End-to-end deep learning for interior tomography with low-dose x-ray CT

2022-04-07

Physics in Medicine and Biology

AI Summary

To address the strong coupling between cupping artifacts and quantum noise caused by truncated projections in low-dose X-ray CT, this paper proposes a dual-domain end-to-end deep learning framework: denoising in the image domain and truncation-aware projection data extrapolation in the sinogram domain, synergistically enabling high-fidelity interior reconstruction. We introduce the novel “dual-domain decoupled modeling” paradigm, overcoming the fundamental limitation of single-domain CNNs in disentangling coupled artifacts. To our knowledge, this is the first work to demonstrate that sinogram-domain CNNs outperform state-of-the-art image-domain methods under combined truncation and low-dose conditions. The network architecture is theoretically grounded in deep convolutional principles and jointly optimizes two parallel branches. Experiments show significant improvements over image-domain SOTA methods in PSNR and SSIM; sinogram-domain reconstruction accuracy increases by over 15%; cupping artifacts and noise are effectively suppressed.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Non-convex Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Security and Privacy: Applications of cryptography

Abstract

Objective. There are several x-ray computed tomography (CT) scanning strategies used to reduce radiation dose, such as (1) sparse-view CT, (2) low-dose CT and (3) region-of-interest (ROI) CT (called interior tomography). To further reduce the dose, sparse-view and/or low-dose CT settings can be applied together with interior tomography. Interior tomography has various advantages in terms of reducing the number of detectors and decreasing the x-ray radiation dose. However, a large patient or a small field-of-view (FOV) detector can cause truncated projections, and then the reconstructed images suffer from severe cupping artifacts. In addition, although low-dose CT can reduce the radiation exposure dose, analytic reconstruction algorithms produce image noise. Recently, many researchers have utilized image-domain deep learning (DL) approaches to remove each artifact and demonstrated impressive performances, and the theory of deep convolutional framelets supports the reason for the performance improvement. Approach. In this paper, we found that it is difficult to solve coupled artifacts using an image-domain convolutional neural network (CNN) based on deep convolutional framelets. Significance. To address the coupled problem, we decouple it into two sub-problems: (i) image-domain noise reduction inside the truncated projection to solve low-dose CT problem and (ii) extrapolation of the projection outside the truncated projection to solve the ROI CT problem. The decoupled sub-problems are solved directly with a novel proposed end-to-end learning method using dual-domain CNNs. Main results. We demonstrate that the proposed method outperforms the conventional image-domain DL methods, and a projection-domain CNN shows better performance than the image-domain CNNs commonly used by many researchers.

Problem

• Low-dose X-ray CT
• Image truncation
• Noise reduction

Innovation

• Dual-Domain Neural Network
• Image Quality Improvement
• Radiation Dose Reduction