Improvement of likelihood estimation in Logan graphical analysis using maximum a posteriori for neuroreceptor PET imaging.
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.
Shidahara
Miho
M
National Institute of Radiological Sciences, Molecular Imaging Center, 4-9-1 Anagawa, Inage-ku, Chiba 263-8555, Japan. sidahara@nirs.go.jp
Seki
Chie
C
Naganawa
Mika
M
Sakata
Muneyuki
M
Ishikawa
Masatomo
M
Ito
Hiroshi
H
Kanno
Iwao
I
Ishiwata
Kiichi
K
Kimura
Yuichi
Y
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
D002250:Carbon Radioisotopes; D010879:Piperazines; D019275:Radiopharmaceuticals; D017480:Receptors, sigma; D000097605:Sigma-1 Receptor; C101789:SA 4503