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Prediction of standard-dose brain PET image by using MRI and low-dose brain [F-18]FDG PET images

机译:使用MRI和低剂量大脑[F-18] FDG PET图像预测标准剂量的大脑PET图像

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摘要

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose 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-dose PET images using low-dose 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-dose brain [F-18]FDG PET image by using a low-dose brain [F-18]FDG PET image and its corresponding magnetic resonance imaging (MRI) image.
机译:目的:正电子发射断层扫描(PET)是一种核医学成像技术,可产生反映人体组织代谢活动的3D图像。 PET已广泛用于各种临床应用,例如脑部疾病的诊断。高质量的PET图像在诊断脑部疾病/疾病中起着至关重要的作用。实际上,为了获得高质量的PET图像,需要使用标准剂量的放射性核素(示踪剂)并将其注入生物体内。结果,将不可避免地增加患者对放射线的照射。解决此问题的一种方法是使用低剂量PET图像预测标准剂量PET图像。迄今为止,尚无关于这种方法的研究报道。因此,在本文中,作者提出了一种基于回归森林的框架,该框架通过使用低剂量脑[F-18] FDG PET图像及其相应的磁共振成像来预测标准剂量脑[F-18] FDG PET图像(MRI)图像。

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