图像翻译用于从结构性MRI中估计二维轴性粉样蛋白-β PET
Fernando Vega1,2,3,4, Abdoljalil Addeh1,2,3,4, Aravind Ganesh3,4,5
1Department of Biomedical, University of Calgary, Calgary, Alberta, Canada.
Journal of magnetic resonance imaging : JMRI
|November 3, 2023
概括
这项研究开发了一个模型,从MRI扫描中创建Amyloid-beta PET图像,显示与真实PET图像的高度相似性. 这为阿尔茨海默病研究提供了更便宜,更容易获得的替代方案.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
背景情况:
- 阿尔茨海默病的特点是粉样β斑块和大脑缩.
- pozitron 辐射断层扫描 (PET) 和磁共振成像 (MRI) 用于检测这些标志.
- 磁力共振成像 (MRI) 比PET更容易获得和更少的侵入性,其数据可能会根据已知的相关性推断PET图像.
研究的目的:
- 使用条件生成对抗网络 (cGAN) 开发一个图像翻译模型.
- 该模型旨在从结构性MRI数据中合成粉样βPET图像.
主要方法:
- 一项涉及882名患有不同认知衰退的成年人的回顾性研究.
- 在552名受试者身上训练了一种cGAN模型,并使用T1加权MRI对331名受试者进行了测试.
- 模型性能使用结构相似性指数测量 (SSIM) 和峰值信号噪声比 (PSNR) 以及盲目观察员评估进行了评估.
主要成果:
- 合成的PET图像与实际的PET图像具有很高的相似性 (平均SSIM=0.905,PSNR=2.685).
- 像素智能的皮尔森相关性显示R2 > 0.96.
- 盲目观察者无法显著区分真实和合成图像 (P = 0.68).
结论:
- 合成结构性MRI的Amyloid-beta PET图像是可能的.
- 开发的模型具有很高的相似性,这表明在阿尔茨海默氏症研究中可能有更广泛的应用.
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