The objective of this investigation was to propose techniques for determining which patients are likely to benefit from quantitative respiratory-gated imaging by correlating respiratory patterns to changes in positron emission tomography (PET) metrics. Twenty-six lung and liver cancer patients underwent PET/computed tomography exams with recorded chest/abdominal displacements. Static and adaptive amplitude-gated [F18]fluoro-D-glucose (FDG) PET images were generated from list-mode acquisitions. Patients were grouped by respiratory pattern, lesion location, or degree of lesion attachment to anatomical structures. Respiratory pattern metrics were calculated during time intervals corresponding to PET field of views over lesions of interest. FDG PET images were quantified by lesion maximum standardized uptake value (SUVmax). Relative changes in SUVmax between static and gated PET images were tested for association to respiratory pattern metrics. Lower lung lesions and liver lesions had significantly higher changes in SUVmax than upper lung lesions (14 versus 3%, p<0.0001). Correlation was highest (0.42±0.10, r2=0.59, p<0.003) between changes in SUVmax and nonstandard respiratory pattern metrics. Lesion location had a significant impact on changes in PET quantification due to respiratory gating. Respiratory pattern metrics were correlated to changes in SUVmax, though sample size limited statistical power. Validation in larger cohorts may enable selection of patients prior to acquisition who would benefit from respiratory-gated PET imaging.
KEYWORDS: Collimation, Positron emission tomography, Scanners, 3D metrology, Microchannel plates, Monte Carlo methods, 3D acquisition, Sensors, Collimators, Single photon
We present a simulation study of the countrate performance of a PET scanner with partial collimation. In this study, partial collimation is achieved by removal of every other septum from the standard 2D septa set for the GE Advance PET scanner. System behavior is evaluated with a photon tracking simulation package (SimSET) and calibrated to measured data for 2D and fully-3D acquisition modes using the NEMA NU-2 countrate phantom. Results are evaluated in terms of true, scattered, and random coincidences and noise equivalent counts (NEC) regarding both counts per image plane and total counts as a function of activity. Our results show a good agreement between the measured and simulated count rates for the Advance PET scanner for the 2D (full collimation) and fully-3D (no collimation) acquisition modes, increasing our confidence in the predicted countrate results for the partial collimation mode. The latter results in a countrate performance intermediate between the 2D and fully-3D acquisition modes and yields a more favorable countrate performance for clinical activity levels.
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