Kang Yanqin, Liu Jin, Wu Fan, Wang Kun, Qiang Jun, Hu Dianlin, Zhang Yikun
College of Computer and Information, Anhui Polytechnic University, Wuhu, China; Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing, China.
College of Computer and Information, Anhui Polytechnic University, Wuhu, China; Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing, China.
Comput Methods Programs Biomed. 2024 Feb;244:108010. doi: 10.1016/j.cmpb.2024.108010. Epub 2024 Jan 6.
Purpose Numerous techniques based on deep learning have been utilized in sparse view computed tomography (CT) imaging. Nevertheless, the majority of techniques are instinctively constructed utilizing state-of-the-art opaque convolutional neural networks (CNNs) and lack interpretability. Moreover, CNNs tend to focus on local receptive fields and neglect nonlocal self-similarity prior information. Obtaining diagnostically valuable images from sparsely sampled projections is a challenging and ill-posed task. Method To address this issue, we propose a unique and understandable model named DCDL-GS for sparse view CT imaging. This model relies on a network comprised of convolutional dictionary learning and a nonlocal group sparse prior. To enhance the quality of image reconstruction, we utilize a neural network in conjunction with a statistical iterative reconstruction framework and perform a set number of iterations. Inspired by group sparsity priors, we adopt a novel group thresholding operation to improve the feature representation and constraint ability and obtain a theoretical interpretation. Furthermore, our DCDL-GS model incorporates filtered backprojection (FBP) reconstruction, fast sliding window nonlocal self-similarity operations, and a lightweight and interpretable convolutional dictionary learning network to enhance the applicability of the model. Results The efficiency of our proposed DCDL-GS model in preserving edges and recovering features is demonstrated by the visual results obtained on the LDCT-P and UIH datasets. Compared to the results of the most advanced techniques, the quantitative results are enhanced, with increases of 0.6-0.8 dB for the peak signal-to-noise ratio (PSNR), 0.005-0.01 for the structural similarity index measure (SSIM), and 1-1.3 for the regulated Fréchet inception distance (rFID) on the test dataset. The quantitative results also show the effectiveness of our proposed deep convolution iterative reconstruction module and nonlocal group sparse prior. Conclusion In this paper, we create a consolidated and enhanced mathematical model by integrating projection data and prior knowledge of images into a deep iterative model. The model is more practical and interpretable than existing approaches. The results from the experiment show that the proposed model performs well in comparison to the others.
基于深度学习的众多技术已应用于稀疏视图计算机断层扫描（CT）成像。然而，大多数技术是使用先进的不透明卷积神经网络（CNN）本能构建的，缺乏可解释性。此外，CNN倾向于关注局部感受野，而忽略非局部自相似性先验信息。从稀疏采样投影中获取具有诊断价值的图像是一项具有挑战性的不适定任务。方法：为解决此问题，我们提出了一种用于稀疏视图CT成像的独特且可理解的模型DCDL-GS。该模型依赖于一个由卷积字典学习和非局部组稀疏先验组成的网络。为提高图像重建质量，我们将神经网络与统计迭代重建框架结合使用，并执行一定次数的迭代。受组稀疏先验启发，我们采用一种新颖的组阈值操作来提高特征表示和约束能力，并获得理论解释。此外，我们的DCDL-GS模型结合了滤波反投影（FBP）重建、快速滑动窗口非局部自相似性操作以及一个轻量级且可解释的卷积字典学习网络，以提高模型的适用性。结果：通过在LDCT-P和UIH数据集上获得的视觉结果，证明了我们提出的DCDL-GS模型在保留边缘和恢复特征方面的效率。与最先进技术的结果相比，定量结果得到了增强，在测试数据集上，峰值信噪比（PSNR）提高了0.6 - 0.8 dB，结构相似性指数测量（SSIM）提高了0.005 - 0.01，调节后的弗雷歇初始距离（rFID）提高了1 - 1.3。定量结果还显示了我们提出的深度卷积迭代重建模块和非局部组稀疏先验的有效性。结论：在本文中，我们通过将投影数据和图像的先验知识集成到一个深度迭代模型中，创建了一个综合且增强的数学模型。该模型比现有方法更实用且更具可解释性。实验结果表明，所提出的模型与其他模型相比表现良好。
