使用深度学习技术来提高图像质量和降低噪音,在临床前的低剂量PET图像中,在sinogram领域
Kishore Krishnagiri Manoj Doss1, Jyh-Cheng Chen1,2,3
1Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Medical physics
|November 15, 2023
概括
深度学习模型通过提高图像质量和减少噪音来增强低剂量PET成像. 这种方法可以生成现实的高剂量PET影像,而不会影响空间分辨率.
科学领域:
- 医疗成像医学成像
- 临床前研究 临床前研究
- 人工智能的人工智能
背景情况:
- 低剂量正子发射断层扫描 (LD-PET) 在临床前研究中对于最大限度地减少辐射暴露至关重要.
- 由于信号噪声比较低,LD-PET图像通常质量差,噪声高.
- 深度学习 (DL) 提供了提高噪音PET图像质量的潜力.
研究的目的:
- 优化图像质量,减少临床前PET图像中的噪声.
- 使用sinogram域作为DL模型的输入.
- 与标准LD-PET相比,实现更好的图像质量.
主要方法:
- 使用生成对抗网络 (GAN) 和卷积神经网络 (CNN) 来从LD-PET中预测高剂量 (HD) PET信号.
- 在微型幽灵,动物实验对象 (老鼠) 和模拟实验中进行实验.
- 使用SSIM,RMSE,PSNR,SNR,CNR,FWHM和FWTM等指标评估图像质量,将DL结果与NLM和BM3D算法进行比较.
主要成果:
- DL模型成功生成了高质量的图像,并改进了定量指标.
- 通过DL生成的PET图像显示FWHM和FWTM的精度明显优于LD,NLM和BM3D,与HD PET的精度相匹配.
- 平均平方误差 (MSE) 损失表明模型性能很好,发电机损失超过了GAN收的区分器损失.
结论:
- 由GAN生成的阴影图与真正的高清临床前PET阴影图非常相似,性能优于LD阴影图.
- 在预测的高清图像中观察到图像质量和降噪的显著改善.
- 在不影响空间分辨率的情况下,DL网络有效地提高了图像质量.
相关概念视频
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