# Convolutional Sparse Coding for Compressed Sensing CT Reconstruction

## TL;DR

This paper introduces a convolutional sparse coding approach for sparse-view CT reconstruction that processes entire images directly, leading to improved detail preservation and artifact reduction compared to patch-based methods.

## Contribution

The paper proposes a novel CSC-based method for CT reconstruction that avoids patch division, maintaining image consistency and detail, and demonstrates superior performance over existing methods.

## Key findings

- Outperforms state-of-the-art methods in simulated data
- Effective on real CT data with improved image quality
- Reduces artifacts and preserves details in reconstructed images

## Abstract

Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems. However, traditional DL-based computed tomography (CT) reconstruction methods are patch-based and ignore the consistency of pixels in overlapped patches. In addition, the features learned by these methods always contain shifted versions of the same features. In recent years, convolutional sparse coding (CSC) has been developed to address these problems. In this paper, inspired by several successful applications of CSC in the field of signal processing, we explore the potential of CSC in sparse-view CT reconstruction. By directly working on the whole image, without the necessity of dividing the image into overlapped patches in DL-based methods, the proposed methods can maintain more details and avoid artifacts caused by patch aggregation. With predetermined filters, an alternating scheme is developed to optimize the objective function. Extensive experiments with simulated and real CT data were performed to validate the effectiveness of the proposed methods. Qualitative and quantitative results demonstrate that the proposed methods achieve better performance than several existing state-of-the-art methods.