Convolutional Sparse Coding for Compressed Sensing CT Reconstruction

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.

Accepted by IEEE TMI

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Cited by in corpus (8)

• Low-Dose CT with Deep Learning Regularization via Proximal Forward Backward Splitting
• MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising
• Systematic Review on Learning-based Spectral CT
• Efficient ADMM-based Algorithms for Convolutional Sparse Coding
• Deep learning based dictionary learning and tomographic image reconstruction
• Three-dimensional visualization of X-ray micro-CT with large-scale datasets: Efficiency and accuracy for real-time interaction
• Deep Inertia $L_p$ Half-Quadratic Splitting Unrolling Network for Sparse View CT Reconstruction
• Super time-resolved tomography