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TOF-PET image reconstruction with multiple timing kernels applied on Cherenkov radiation in BGO
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  • Nikos Efthimiou ,
  • Nicolaus Kratochwil ,
  • Stefan Gundacker ,
  • Andea Polesel ,
  • Matteo Salomoni ,
  • Etiennette Auffray ,
  • Marco Pizzichemi
Nikos Efthimiou
University of Pennsylvania

Corresponding Author:[email protected]

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Nicolaus Kratochwil
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Stefan Gundacker
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Andea Polesel
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Matteo Salomoni
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Etiennette Auffray
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Marco Pizzichemi
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Today Time-Of-Flight in PET scanners assumes a unique, well-defined timing resolution for all coincidence events.
However, recent BGO-Cherenkov detectors, combining prompt Cherenkov emission and the typical BGO scintillation signal, are capable of sorting events into multiple mixture timing kernels and therefore increase the available information for the image reconstruction. The number of Cherenkov photons detected per event impacts directly the signal rise time, which can therefore be used to improve the timing resolution.
In this work, we present a simulation toolkit that outputs data with multiple timing resolutions and image reconstruction that incorporates this information.
A full cylindrical BGO-Cherenkov PET model was compared, in terms of contrast recovery and contrast-to-noise ratio, against non-TOF and an LYSO model with time resolution of 213 ps.
Two reconstruction approaches for the mixture kernels were tested; mixture Gaussian and decomposed simple Gaussian models. The decomposed model used the exact mixture component applied in the simulation.
Images reconstructed using mixture kernels provided similar mean value and less noise as compared to the decomposed simple Gaussian kernels. Although, the later converged faster.
Related to the standard LYSO model, the BGO-Cherenkov provided similar contrast, although, in most cases, with more iterations.
However, due to the higher sensitivity, the contrast-to-noise ratio was 26.4% better for the BGO model.
Sep 2021Published in IEEE Transactions on Radiation and Plasma Medical Sciences volume 5 issue 5 on pages 703-711. 10.1109/TRPMS.2020.3048642