imaging 2011},
 title = {MAP-based denoising of dynamic PET data for quantitative receptor imaging},
 year = {2011}
}