Improvement of likelihood estimation in Logan graphical analysis using maximum a posteriori for neuroreceptor PET imaging

Miho Shidahara, Chie Seki, Mika Naganawa, Muneyuki Sakata, Masatomo Ishikawa, Hiroshi Ito, Iwao Kanno, Kiichi Ishiwata, Yuichi Kimura

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Objective: To reduce variance of the total volume of distribution (V T) image, we improved likelihood estimation in graphical analysis (LEGA) for dynamic positron emission tomography (PET) images using maximum a posteriori (MAP). Methods: In our proposed MAP estimation in graphical analysis (MEGA), a set of time-activity curves (TACs) was formed with V T varying in physiological range as a template, and then the most similar TAC was sought out for a given measured TAC in a feature space. In simulation, MEGA was compared with other three methods, Logan graphical analysis (GA), multilinear analysis (MA1), and LEGA using 500 noisy TACs, under each of seven physiological conditions (from 9.9 to 61.5 of V T). PET studies of [ 11C]SA4503 were performed in three healthy volunteers. In clinical studies, the V T images estimated from MEGA were compared with region of interest (ROI) estimates from a nonlinear least square (NLS) fitting over four brain regions. Results: In the simulation study, the estimated V T by GA had a large underestimation (y = 0.27x + 8.72, r 2 = 0.87). Applying the other methods (MA1, LEGA, and MEGA), these noise-induced biases were improved (y = 0.80x + 4.04, r 2 = 0.98; y = 0.85x + 3.05, r 2 = 0.99; y = 0.96x + 1.21, r 2 = 0.99, respectively). MA1 and LEGA produced increased variance of the estimated V T in clinical studies. However, MEGA improved signal-to-noise ratio (SNR) in V T images with linear correlations between ROI estimates with NLS (y = 0.87x + 5.1, r 2 = 0.96). Conclusions: MEGA was validated as an alternative strategy of LEGA to improve estimates of V T in clinical PET imaging.

Original languageEnglish
Pages (from-to)163-171
Number of pages9
JournalAnnals of Nuclear Medicine
Volume23
Issue number2
DOIs
Publication statusPublished - 2009 Feb 1
Externally publishedYes

Keywords

  • Kinetic analysis
  • Logan graphical analysis
  • MAP-based estimation algorithm in graphical analysis
  • Positron emission tomography

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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