通过使用集成的全球光谱卷积神经网络,学习了用于CEST图像否定的时空空间相关性先验
Huan Chen1, Xinran Chen1, Liangjie Lin2
1Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China.
Magnetic resonance in medicine
|June 19, 2023
概括
一种新的深度学习方法,DECENT,通过利用时空相关性,有效地否定化学交换和转移 (CEST) 图像. 这种先进的技术在低信号噪声比率的场景中优于现有的方法.
科学领域:
- 生物医学成像技术 生物医学成像技术
- 医疗成像中的人工智能
- 图像处理 图像处理
背景情况:
- 化学交换和转移 (CEST) 成像对于各种医疗应用至关重要.
- 在CEST图像中,低信号噪声比 (SNR) 对准确分析构成重大挑战.
- 现有的无声化方法经常与CEST数据中复杂的噪声模式作斗争.
研究的目的:
- 开发一种基于深度学习的新方法,即Denoising CEST网络 (DECENT),用于有效的CEST图像排斥.
- 利用CEST图像中固有的时空相关性来改善降噪.
- 为在低SNR条件下提高CEST图像质量提供强大高效的解决方案.
主要方法:
- DECENT使用双通道U-Net架构,具有不同的卷积内核大小,以捕获全球和光谱特征.
- 融合途径集成了两条平行途径的特征,以实现全面的无声化.
- 该方法通过使用数值模拟,幻影实验和在小鼠大脑和人类骨肌肉上的体内研究来验证.
主要成果:
- 与NLmCED,MLSVD和BM4D等最先进的方法相比,DECENT表现出更优异的脱光性能,正如峰值SNR (PSNR) 和结构相似度指数 (SSIM) 的指标所证明的那样.
- 该网络在模拟和实验低SNR CEST图像中有效地减少了Rician噪声.
- DECENT提供了一个计算效率高的替代方案,避免了复杂的参数调整和漫长的代过程.
结论:
- DECENT成功地利用CEST图像之前的时空相关性,从噪音数据中恢复无噪音的观测.
- 拟议的深度学习方法显著优于现有的除技术.
- 在临床和研究环境中,DECENT为提高CEST成像的诊断实用性提供了一个有前途的工具.
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