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Small Animal PET Imaging: Towards an Imaging Analysis Approach for System Average Performance Conclusion

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Biomedical and Computational Biology (BECB 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13637))

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Abstract

The purpose of this study is to present a novel small animal PET imaging system, which can be integrated with a small animal CT or MRI imaging in a PET/CT or PET/MRI schemes. The specific small animal PET imaging design consists of two planar detectors, with LYSO crystals, followed by Position Sensitive Photomultiplier tubes (PSPMTs). The output of the each PSPMTS is led to the corresponding modern electronics for analysis and digitization while at the end is stored on a host computer for further processing. In order to evaluate the average performance of the aforementioned PET system, SIMSET simulation tool was used. So, the small animal PET imaging system was simulated, taking into account that the radioactive source inside it’s field of view was a Derenzo-like phantom full of FDG. Attenuation and scatter phenomena, as well as random events were also considered during simulation process. The simulated data (Derenzo-like phantom sinogram) was then reconstructed with the ordered subsets versions of EM-ML, ISRA and WLS algorithms, considering regularization and penalized schemes. Regularization techniques accelerate reconstruction procedure while penalized methods increase signal to noise ratios with small image resolution degradation. Reconstructed images are further analyzed and evaluated so that a general conclusion on final system resolution output to be obtained.

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Authors and Affiliations

  1. University of West Attica, Athens, Greece

    Evangelia K. Karali

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  1. Evangelia K. Karali
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Correspondence to Evangelia K. Karali .

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Editors and Affiliations

  1. University of Technology Sydney, Sydney, NSW, Australia

    Shiping Wen

  2. Nanchang Hangkong University, Nanchang, China

    Cihui Yang

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Karali, E.K. (2023). Small Animal PET Imaging: Towards an Imaging Analysis Approach for System Average Performance Conclusion. In: Wen, S., Yang, C. (eds) Biomedical and Computational Biology. BECB 2022. Lecture Notes in Computer Science(), vol 13637. Springer, Cham. https://doi.org/10.1007/978-3-031-25191-7_58

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  • DOI: https://doi.org/10.1007/978-3-031-25191-7_58

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