论文标题
低剂量CT重建的基于两层聚类的稀疏转换学习
Two-layer clustering-based sparsifying transform learning for low-dose CT reconstruction
论文作者
论文摘要
在临床环境中，通过低剂量计算机断层扫描（LDCT）测量实现高质量的重建非常重要。基于模型的图像重建方法已被证明可以有效地去除LDCT中的伪影。在这项工作中，我们提出了一种学习丰富的基于两层聚类的稀疏转换模型（MCST2）的方法，其中图像贴片及其后续特征映射（滤镜残差）被聚类为每组不同学识渊博的稀疏过滤器的组。我们调查了纳入LDCT重建的惩罚加权最小二乘方法（PWLS）方法，该方法纳入了学习的MCST2先验。实验结果表明，与其他相关方案相比，所提出的PWLS-MCST2方法的出色表现。
Achieving high-quality reconstructions from low-dose computed tomography (LDCT) measurements is of much importance in clinical settings. Model-based image reconstruction methods have been proven to be effective in removing artifacts in LDCT. In this work, we propose an approach to learn a rich two-layer clustering-based sparsifying transform model (MCST2), where image patches and their subsequent feature maps (filter residuals) are clustered into groups with different learned sparsifying filters per group. We investigate a penalized weighted least squares (PWLS) approach for LDCT reconstruction incorporating learned MCST2 priors. Experimental results show the superior performance of the proposed PWLS-MCST2 approach compared to other related recent schemes.
