Two-layer clustering-based sparsifying transform learning for low-dose CT reconstruction

• URL: http://arxiv.org/abs/2011.00428v1
• Date: Sun, 1 Nov 2020 05:15:37 GMT
• Title: Two-layer clustering-based sparsifying transform learning for low-dose CT reconstruction
• Authors: Xikai Yang, Yong Long, Saiprasad Ravishankar
• Abstract summary: We propose an approach to learn a rich two-layer clustering-based sparsifying transform model (MCST2) Experimental results show the superior performance of the proposed PWLS-MCST2 approach compared to other related recent schemes.
• Score: 12.37556184089774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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