Machine Learning in Quantitative PET Imaging

Recent years have witnessed the trend that machine learning, especially deep learning, is being increasingly used in the application of PET imaging. Various t ypes of machine learning networks have been borrowed from computer vision field and adapted to speci fic clinical tasks for PET quantitative imaging. As reviewed in this paper, the most common applicat ions are PET AC and low-count PET reconstruction. It is also an emerging field since all of thes e reviewed studies were published within five years. With the development in both machine learning alg orithm and computing hardware, more learning-based methods are expected to facilitate the clin ical workflow of PET imaging with more potential quantification application. In addition to PET AC and low-count reconstruction, there ar e other topics in PET imaging where machine learning can be exploited. For example, high resolu tion PET has great potential in visualizing and accurately measuring the radiotracer concentrat ion in structures with dimensions of millimeter, while it is subject to the partial volume e ffect due to the limited spatial discriminating ability of scanner.[