Characterizing, optimizing, and harmonizing cancer detection with PET imaging

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Project summary Detection and diagnosis of smaller and earlier-stage cancers significantly improves a patient's chances of survival. Positron emission tomography (PET) imaging using fluorine 18–fluorodeoxyglucose (FDG-PET) provides a functional or metabolic assessment of normal versus cancerous tissues, and since 2000 has been widely used clinically for the detection and diagnosis of many cancers. Studies over a decade ago by our group and others had shown that it was feasible to both measure and improve the detection ability of FDG-PET imaging for cancer by adjusting acquisition and image reconstruction parameters. This could be done systematically by evaluating the effect on observer models that replicated human performance (i.e. radiologists or nuclear medicine physicians). At the time, however, it is challenging to understand how this varied across systems with different resolutions, sensitivities, and reconstruction algorithms, or if they were operated differently across imaging sites. Over the last decade there have been dramatic improvements in scanner resolution, sensitivity, and reconstruction algorithms, as well as the routine adoption of time-of-flight PET imaging. In parallel there has been an improved understanding and adoption of model observers, as well as pathways for adoption or harmonization of methods across multiple PET manufacturers and imaging sites. Most recently there has been the development of machine intelligence algorithms, such deep neural networks, for both image reconstruction and image analysis, which have the potential to improve performance. We are proposing to take advantage of these developments to characterize, optimize, and harmonize cancer detection with PET imaging. The three specific aims are: (1) Develop methods for characterization (i.e. measurement) of detection performance for FDG PET imaging. (2) Using a model system calibrated to a modern physical system we will then determine how to optimize cancer detection as a function of acquisition and image reconstruction parameters. (3) Finally we will develop a platform-independent (vendor agnostic) standard that can be applied across systems and imaging sites. This will lead to a roadmap for multi-site and multi-vendor implementation approaches that optimizing cancer detectability and thus improved patient outcomes.