Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT

Abstract

X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose. However, due to the insufficient projection views, an analytic reconstruction approach using the filtered back projection (FBP) produces severe streaking artifacts. Recently, deep learning approaches using large receptive field neural networks such as U-Net have demonstrated impressive performance for sparse- view CT reconstruction. However, theoretical justification is still lacking. Inspired by the recent theory of deep convolutional framelets, the main goal of this paper is, therefore, to reveal the limitation of U-Net and propose new multi-resolution deep learning schemes. In particular, we show that the alternative U- Net variants such as dual frame and the tight frame U-Nets satisfy the so-called frame condition which make them better for effective recovery of high frequency edges in sparse view- CT. Using extensive experiments with real patient data set, we demonstrate that the new network architectures provide better reconstruction performance.

arXiv commentThis will appear in IEEE Transaction on Medical Imaging, a special issue of Machine Learning for Image Reconstruction

References in corpus (10)

• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• Deep Convolutional Neural Network for Inverse Problems in Imaging
• Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
• Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
• Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis
• Accurate Image Super-Resolution Using Very Deep Convolutional Networks
• CT Image Denoising with Perceptive Deep Neural Networks
• Wavelet Domain Residual Network (WavResNet) for Low-Dose X-ray CT Reconstruction
• LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT
• Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network

Cited by in corpus (5)

• Adapting Mask-RCNN for Automatic Nucleus Segmentation
• Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRI
• Multi-level Wavelet-CNN for Image Restoration
• Comparison of projection domain, image domain, and comprehensive deep learning for sparse-view X-ray CT image reconstruction
• Robust X-ray Sparse-view Phase Tomography via Hierarchical Synthesis Convolutional Neural Networks