Sparse Coding Super-Resolution Scheme for Chest Computed Tomography
Sparse Coding Super-Resolution Scheme for Chest Computed Tomography
复制标题
胸部计算机断层扫描稀疏编码超分辨率方案
DOI：
10.1166/jmihi.2018.2399
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发表时间：
2018
影响因子：
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通讯作者：
Ishida Takayuki
中科院分区：
文献类型：
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作者：
Ota Junko;Umehara Kensuke;Ishimaru Naoki;Ohno Shunsuke;Okamoto Kentaro;Suzuki Takanori;Ishida Takayuki
高分辨率胸部计算机断层扫描图像现在在诊断中具有非常重要的意义。然而，与低分辨率计算机断层扫描相比，这种方式需要使用更高的辐射剂量和更长的扫描时间。在本研究中，我们应用稀疏编码超分辨率方法在不增加辐射剂量的情况下重建高分辨率图像。我们通过在低分辨率和高分辨率补丁之间进行映射来准备一个超完备字典，并将其表示为低分辨率输入的每个补丁的稀疏线性组合。这些系数用于重建高分辨率输出。在我们的实验中，分析了 89 次计算机断层扫描。我们对图像进行了 2 或 4 次上采样，并将稀疏编码超分辨率方案的图像质量与传统插值方案的最近邻和双线性插值的图像质量进行了比较。通过测量峰值信噪比和结构相似性来评估图像质量。稀疏编码超分辨率方法与最近邻或双线性方法在峰值信噪比和结构相似性方面的差异具有统计学意义。视觉评估证实稀疏编码超分辨率方法生成高分辨率图像，而传统插值方法生成过平滑图像。综上所述，这些结果表明，稀疏编码超分辨率方法是一种用于对计算机断层扫描图像进行上采样的稳健方法，并且在放大胸部计算机断层扫描时，它会产生具有明显高分辨率的图像。
High-resolution chest computed tomography images now has a great importance in the diagnosis. However, this modality requires using a higher radiation dose and a longer scanning time compared to low-resolution computed tomography. In this study, we applied the sparse coding super-resolution method to reconstruct high-resolution images without increasing the radiation dose. We prepared an over-complete dictionary by mapping between low- and high-resolution patches and represented this as a sparse linear combination of each patch of the low-resolution input. These coefficients were used to reconstruct the high-resolution output. In our experiments, 89 computed tomography scans were analyzed. We up-sampled the images 2 or 4 times and compared the image quality of the sparse coding super-resolution scheme with those of the nearest neighbor and bilinear interpolations, which are the traditional interpolation schemes. The image quality was evaluated by measuring the peak signal-to-noise ratio and structure similarity. The differences in the peak signal-to-noise ratios and structure similarities between the sparse coding super-resolution method and the nearest neighbor or bilinear method were statistically significant. Visual assessment confirmed that the sparse coding super-resolution method generated high-resolution images, whereas the conventional interpolation methods generated over-smoothed images. Taken together, these results suggest that the sparse coding super-resolution approach is a robust method for up-sampling computed tomography images and that it yields images with markedly high resolution when magnifying chest computed tomography scans.