Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, 518055, China.
Sci Rep. 2018 Jun 11;8(1):8799. doi: 10.1038/s41598-018-27261-z.
In this paper, a single-computed tomography (CT) image super-resolution (SR) reconstruction scheme is proposed. This SR reconstruction scheme is based on sparse representation theory and dictionary learning of low- and high-resolution image patch pairs to improve the poor quality of low-resolution CT images obtained in clinical practice using low-dose CT technology. The proposed strategy is based on the idea that image patches can be well represented by sparse coding of elements from an overcomplete dictionary. To obtain similarity of the sparse representations, two dictionaries of low- and high-resolution image patches are jointly trained. Then, sparse representation coefficients extracted from the low-resolution input patches are used to reconstruct the high-resolution output. Sparse representation is used such that the trained dictionary pair can reduce computational costs. Combined with several appropriate iteration operations, the reconstructed high-resolution image can attain better image quality. The effectiveness of the proposed method is demonstrated using both clinical CT data and simulation image data. Image quality evaluation indexes (root mean squared error (RMSE) and peak signal-to-noise ratio (PSNR)) indicate that the proposed method can effectively improve the resolution of a single CT image.
本文提出了一种基于单张计算机断层扫描（CT）图像超分辨率（SR）重建的方案。该方案基于稀疏表示理论和高低分辨率图像块对字典学习，以提高使用低剂量 CT 技术在临床实践中获得的低质量低分辨率 CT 图像的质量。该方案的基本思想是图像块可以通过从过完备字典中提取的元素的稀疏编码来很好地表示。为了获得相似的稀疏表示，联合训练了高低分辨率图像块的两个字典。然后，使用从低分辨率输入块中提取的稀疏表示系数来重建高分辨率输出。使用稀疏表示使得训练的字典对可以降低计算成本。通过结合几个适当的迭代操作，可以获得更好的图像质量的重建高分辨率图像。使用临床 CT 数据和模拟图像数据验证了所提出方法的有效性。图像质量评估指标（均方根误差（RMSE）和峰值信噪比（PSNR））表明，该方法可以有效地提高单张 CT 图像的分辨率。
