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DaNet: dose-aware network embedded with dose-level estimation for low-dose CT imaging
Physics in Medicine & Biology ( IF 3.5 ) Pub Date : 2021-01-13 , DOI: 10.1088/1361-6560/abc5cc
Zhenxing Huang 1, 2, 3, 4 , Zixiang Chen 4 , Jincai Chen 1, 2, 3, 5 , Ping Lu 1, 2, 3 , Guotao Quan 6 , Yanfeng Du 6 , Chenwei Li 6 , Zheng Gu 7 , Yongfeng Yang 4, 8 , Xin Liu 4, 8 , Hairong Zheng 4, 8 , Dong Liang 4, 8 , Zhanli Hu 4, 5, 8
Affiliation  

Many deep learning (DL)-based image restoration methods for low-dose CT (LDCT) problems directly employ the end-to-end networks on low-dose training data without considering dose differences. However, the radiation dose difference has a great impact on the ultimate results, and lower doses increase the difficulty of restoration. Moreover, there is increasing demand to design and estimate acceptable scanning doses for patients in clinical practice, necessitating dose-aware networks embedded with adaptive dose estimation. In this paper, we consider these dose differences of input LDCT images and propose an adaptive dose-aware network. First, considering a large dose distribution range for simulation convenience, we coarsely define five dose levels in advance as lowest, lower, mild, higher and highest radiation dose levels. Instead of directly building the end-to-end mapping function between LDCT images and high-dose CT counterparts, the dose level is primarily estimated in the first stage. In the second stage, the adaptively learned low-dose level is used to guide the image restoration process as the pattern of prior information through the channel feature transform. We conduct experiments on a simulated dataset based on original high dose parts of American Association of Physicists in Medicine challenge datasets from the Mayo Clinic. Ablation studies validate the effectiveness of the dose-level estimation, and the experimental results show that our method is superior to several other DL-based methods. Specifically, our method provides obviously better performance in terms of the peak signal-to-noise ratio and visual quality reflected in subjective scores. Due to the dual-stage process, our method may suffer limitations under more parameters and coarse dose-level definitions, and thus, further improvements in clinical practical applications with different CT equipment vendors are planned in future work.



中文翻译:

DaNet:嵌入剂量水平估计的剂量感知网络,用于低剂量 CT 成像

许多针对低剂量 CT (LDCT) 问题的基于深度学习 (DL) 的图像恢复方法直接在低剂量训练数据上使用端到端网络,而不考虑剂量差异。但辐射剂量差异对最终结果影响较大,剂量越低,修复难度越大。此外,在临床实践中为患者设计和估计可接受的扫描剂量的需求不断增加,因此需要嵌入自适应剂量估计的剂量感知网络。在本文中,我们考虑了输入 LDCT 图像的这些剂量差异,并提出了一种自适应剂量感知网络。首先,考虑到较大的剂量分布范围,为便于模拟,我们预先粗略地定义了五个剂量水平,分别为最低、较低、轻度、较高和最高辐射剂量水平。不是在 LDCT 图像和高剂量 CT 对应物之间直接构建端到端映射函数,而是主要在第一阶段估计剂量水平。在第二阶段,自适应学习的低剂量水平通过通道特征变换作为先验信息的模式来指导图像恢复过程。我们在模拟数据集上进行实验,该数据集基于来自梅奥诊所的美国医学物理学家协会原始高剂量部分挑战数据集。消融研究验证了剂量水平估计的有效性,实验结果表明我们的方法优于其他几种基于 DL 的方法。具体来说,我们的方法在峰值信噪比和主观分数反映的视觉质量方面提供了明显更好的性能。

更新日期:2021-01-13
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