Impact of Time-of-Flight PET on Whole-Body Oncologic Studies: A Human Observer Lesion Detection and Localization Study

Phantom studies have shown improved lesion detection performance with time-of-flight (TOF) PET. In this study, we evaluate the benefit of fully 3-dimensional, TOF PET in clinical whole-body oncology using human observers to localize and detect lesions in realistic patient anatomic backgrounds. Our hypothesis is that with TOF imaging we achieve improved lesion detection and localization for clinically challenging tasks, with a bigger impact in large patients. Methods: One hundred patient studies with normal (18)F-FDG uptake were chosen. Spheres (diameter, 10 mm) were imaged in air at variable locations in the scanner field of view corresponding to lung and liver locations within each patient. Sphere data were corrected for attenuation and merged with patient data to produce fused list-mode data files with lesions added to normal-uptake scans. All list files were reconstructed with full corrections and with or without the TOF kernel using a list-mode iterative algorithm. The images were presented to readers to localize and report the presence or absence of a lesion and their confidence level. The interpretation results were then analyzed to calculate the probability of correct localization and detection, and the area under the localized receiver operating characteristic (LROC) curve. The results were analyzed as a function of scan time per bed position, patient body mass index (BMI < 26 and BMI >= 26), and type of imaging (TOF and non-TOF). Results: Our results showed that longer scan times led to an improved area under the LROC curve for all patient sizes. With TOF imaging, there was a bigger increase in the area under the LROC curve for larger patients (BMI > 26). Finally, we saw smaller differences in the area under the LROC curve for large and small patients when longer scan times were combined with TOF imaging. Conclusion: A combination of longer scan time (3 min in this study) and TOF imaging provides the best performance for imaging large patients or a low-uptake lesion in small or large patients. This imaging protocol also provides similar performance for all patient sizes for lesions in the same organ type with similar relative uptake, indicating an ability to provide a uniform clinical diagnosis in most oncologic lesion detection tasks.