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Updated: Jul 5, 2026

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图像中PET数据的同时减弱和散射校正:图像对图像深度学习模型的定量和临床评估

Avishan Elkayee Dehno1, Pardis Ghafarian2, Hossein Arabi3

  • 1Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran; Research Center for Molecular and Cellular Imaging (RCMCI), Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
|December 7, 2025
PubMed
概括

深度学习模型,UNET和CGAN,可以将非衰减分散校正 (NASC) 大脑PET/CT图像转换为测量衰减和分散校正 (MASC) 图像. 这种方法对脑PET成像具有前景,特别是当CT无法使用时.

关键词:
减弱和散射纠正的减弱和分散纠正脑部成像 脑部成像深度学习是一种深度学习.没有了,没有了,没有了.在PET-CT中使用.放射性特征 放射性特征 放射性特征在UNETET上,您可以使用.

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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
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