Optimized Monte-Carlo Scatter and Prompt Gamma Correction for WholeBody and Cardiac PET Imaging

Le résumé fourni par la source

Accurate scatter correction is of great importance for quantitative PET imaging. However, the current widely used model-based scatter estimation methods on clinical PET scanners is prone to correction errors at some scenarios and result in artifacts. This situation becomes even worse when the scans employ non-pure tracers which can emit the prompt gammas and formulate accidental coincidences while paring one or both 511 keV annihilation photons. Accurate estimation of the scatter and prompt gamma is particularly crucial for cardiac and oncological imaging with non-pure tracers. In this study we investigated and developed a multi-purpose Monte-Carlo (MC) simulation method to provide accurate estimation of the scatter and prompt gamma for quantitative correction. We implemented a simulator to generate accidental coincidences by recording the list-mode singles events with different energy emission and decay information, then sorting the coincidences of the two singles events (one from 511 keV & one from prompt gamma) by matching at the same decay location. To cover the scatter contribution from both inside and outside of FOV, we construct the extended body emission and attenuation images from the adjacent bed scans for estimation. We perform a fast ultra-low count simulation and process the MC histogram through down-sampling, up-sampling and interpolation to reach sufficient statistics, then via denormalization and scaling to match and align with the measured data. After a few iterative updates of the above steps and reconstruction, the scatter mean can quickly reach the convergence. The results demonstrate that our proposed approach can outperform the current model-based methods and lead to a greatly improved quantitative accuracy for clinical PET imaging. Our approach has been applied and validated in multiple phantoms and clinical studies on the GE HealthCare scanners involving various tracer tasks.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.