Uptake time (duration between tracer injection and image acquisition) affects the standardized uptake value (SUV) measured for tumors in 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) images. With dissimilar uptake times, changes in tumor SUVs will be under- or overestimated. This study examines the influence of uptake time on tumor response assessment, using a virtual clinical trials approach. Methods Tumor kinetic parameters were estimated from dynamic 18F-FDG PET scans of breast cancer patients and used to simulate time-activity curves (TACs) for 45–120 minutes post-injection. Five-minute uptake time frames followed four scenarios: (#1) standardized static measurement time (60–65 minutes for all), (#2) uptake times sampled from an academic PET facility with strict adherence to standardization protocols, (#3) distribution similar to #2 but with greater deviation from standards, (#4) mixture of hurried scans (45–65 minute start of image acquisition) and frequent delays (58–115 minute uptake time). The proportion of out-of-range scans (<50 or >70 minutes, or >15 minutes difference between paired scans) was 0%, 20%, 44%, and 64% for scenarios #1, #2, #3, and #4. A published SUV correction based on local linearity of uptake time dependence was applied in a separate analysis. Influence of uptake time variation was assessed as sensitivity for detecting response (probability of observing a change of ≥30% decrease in 18F-FDG PET SUV, given a true decrease of 40%) and specificity (probability of observing absolute change of <30%, given no true change). Results Sensitivity was 96% for scenario #1, and ranged from 73% for scenario #4 (95% confidence interval 70%–76%) to 92% (90%–93%) for scenario #2. Specificity for all scenarios was ≥91%. Single-arm phase II trials required 8%–115% greater sample size for scenarios #2–#4 compared to #1. If uptake time is known, SUV correction methods may raise sensitivity to 87%–95% and reduce the sample size increase to <27%. Conclusion Uptake time deviations from standardized protocols occur frequently, potentially decreasing performance of 18F-FDG PET response biomarkers. Correcting SUV for uptake time improves sensitivity, but algorithm refinement is needed. Stricter uptake time control and effective correction algorithms could improve power and decrease costs for clinical trials using 18F-FDG PET endpoints.
We have previously developed a convergent penalized likelihood (PL) image reconstruction algorithm using the relative difference prior (RDP) and showed that it achieves more accurate lesion quantitation compared to ordered subsets expectation maximization (OSEM). We evaluated the detectability of low-contrast liver and lung lesions using the PL-RDP algorithm compared to OSEM. We performed a two-alternative forced choice study using a channelized Hotelling observer model that was previously validated against human observers. Lesion detectability showed a stronger dependence on lesion size for PL-RDP than OSEM. Lesion detectability was improved using time-of-flight (TOF) reconstruction, with greater benefit for the liver compared to the lung and with increasing benefit for decreasing lesion size and contrast. PL detectability was statistically significantly higher than OSEM for 20 mm liver lesions when contrast was [Formula: see text] ([Formula: see text]), and TOF PL detectability was statistically significantly higher than TOF OSEM for 15 and 20 mm liver lesions with contrast [Formula: see text] and [Formula: see text], respectively. For all other cases, there was no statistically significant difference between PL and OSEM ([Formula: see text]). For the range of studied lesion properties, lesion detectability using PL-RDP was equivalent or improved compared to using OSEM.
Purpose: There are several important positron emission tomography (PET) imaging scenarios that require imaging with very low photon statistics, for which both quantitative accuracy and visual quality should not be neglected. For example, PET imaging with the low photon statistics is closely related to active efforts to significantly reduce radiation exposure from radiopharmaceuticals. We investigated two examples of low-count PET imaging: a) Imaging [ 90 Y]microsphere radioembolization that suffers the very small positron emission fraction of Y-90's decay processes, and b) cancer imaging with [ 68 Ga]citrate with uptake time of 3-4 halflives, necessary for visualizing tumors. In particular, we investigated a type of penalized likelihood
We developed a method to evaluate variations in the PET imaging process in order to characterize the relative ability of static and dynamic metrics to measure breast cancer response to therapy in a clinical trial setting. We performed a virtual clinical trial by generating 540 independent and identically distributed PET imaging study realizations for each of 22 original dynamic fluorodeoxyglucose (18F-FDG) breast cancer patient studies pre- and post-therapy. Each noise realization accounted for known sources of uncertainty in the imaging process, such as biological variability and SUV uptake time. Four definitions of SUV were analyzed, which were SUVmax, SUVmean, SUVpeak, and SUV50%. We performed a ROC analysis on the resulting SUV and kinetic parameter uncertainty distributions to assess the impact of the variability on the measurement capabilities of each metric. The kinetic macro parameter, Ki, showed more variability than SUV (mean CV Ki = 17%, SUV = 13%), but Ki pre- and post-therapy distributions also showed increased separation compared to the SUV pre- and post-therapy distributions (mean normalized difference Ki = 0.54, SUV = 0.27). For the patients who did not show perfect separation between the pre- and post-therapy parameter uncertainty distributions (ROC AUC < 1), dynamic imaging outperformed SUV in distinguishing metabolic change in response to therapy, ranging from 12 to 14 of 16 patients over all SUV definitions and uptake time scenarios (p < 0.05). For the patient cohort in this study, which is comprised of non-high-grade ER+ tumors, Ki outperformed SUV in an ROC analysis of the parameter uncertainty distributions pre- and post-therapy. This methodology can be applied to different scenarios with the ability to inform the design of clinical trials using PET imaging.
Ordered Subset Expectation Maximization (OSEM) is currently the most widely used image reconstruction algorithm for clinical PET. However, OSEM does not necessarily provide optimal image quality, and a number of alternative algorithms have been explored. We have recently shown that a penalized likelihood image reconstruction algorithm using the relative difference penalty, block sequential regularized expectation maximization (BSREM), achieves more accurate lesion quantitation than OSEM, and importantly, maintains acceptable visual image quality in clinical wholebody PET. The goal of this work was to evaluate lesion detectability with BSREM versus OSEM. We performed a twoalternative forced choice study using 81 patient datasets with lesions of varying contrast inserted into the liver and lung. At matched imaging noise, BSREM and OSEM showed equivalent detectability in the lungs, and BSREM outperformed OSEM in the liver. These results suggest that BSREM provides not only improved quantitation and clinically acceptable visual image quality as previously shown but also improved lesion detectability compared to OSEM. We then modeled this detectability study, applying both nonprewhitening (NPW) and channelized Hotelling (CHO) model observers to the reconstructed images. The CHO model observer showed good agreement with the human observers, suggesting that we can apply this model to future studies with varying simulation and reconstruction parameters.
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