一个密集连接的LDCT图像消除网络,基于双边提取和复合损失下的多尺度关注
Jia Lina1, He Xu1, Huang Aimin1
1School of Physics and Electronic Engineering, Shanxi University, Taiyuan, China.
Journal of X-ray science and technology
|September 24, 2023
概括
这项研究引入了一个新的深度学习网络,DEMACNN,通过减少噪音,提高低剂量计算机断层扫描 (LDCT) 图像质量,同时保留关键边缘细节,以便更好地诊断疾病.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 低剂量计算机断层扫描 (LDCT) 减少了辐射暴露,但引入了噪音,可能阻碍疾病诊断.
- 现有的与边缘提取运营商的深度学习方法在完全解决LDCT图像噪声和细节保存方面存在局限性.
研究的目的:
- 提高低剂量计算机断层扫描 (LDCT) 图像的质量.
- 提出和评估一种新的双边抽取多尺度注意力机制卷积神经网络 (DEMACNN),以改进图像无声化.
主要方法:
- 开发了DEMACNN,在输入图像和特征地图上结合了边缘提取操作员,以保留边缘信息.
- 实现了一个功能增强块,将注意力机制和多尺度模块融合在一起,以优化信息利用.
- 利用剩余学习来提高网络性能并减轻梯度消失.
- 引入了一种复合损失函数 (MSE,关节总变化和边缘损失) 以增强无雾化和边缘保护.
主要成果:
- 与REDCNN,CT-former和EDCNN相比,DEMACNN在腹部LDCT图像中获得了优异的峰值信号噪声比 (PSNR) 和结构相似度指数 (SSIM) 值 (分别为33.3486和0.9104).
- 该网络在头部和胸部LDCT图像数据集上表现强.
结论:
- 拟议的DEMACNN有效地减少了LDCT图像中的噪音.
- 该网络成功地保存了关键的边缘和细节信息,这对于准确的医学诊断至关重要.
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