Deep Encoder-decoder Adversarial Reconstruction (DEAR) Network for 3D CT from Few-view Data

Abstract

X-ray computed tomography (CT) is widely used in clinical practice. The involved ionizing X-ray radiation, however, could increase cancer risk. Hence, the reduction of the radiation dose has been an important topic in recent years. Few-view CT image reconstruction is one of the main ways to minimize radiation dose and potentially allow a stationary CT architecture. In this paper, we propose a deep encoder-decoder adversarial reconstruction (DEAR) network for 3D CT image reconstruction from few-view data. Since the artifacts caused by few-view reconstruction appear in 3D instead of 2D geometry, a 3D deep network has a great potential for improving the image quality in a data-driven fashion. More specifically, our proposed DEAR-3D network aims at reconstructing 3D volume directly from clinical 3D spiral cone-beam image data. DEAR is validated on a publicly available abdominal CT dataset prepared and authorized by Mayo Clinic. Compared with other 2D deep-learning methods, the proposed DEAR-3D network can utilize 3D information to produce promising reconstruction results.

References in corpus (7)

• Deep Convolutional Neural Network for Inverse Problems in Imaging
• Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
• Improved Training of Wasserstein GANs
• Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?
• 3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network
• Deep-neural-network based sinogram synthesis for sparse-view CT image reconstruction
• TomoGAN: Low-Dose Synchrotron X-Ray Tomography with Generative Adversarial Networks

Cited by in corpus (5)

• Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction
• Systematic Review on Learning-based Spectral CT
• Artifact Reduction in 3D and 4D Cone-beam Computed Tomography Images with Deep Learning -- A Review
• Segmentation-free PVC for Cardiac SPECT using a Densely-connected Multi-dimensional Dynamic Network
• A Novel Implicit Neural Representation for Volume Data