State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan, China.
School of Information and Communication Engineering, North University of China, Taiyuan, China.
J Xray Sci Technol. 2023;31(3):593-609. doi: 10.3233/XST-221358.
Low-Dose computed tomography (LDCT) reduces radiation damage to patients, however, the reconstructed images contain severe noise, which affects doctors' diagnosis of the disease. The convolutional dictionary learning has the advantage of the shift-invariant property. The deep convolutional dictionary learning algorithm (DCDicL) combines deep learning and convolutional dictionary learning, which has great suppression effects on Gaussian noise. However, applying DCDicL to LDCT images cannot get satisfactory results.
To address this challenge, this study proposes and tests an improved deep convolutional dictionary learning algorithm for LDCT image processing and denoising.
First, we use a modified DCDicL algorithm to improve the input network and make it do not need to input noise intensity parameter. Second, we use DenseNet121 to replace the shallow convolutional network to learn the prior on the convolutional dictionary, which can obtain more accurate convolutional dictionary. Last, in the loss function, we add MSSIM to enhance the detail retention ability of the model.
The experimental results on the Mayo dataset show that the proposed model obtained an average value of 35.2975 dB in PSNR, which is 0.2954 -1.0573 dB higher than the mainstream LDCT algorithm, indicating the excellent denoising performance.
The study demonstrates that the proposed new algorithm can effectively improve the quality of LDCT images acquired in the clinical practice.
低剂量计算机断层扫描（LDCT）降低了患者的辐射损伤，但重建图像存在严重噪声，影响医生对疾病的诊断。卷积字典学习具有平移不变性的优势。深度卷积字典学习算法（DCDicL）结合了深度学习和卷积字典学习，对高斯噪声具有很好的抑制效果。然而，将 DCDicL 应用于 LDCT 图像并不能得到满意的结果。
针对这一挑战，本研究提出并测试了一种用于 LDCT 图像处理和去噪的改进的深度卷积字典学习算法。
首先，我们使用改进的 DCDicL 算法来改进输入网络，使其不需要输入噪声强度参数。其次，我们使用 DenseNet121 代替浅层卷积网络来学习卷积字典的先验知识，从而可以获得更准确的卷积字典。最后，在损失函数中，我们添加了 MSSIM，以增强模型的细节保留能力。
在 Mayo 数据集上的实验结果表明，所提出的模型在 PSNR 方面的平均值为 35.2975dB，比主流的 LDCT 算法高 0.2954-1.0573dB，表明去噪性能优异。
该研究表明，所提出的新算法可以有效提高临床实践中获取的 LDCT 图像的质量。
