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tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High\u2010quality PET images play an essential role in diagnosing brain diseases\/disorders. In practice, in order to obtain high\u2010quality PET images, a standard\u2010dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient's exposure to radiation. One solution to solve this problem is predicting standard\u2010dose PET images using low\u2010dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard\u2010dose brain [<jats:sup>18<\/jats:sup>F]FDG PET image by using a low\u2010dose brain [<jats:sup>18<\/jats:sup>F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods:<\/jats:title><jats:p>The authors employ a regression forest for predicting the standard\u2010dose brain [<jats:sup>18<\/jats:sup>F]FDG PET image by low\u2010dose brain [<jats:sup>18<\/jats:sup>F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low\u2010dose PET and MRI images to build tissue\u2010specific models that can be used to initially predict standard\u2010dose brain [<jats:sup>18<\/jats:sup>F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results:<\/jats:title><jats:p>The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low\u2010dose PET, and standard\u2010dose PET images, using leave\u2010one\u2010out cross\u2010validations. The proposed algorithm gives promising results with well\u2010estimated standard\u2010dose brain [<jats:sup>18<\/jats:sup>F]FDG PET image and substantially enhanced image quality of low\u2010dose brain [<jats:sup>18<\/jats:sup>F]FDG PET image.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions:<\/jats:title><jats:p>In this paper, the authors propose a framework to generate standard\u2010dose brain [<jats:sup>18<\/jats:sup>F]FDG PET image using low\u2010dose brain [<jats:sup>18<\/jats:sup>F]FDG PET and MRI images. 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