# Multi-layer Clustering-based Residual Sparsifying Transform for Low-dose CT Image Reconstruction

arXiv: https://arxiv.org/abs/2203.11565 · HTML: https://mlsanity.com/paper/2203.11565/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Convolutional Neural Network for Inverse Problems in Imaging](https://mlsanity.com/paper/1611.03679/) (2016)
- [Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)](https://mlsanity.com/paper/1702.00288/) (2017)
- [PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction](https://mlsanity.com/paper/1703.09165/) (2017)
- [ℓ0 Sparsifying Transform Learning with Efficient Optimal Updates and Convergence Guarantees](https://mlsanity.com/paper/1501.02859/) (2015)
- [Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond](https://mlsanity.com/paper/2103.14528/) (2021)
- [Efficient Blind Compressed Sensing Using Sparsifying Transforms with Convergence Guarantees and Application to MRI](https://mlsanity.com/paper/1501.02923/) (2015)
- [Learning Multi-Layer Transform Models](https://mlsanity.com/paper/1810.08323/) (2018)
- [Deep Micro-Dictionary Learning and Coding Network](https://mlsanity.com/paper/1809.04185/) (2018)
- [Majorization Minimization Technique for Optimally Solving Deep Dictionary Learning](https://mlsanity.com/paper/1912.10801/) (2019)

## Built on by (most cited first, 1 of 1)

- [Enhancing Low-dose CT Image Reconstruction by Integrating Supervised and Unsupervised Learning](https://mlsanity.com/paper/2311.12071/) (2023)

## Abstract (arXiv)

The recently proposed sparsifying transform models incur low computational cost and have been applied to medical imaging. Meanwhile, deep models with nested network structure reveal great potential for learning features in different layers. In this study, we propose a network-structured sparsifying transform learning approach for X-ray computed tomography (CT), which we refer to as multi-layer clustering-based residual sparsifying transform (MCST) learning. The proposed MCST scheme learns multiple different unitary transforms in each layer by dividing each layer's input into several classes. We apply the MCST model to low-dose CT (LDCT) reconstruction by deploying the learned MCST model into the regularizer in penalized weighted least squares (PWLS) reconstruction. We conducted LDCT reconstruction experiments on XCAT phantom data and Mayo Clinic data and trained the MCST model with 2 (or 3) layers and with 5 clusters in each layer. The learned transforms in the same layer showed rich features while additional information is extracted from representation residuals. Our simulation results demonstrate that PWLS-MCST achieves better image reconstruction quality than the conventional FBP method and PWLS with edge-preserving (EP) regularizer. It also outperformed recent advanced methods like PWLS with a learned multi-layer residual sparsifying transform prior (MARS) and PWLS with a union of learned transforms (ULTRA), especially for displaying clear edges and preserving subtle details.