Super-resolution reconstruction of 4D-CT lung data via patch-based low-rank matrix reconstruction
Super-resolution reconstruction of 4D-CT lung data via patch-based low-rank matrix reconstruction
复制标题
通过基于块的低秩矩阵重建对 4D-CT 肺部数据进行超分辨率重建
DOI：
10.1088/1361-6560/aa8a48
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发表时间：
2017-10
影响因子：
3.5
通讯作者：
Zhang Yu
中科院分区：
文献类型：
--
作者：
Fang Shiting;Wang Huafeng;Liu Yueliang;Zhang Minghui;Yang Wei;Feng Qianjin;Chen Wufan;Zhang Yu
肺部4D计算机断层扫描（4D-CT）是一种时间分辨的CT数据采集，在治疗计划和交付中明确包括呼吸运动方面发挥着重要作用。然而，通常以牺牲层间空间分辨率为代价来降低辐射剂量，以尽量减少与辐射有关的健康风险。因此，有必要沿上下方向增强分辨率。为了提高肺部4D-CT图像的分辨率，本文提出了一种基于斑块低秩矩阵重建的超分辨率（SR）重建方法。具体而言，利用补丁搜索策略构建与每个补丁相关的低秩矩阵。然后，在图像退化模型的约束下，利用奇异值收缩对高分辨率斑块进行恢复。最后将输出的高分辨率补丁组装成完整的图像。使用两个公共数据集对该方法进行了广泛的评估。定量分析表明，与线性插值、BP （back projection）和Zhang等算法相比，本文算法的均方根误差降低了9.7% ~ 33.4%，边缘宽度降低了11.4% ~ 24.3%。提出了一种提高4D-CT图像分辨率的新算法。在所有的实验中，本文提出的方法都优于各种插值方法，也优于BP和Zhang等人的方法，表明本文提出的算法的有效性和竞争力。
Lung 4D computed tomography (4D-CT), which is a time-resolved CT data acquisition, performs an important role in explicitly including respiratory motion in treatment planning and delivery. However, the radiation dose is usually reduced at the expense of inter-slice spatial resolution to minimize radiation-related health risk. Therefore, resolution enhancement along the superior–inferior direction is necessary. In this paper, a super-resolution (SR) reconstruction method based on a patch low-rank matrix reconstruction is proposed to improve the resolution of lung 4D-CT images. Specifically, a low-rank matrix related to every patch is constructed by using a patch searching strategy. Thereafter, the singular value shrinkage is employed to recover the high-resolution patch under the constraints of the image degradation model. The output high-resolution patches are finally assembled to output the entire image. This method is extensively evaluated using two public data sets. Quantitative analysis shows that the proposed algorithm decreases the root mean square error by 9.7%–33.4% and the edge width by 11.4%–24.3%, relative to linear interpolation, back projection (BP) and Zhang et al’s algorithm. A new algorithm has been developed to improve the resolution of 4D-CT. In all experiments, the proposed method outperforms various interpolation methods, as well as BP and Zhang et al’s method, thus indicating the effectivity and competitiveness of the proposed algorithm.
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影响因子：
3.8
作者：
Vandemeulebroucke, Jef;Rit, Simon;Sarrut, David
通讯作者：
Sarrut, David
DOI：
10.1016/j.ijrobp.2014.01.016
发表时间：
2014-05-01
影响因子：
7
作者：
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通讯作者：
Low, Daniel
影响因子：
3.8
作者：
Yin, Youbing;Hoffman, Eric A.;Lin, Ching-Long
通讯作者：
Lin, Ching-Long
影响因子：
10.6
作者：
Yu Zhang;Guorong Wu;P. Yap;Qianjin Feng;Jun Lian;Wufan Chen;D. Shen
通讯作者：
Yu Zhang;Guorong Wu;P. Yap;Qianjin Feng;Jun Lian;Wufan Chen;D. Shen
DOI：
10.1109/iccv.2009.5459463
发表时间：
2010-07
期刊：
2009 IEEE 12th International Conference on Computer Vision
影响因子：
--
作者：
Ji Liu;Przemyslaw Musialski;P. Wonka;Jieping Ye
通讯作者：
Ji Liu;Przemyslaw Musialski;P. Wonka;Jieping Ye