Hierarchical decomposed dual-domain deep learning for sparse-view CT reconstruction

2024-03-08

Physics in Medicine and Biology

AI Summary

To address severe streaking artifacts and the trade-off between radiation dose and image quality in sparse-view CT reconstruction, this paper proposes a measurement-hierarchical decomposition-driven dual-domain deep learning framework that jointly models the image and projection domains. Innovatively integrating deep convolutional frames (DCFs) with hierarchical measurement decomposition, we first reveal that projection-domain network performance gains stem from low-rank structure and butterfly-shaped Fourier-domain support characteristics—enabling the design of a dual-domain joint optimization scheme with explicit Fourier-domain constraints. The method preserves physical interpretability while significantly suppressing streaking artifacts. Quantitative evaluations demonstrate superior reconstruction accuracy under sparse-view conditions compared to filtered back-projection and state-of-the-art single-domain deep learning methods. Source code is publicly available.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Learning & Optimization for CV
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Objective. X-ray computed tomography employing sparse projection views has emerged as a contemporary technique to mitigate radiation dose. However, due to the inadequate number of projection views, an analytic reconstruction method utilizing filtered backprojection results in severe streaking artifacts. Recently, deep learning (DL) strategies employing image-domain networks have demonstrated remarkable performance in eliminating the streaking artifact caused by analytic reconstruction methods with sparse projection views. Nevertheless, it is difficult to clarify the theoretical justification for applying DL to sparse view computed tomography (CT) reconstruction, and it has been understood as restoration by removing image artifacts, not reconstruction. Approach. By leveraging the theory of deep convolutional framelets (DCF) and the hierarchical decomposition of measurement, this research reveals the constraints of conventional image and projection-domain DL methodologies, subsequently, the research proposes a novel dual-domain DL framework utilizing hierarchical decomposed measurements. Specifically, the research elucidates how the performance of the projection-domain network can be enhanced through a low-rank property of DCF and a bowtie support of hierarchical decomposed measurement in the Fourier domain. Main results. This study demonstrated performance improvement of the proposed framework based on the low-rank property, resulting in superior reconstruction performance compared to conventional analytic and DL methods. Significance. By providing a theoretically justified DL approach for sparse-view CT reconstruction, this study not only offers a superior alternative to existing methods but also opens new avenues for research in medical imaging. It highlights the potential of dual-domain DL frameworks to achieve high-quality reconstructions with lower radiation doses, thereby advancing the field towards safer and more efficient diagnostic techniques. The code is available at https://github.com/hanyoseob/HDD-DL-for-SVCT.

Problem

• Limited-Angle CT
• Artifact Reduction
• Radiation Dose Reduction

Innovation

• Hierarchical Decomposition
• Low-Rank Property
• Deep Learning Framework