Abstract
The aim of this study was to evaluate an image reconstruction algorithm, including a new maximum-likelihood attenuation correction factor (ML-ACF) for time of flight (TOF) brain positron emission tomography (PET). The implemented algorithm combines an ML-ACF method that simultaneously estimates both the emission image and attenuation sinogram from TOF emission data, and a scaling method based on anatomical features. To evaluate the algorithm's quantitative accuracy, three-dimensional brain phantom images were acquired and soft-tissue attenuation coefficients and emission values were analyzed. The heterogeneous distributions of attenuation coefficients in soft tissue, skull, and nasal cavity were sufficiently visualized. The attenuation coefficient of soft tissue remained within 5% of theoretical value. Attenuation-corrected emission showed no lateral differences, and significant differences among soft tissue were within the error range. The ML-ACF-based attenuation correction implemented for TOF brain PET worked well and obtained practical levels of accuracy.
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