Amyloid-beta positron emission tomography (PET) is used for the diagnosis of Alzheimer’s disease (AD). However, the inherent radiation of radioactive tracers used for PET is potentially harmful to the human body. In this study, we present a deep-learning framework for generating high-quality standard-dose PET brain images from scans that have a simulated reduced injected dose of 12.5% of the standard injected dose, thus reducing radiation exposure without compromising image quality. This novel approach achieves remarkable similarity to full-dose images in both visual and quantitative aspects. Our method offers the potential of enabling safer and more accessible PET imaging for early Alzheimer’s disease detection.
Computed tomography (CT) is a widely available, low-cost neuroimaging modality primarily used as a brain examination tool for visual assessment of structural brain integrity in neurodegenerative diseases such as dementia disorders. In this study, we developed a deep learning model to expand the applications of CT to morphological brain segmentation and volumetric extraction. We trained densely connected 3D convoluted neural network variants called U-Nets to segment intracranial volume (ICV), grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF). Dice similarity scores and volumetric Pearson correlation were the evaluation metrics incorporated. Our pilot study created a model that enables automated segmentation in CT with results comparable to magnetic resonance imaging.
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