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Improvement of likelihood estimation in Logan graphical analysis using maximum a posteriori for neuroreceptor PET imaging --- toward bias-free distribution volume imaging from noise in PET data ---
https://repo.qst.go.jp/records/69660
https://repo.qst.go.jp/records/69660cfb2bcaa-e924-425e-a34d-bdae58ba498b
| Item type | 会議発表用資料 / Presentation(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2008-12-19 | |||||
| タイトル | ||||||
| タイトル | Improvement of likelihood estimation in Logan graphical analysis using maximum a posteriori for neuroreceptor PET imaging --- toward bias-free distribution volume imaging from noise in PET data --- | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_c94f | |||||
| 資源タイプ | conference object | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Kimura, Yuichi
× Kimura, Yuichi× Shidahara, Miho× Seki, Chie× Naganawa, Mika× Sakata, Muneyuki× Ishikawa, Masatomo× Ito, Hiroshi× Kanno, Iwao× Ishiwata, Kiichi× 木村 裕一× 志田原 美保× 関 千江× 長縄 美香× 坂田 宗之× 伊藤 浩× 菅野 巖× 石渡 喜一 |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Objective: Logan graphical analysis (LGA; Logan, JCBF, 1990) is useful for neuroreceptor imaging. LGA does not assume the number of compartments to describe a behavior of administered radioligand, and it is implemented as a line estimation, that is stable and fast. However, LGA with regression line estimation suffers from an underestimation in estimated total distribution volume (VT) due to a noise in PET data (tTAC) (Kimura, ANM, 2007). In this study, a new approach combining a likelihood estimation for graphical analysis (LEGA; Ogden, Stat Med, 2003) with a maximum a posteriori (MAP) algorithm, naming MEGA (MAP estimation in graphical analysis). Algorithm: In LEGA, tTAC is computed from VT and an intercept in LGA (b) in the manner of LGA, and VT and b are derived in a likelihood fashion using a nonlinear estimation algorithm. In MEGA, a set of tTACs are formed with VT and b varying in a physiological range as a prior knowledge, and then the most similar tTAC is searched for a given measured tTAC in a feature space, where a shape of tTAC is represented as a point in a multidimensional space, and a similarity between two tTACs can be evaluated using the distance as described in Kimura (NeuroImage, 1999). A noise in a measured voxel-based tTAC should be addressed. A fluctuation in a shape of tTAC is dealt with a distribution of feature points, and a Mahalanobis distance is utilized for the. Moreover, for noise reduction in a tTAC, principal components (PCs) are computed from the template to form an orthogonal subspace, and a measured tTAC is projected onto it before the searching. Conclusion: LGA is attractive for PET neuroreceptor imaging due to its reliability and simplicity. But the noise-induced underestimation is problematic. The proposed MEGA realized robust estimation and suppressed the bias under existence of large noise observed in a voxel-based tTAC. In conclusion, MEGA can be practical algorithm for LGA neuroreceptor imaging with PET. |
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| 会議概要（会議名, 開催地, 会期, 主催者等） | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | 分子イメージング研究シンポジウム MOLECULAR IMAGING2008 | |||||
| 発表年月日 | ||||||
| 日付 | 2008-12-15 | |||||
| 日付タイプ | Issued | |||||
