An assessment of PET dose reduction with penalized likelihood image reconstruction using a computationally efficient model observer

Howard C. Gifford; C. Ross Schmidtlein; Andrzej Krol; Yuesheng Xu

doi:10.1117/12.2550856

16 March 2020 An assessment of PET dose reduction with penalized likelihood image reconstruction using a computationally efficient model observer

Howard C. Gifford, C. Ross Schmidtlein, Andrzej Krol, Yuesheng Xu

Author Affiliations +

Proceedings Volume 11312, Medical Imaging 2020: Physics of Medical Imaging; 113120T (2020) https://doi.org/10.1117/12.2550856
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Developing PET reconstruction algorithms with improved low-count capabilities may provide a timely and cost- effective means of reducing radiation dose in promising clinical applications such as immuno-PET that require long-lived radiotracers. For many PET clinics, the reconstruction protocol consists of postsmoothed ordered-sets expectation-maximization (OSEM) reconstruction, but penalized likelihood methods based on total-variation (TV) regularization could substantially reduce dose. We performed a task-based comparison of postsmoothed OSEM and higher-order TV (HOTV) reconstructions using simulated images of a contrast-detail phantom. An anthropomorphic visual-search model observer read the images in a location-known receiver operating characteristic (ROC) format. Acquisition counts, target uptake, and target size were study variables, and the OSEM postfiltering was task-optimized based on count level. A psychometric analysis of observer performance for the selected task found that the HOTV algorithm allowed a two-fold reduction in dose compared to the optimized OSEM algorithm.

Citation Download Citation

Howard C. Gifford, C. Ross Schmidtlein, Andrzej Krol, and Yuesheng Xu "An assessment of PET dose reduction with penalized likelihood image reconstruction using a computationally efficient model observer", Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113120T (16 March 2020); https://doi.org/10.1117/12.2550856

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