Scan time reduction with advanced PET reconstruction: Preserving lesion detection performance

Lesion-detection performance in oncologic PET depends in part upon count statistics, with shorter scans having higher noise and reduced lesion detectability. However, advanced techniques such as time-of-flight (TOF) and point spread function (PSF) modeling can improve lesion detectability. This work investigates the relationship between reducing count levels (as a surrogate for scan time) and reconstructing with PSF model and TOF. A series of 24 whole-body phantom scans was acquired on a Biograph mCT TOF PET/CT scanner using the experimental methodology prescribed for the Utah PET Lesion Detection Database. Six scans were acquired each day over 4 days, with up to 23 ⁶⁸Ge shell-less lesions (diam. 6, 8, 10, 12, 16mm) distributed throughout the phantom thorax and pelvis. Each scan acquired 6 bed positions at 240s/bed in listmode format. The list-mode files were then statistically pruned, preserving Poisson statistics, to equivalent count levels for scan times of 180s, 120s, 90s, 60s, 45s, 30s, and 15s per bed field-of-view. Each dataset was reconstructed using ordinary Poisson line-of-response (LOR) OSEM, with PSF model, with TOF, and with PSF+TOF. Localization receiver operating characteristics (LROC) analysis was then performed using the channelized non-prewhitened (CNPW) observer. The results were analyzed to delineate the relationship between scan time, reconstruction method, and strength of post-reconstruction filter. Lesion-detection performance degraded as scan time was reduced, and progressively stronger filters were required to maximize performance for the shorter scans. PSF modeling and TOF were found to improve detection performance, offsetting in part the reduced detectability for shorter scans. Notably, PSF+TOF at 120s per bed position preserved essentially the same detection performance as the baseline reconstruction at 240s/bed - effectively cutting whole-body scan time in half without degrading lesion-detection performance in these data.