4.7 Article

PET Counting Response Variability Depending on Tumor Location, Activity, and Patient Obesity: A Feasibility Study of Solitary Pulmonary Nodule Using Monte Carlo

Journal

IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 38, Issue 7, Pages 1763-1774

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2019.2891578

Keywords

Image quality assessment; lung; nuclear imaging

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We aim to investigate the counting response variations of positron emission tomography (PET) scanners with different detector configurations in the presence of solitary pulmonary nodule (SPN). Using experimentally validated Monte Carlo simulations, the counting performance of four different scanner models with varying tumor activity, location, and patient obesity is represented using a noise equivalent count rate (NECR). NECR is a well-established quantitative metric which has positive correlation with clinically perceived image quality. The combined effect of tumor displacement and increased activity shows a linear ascending trend for NECR with slope ranges of (12.5-18.2)*10(-3) (kBq/cm(3))(-1) for three-ring (3R) scanners and (15.3-21.5)*10(-3) (kBq/cm(3))(-1) for four-ring (4R). The trend for the combined effect of tumor displacement and patient obesity is exponential decay with 3R configurations weakly dependent on the patient obesity if the tumor is located at the center of the field of view with exponent's range of (6.6-33.8)*10(-2) cm(-1). The dependence is stronger for 4R scanners (9.6-38.5)*10(-2) cm(-1). The analysis indicates that quantitative PET data from the same SPN patient possibly examined in different time points (e.g., during staging or for the evaluation of treatment response) are affected by the different detector configurations and need to be normalized with patient weight, activity, and tumor location to reduce unwanted bias of the diagnosis. This paper provides also with a proof of concept for the ability of properly tuned simulations to provide additional insights into the counting response variability especially in tumor types where often borderline decisions have to be made regarding their characterization.

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