Clin Nucl Med. 2018 Jun;43(6):e212-e214. doi: 10.1097/RLU.0000000000002115.
Dementia is an important cause of morbidity and mortality worldwide and encompasses a very heterogenous group of disease processes. Positron emission tomography (PET) of the brain using fluorodeoxyglucose (FDG) is a useful modality for differentiating types of dementia. Because FDG does not bind to pathologic proteins, FDG-PET requires that the reader recognize characteristic patterns of glucose hypometabolism to identify pathology. These patterns have been documented in the literature for both primary neurodegenerative disorders and secondary causes of dementia. This article presents an algorithm for organizing these findings and systematically applying them to interpret FDG-PET brain imaging for dementia.
痴呆是全球范围内发病率和死亡率的重要原因,包含了一组非常异质的疾病过程。使用氟脱氧葡萄糖(FDG)的正电子发射断层扫描(PET)是区分痴呆类型的有用方式。由于 FDG 不会与病理蛋白结合,因此 FDG-PET 需要读者识别葡萄糖代谢低下的特征模式以识别病理。这些模式在原发性神经退行性疾病和痴呆的继发性病因的文献中均有记载。本文提出了一种算法,用于对这些发现进行分类,并系统地将其应用于解释痴呆的 FDG-PET 脑成像。