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HCformer:用于LDCT的混合CNN-变压器图像去除

Jinli Yuan¹, Feng Zhou¹, Zhitao Guo²

¹The School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China.

Journal of digital imaging

|June 29, 2023

概括

这项研究介绍了HCformer,这是一个混合深度学习模型,将CNN和变压器结构结合起来,用于低剂量计算机断层扫描 (LDCT) 图像消除. HCformer有效地减少噪音,同时保持图像细节,提高医学成像中的诊断精度.

科学领域:

医疗成像医学成像
人工智能的人工智能
放射学放射学是一门学科.

背景情况:

低剂量计算机断层扫描 (LDCT) 减少了患者的辐射暴露,但引入了图像噪声,阻碍了诊断的准确性.
现有的深度学习否定方法,主要是卷积神经网络 (CNN),由于它们专注于局部信息,因此难以建模复杂结构.
变压器模型提供全球背景,但对于医疗图像处理而言,计算密集.

研究的目的:

开发一种先进的图像后处理方法,用于高质量的LDCT图像重建.
结合CNN和变压器的优势,克服当前无线化技术的局限性.
为了提高从LDCT扫描的临床诊断的精度.

主要方法:

一个混合CNN-变压器 (HCformer) 编解码器网络被提议用于LDCT图像无声化.
设计了一个社区功能增强 (NEF) 模块,将本地信息集成到变压器中.
转移窗口多头自我注意 (SW-MSA) 和窗口多头自我注意 (W-MSA) 交替使用,以减少计算复杂性和增强层间信息交互.

主要成果:

HCformer模型显著改善了图像质量指标:SSIM从0.8017增加到0.8507,HuRMSE从34.1898减少到17.7213,FSIM从0.6885增加到0.7247.
该算法成功地减少了噪音,同时保留了关键的图像细节.
在AAPM 2016 LDCT大挑战数据集上的消去和比较实验证实了HCformer在其他方法上的优越性能.

结论:

拟议的HCformer有效地结合了CNN和变压器架构,实现了卓越的LDCT图像消噪.
在低剂量CT应用中,HCformer显示了提高医疗图像质量的巨大潜力.
该方法提供了一个有希望的解决方案,通过提供高质量,无色图像来提高LDCT的诊断准确性.

关键词:

扫描图像消噪的CT图像深度学习是一种深度学习.低剂量的CT图像

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