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通过基于深度学习的剥离,实现低剂量体外X射线光计算机断层扫描

Naghmeh Mahmoodian¹, Mohammad Rezapourian¹, Asim Abdulsamad Inamdar¹

¹Chair of Medical Systems Technology, Institute for Medical Technology, Faculty of Electrical Engineering and Information Technology, Otto von Guericke University, 39106 Magdeburg, Germany.

Journal of imaging

|June 26, 2024

概括

人工智能,特别是Swin-Conv-UNet (SCUNet) 模型,在X射线光计算机断层成像 (XFCT) 中显著降低了背景噪声. 这种深度学习方法使高质量的分子成像能够在减少辐射暴露的情况下实现.

科学领域:

医疗成像医学成像
人工智能的人工智能
生物医学工程生物医学工程

背景情况:

射线光计算机断层扫描 (XFCT) 提供了高分辨率的,非侵入性的分子成像.
目前的XFCT技术需要高剂量的辐射才能达到足够的灵敏度,这引发了安全问题.
人工智能 (AI),特别是深度学习 (DL),显示出在医学成像中降低噪音的潜力.

研究的目的:

开发和评估一个优化的Swin-Conv-UNet (SCUNet) 深度学习模型,用于减少X射线光 (XRF) 图像中的背景噪声.
为了从低剂量X射线光数据中实现高质量的XFCT成像.
为了减轻XFCT中的成像灵敏度和辐射暴露之间的权衡.

主要方法:

为减少背景噪音,开发了一个优化的Swin-Conv-UNet (SCUNet) 深度学习模型.
该模型使用增强的XRF数据进行训练和评估,重点关注低度的标记物.
图像质量通过使用高峰信号噪声比 (PSNR) 和结构相似度指数 (SSIM) 对高剂量图像进行定量评估.

主要成果:

SCUNet模型成功地从低剂量输入中生成了高质量的XRF图像,实现了最大PSNR为39.05和SSIM为0.86.
拟议的DL算法与BM3D,BM4D,NLM和DnCNN等传统无声化方法相比,表现优越,特别是在高噪声条件下.
视觉检查和定量指标证实了SCUNet模型在降低噪音方面的有效性.

结论:

优化的SCUNet模型有效地降低了XFCT成像中的背景噪声,在低标记物度下.
使用SCUNet的人工智能驱动的消噪允许高质量的XFCT成像,显著减少辐射暴露.
这种方法对于在分子成像中推进XFCT应用具有巨大的潜力.

关键词:

在X射线光 (XRF) 中.美国XFCTXFCT XFCT 人工智能 (AI) 是一种人工智能.癌症癌症癌症癌症癌症深度学习 (DL) 是指深度学习.纳米颗粒是一种纳米粒子.

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