在帕金森病的Tc-TRODAT-1 SPECT中转移基于学习的减弱校正,使用现实的模拟和临床数据
Wenbo Huang1, Han Jiang2, Yu Du1,3
1Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China.
EJNMMI physics
|May 6, 2025
概括
使用在蒙特卡洛模拟上预训练的模型进行转移学习,可显著改善多巴胺载体SPECT成像的减弱校正,特别是在临床数据有限的情况下.
科学领域:
- 医疗成像医学成像
- 核医学就是核医学.
- 人工智能的人工智能
背景情况:
- 多巴胺转运体 (DAT) SPECT对于早期发现帕金森病 (PD) 是至关重要的.
- 图像减弱显著阻碍了DAT SPECT的准确性.
- 深度学习 (DL) 方法对减弱校正 (AC) 有希望.
研究的目的:
- 通过使用现实的蒙特卡罗 (MC) 模拟数据来研究转移学习 (TL) 的有效性,用于DAT SPECT中的基于深度学习的减弱校正 (DLAC).
- 通过利用为TL预先训练的模型来提高DAT SPECT中的AC性能.
主要方法:
- 一个3D条件生成对抗网络 (cGAN) 在模拟的SPECT数据 (NAC/CTAC) 上进行了预训练.
- 预训练模型使用有限的临床DAT SPECT数据 (TLAC-MC) 进行了微调.
- 性能与各种DLAC方法和Chang的AC进行了比较.
主要成果:
- 与其他方法相比,TLAC-MC在NMSE和SSIM中取得了优异的表现,特别是在有限的临床数据 (8个数据集) 的情况下.
- 随着微调数据集数量的增加,观察到性能改善.
- 当预训练数据域与目标域密切匹配时,TLAC表现出更大的好处.
结论:
- 对DAT SPECT使用基于模拟的预训练模型进行转移学习减弱校正 (TLAC) 是可行的.
- TLAC-MC提供卓越的交流性能,特别是在低数据的临床场景中.
- 预训练和目标数据之间的域相似性对于有效的TLAC至关重要.
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