基于深度学习的图像噪声量化框架用于计算机断层扫描
Nathan R Huber1, Jiwoo Kim2, Shuai Leng1
1From the Department of Radiology, Mayo Clinic, Rochester, MN.
Journal of computer assisted tomography
|June 28, 2023
概括
这项研究介绍了单扫描图像局部方差估计器 (SILVER),这是一种用于精确计算断层扫描 (CT) 噪声估计的深度学习工具. 银提供从单个扫描的像素智能噪声地图,帮助图像质量评估和协议优化.
科学领域:
- 医疗成像医学成像
- 放射学 放射学是一门学科.
- 医疗保健中的人工智能
背景情况:
- 精确的噪声量化对于计算机断层扫描 (CT) 图像质量和协议优化至关重要.
- 传统的噪声评估方法可能耗时且复杂.
研究的目的:
- 提出和评估一个深度学习框架,单扫描图像局部方差估算器 (SILVER),用于估计CT图像中的局部噪声水平.
- 从单个CT扫描直接生成像素智能噪声图.
主要方法:
- 开发了一个U-Net卷积神经网络架构,用于噪声估计的平均平方误差损失.
- 通过使用12万张幻影CT图像训练模型,并从100次复制扫描中计算出像素智能的噪声图.
- 在幻影和患者图像上评估SILVER性能,并将结果与手动测量进行比较.
主要成果:
- 银色准确地预测了虚拟数据上的噪声地图,与计算的噪声地图 (RMSE <8 HU) 密切匹配.
- 在患者图像上,SILVER与手动感兴趣区域测量相比,显示了5%的低平均百分比误差.
结论:
- SILVER框架可以直接从患者CT图像中精确地对像素进行噪声水平的估计.
- 这种在幻影数据上训练的可访问方法在CT图像分析和优化中具有广泛的适用性.
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