CT-Mamba:一种混合卷积状态空间模型,用于低剂量的CT无声化
Linxuan Li1, Wenjia Wei1, Luyao Yang1
1Tianmushan Laboratory, Hangzhou, China; School of Physics, Beihang University, Beijing, China.
概括
新型混合型号CT-Mamba通过结合CNN和Mamba来有效地消除低剂量CT (LDCT) 图像,以增强细节和噪声纹理. 这种方法可以提高诊断的准确性,使得denoised图像与正常剂量CT (NDCT) 图像具有统计学上的相似性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算成像技术的成像
背景情况:
- 低剂量CT (LDCT) 减少了辐射,但引入了噪音和人工物.
- 现有的深度学习解密方法 (CNN,变压器) 在建模远程依赖或计算复杂性方面存在局限性.
- 消音可以改变噪声分布,影响诊断结果.
研究的目的:
- 提出CT-Mamba,一种混合卷积状态空间模型,用于有效的LDCT图像消噪.
- 通过整合本地和全球特征提取来解决当前无雾化技术的局限性.
- 为了确保无噪声图像保持与正常剂量CT (NDCT) 图像相似的噪声特征.
主要方法:
- 开发了CT-Mamba,这是一个混合模型,将CNN结合起来,用于本地特征和Mamba用于远程依赖.
- 实现了一个空间连贯的Z形扫描方案,以实现像素连续性.
- 为训练设计了一个由Mamba驱动的深噪声功率谱 (NPS) 损失函数.
主要成果:
- 在LDCT图像中,CT-Mamba在降低噪音和保存细节方面表现出色.
- 该模型优化了噪声纹理分布,实现了与NDCT图像更高的统计相似性.
- 剥离的图像显示了增强的放射性特征与NDCT图像的相似性.
结论:
- CT-Mamba有效地消除了LDCT图像,保留了细节并改善了噪音质感.
- 混合方法平衡了本地和全球特征提取,以获得卓越的性能.
- 在最不发达国家中,CT-Mamba显示出对Mamba框架应用的承诺,提高诊断价值.
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