title: Improvement of likelihood estimation in Logan graphical analysis using maximum a posteriori for neuroreceptor PET imaging.
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 [(11)C]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.
authors.affiliation: National Institute of Radiological Sciences, Molecular Imaging Center, 4-9-1 Anagawa, Inage-ku, Chiba 263-8555, Japan. sidahara@nirs.go.jp
authors.forename: Miho
authors.initials: M
authors.lastname: Shidahara
authors.forename: Chie
authors.initials: C
authors.lastname: Seki
authors.forename: Mika
authors.initials: M
authors.lastname: Naganawa
authors.forename: Muneyuki
authors.initials: M
authors.lastname: Sakata
authors.forename: Masatomo
authors.initials: M
authors.lastname: Ishikawa
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Ito
authors.forename: Iwao
authors.initials: I
authors.lastname: Kanno
authors.forename: Kiichi
authors.initials: K
authors.lastname: Ishiwata
authors.forename: Yuichi
authors.initials: Y
authors.lastname: Kimura
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging* / Q000378:metabolism*; D002250:Carbon Radioisotopes / Q000493:pharmacokinetics; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D016013:Likelihood Functions; D008297:Male; D010879:Piperazines / Q000493:pharmacokinetics*; D049268:Positron-Emission Tomography / Q000379:methods*; D019275:Radiopharmaceuticals / Q000493:pharmacokinetics; D017480:Receptors, sigma / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D011984:Sensory Receptor Cells / Q000000981:diagnostic imaging*; D000097605:Sigma-1 Receptor
chemical_list: D002250:Carbon Radioisotopes; D010879:Piperazines; D019275:Radiopharmaceuticals; D017480:Receptors, sigma; D000097605:Sigma-1 Receptor; C101789:SA 4503