mlsanityData to 7 Oct 2026

A Lightweight Dual-Domain Attention Framework for Sparse-View CT Reconstruction

arXiv 2202.09609 ↗First posted 19 Feb 2022eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Computed Tomography (CT) plays an essential role in clinical diagnosis. Due to the adverse effects of radiation on patients, the radiation dose is expected to be reduced as low as possible. Sparse sampling is an effective way, but it will lead to severe artifacts on the reconstructed CT image, thus sparse-view CT image reconstruction has been a prevailing and challenging research area. With the popularity of mobile devices, the requirements for lightweight and real-time networks are increasing rapidly. In this paper, we design a novel lightweight network called CAGAN, and propose a dual-domain reconstruction pipeline for parallel beam sparse-view CT. CAGAN is an adversarial auto-encoder, combining the Coordinate Attention unit, which preserves the spatial information of features. Also, the application of Shuffle Blocks reduces the parameters by a quarter without sacrificing its performance. In the Radon domain, the CAGAN learns the mapping between the interpolated data and fringe-free projection data. After the restored Radon data is reconstructed to an image, the image is sent into the second CAGAN trained for recovering the details, so that a high-quality image is obtained. Experiments indicate that the CAGAN strikes an excellent balance between model complexity and performance, and our pipeline outperforms the DD-Net and the DuDoNet.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 52 references that are in the map. First posted on arXiv 19 Feb 2022.

Builds on

20 of 52 references.

References · 20

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  3. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  4. 2017Squeeze-and-Excitation Networkscited by 4,763
  5. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  6. 2019Searching for MobileNetV3cited by 1,984
  7. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  8. 2018Group Normalizationcited by 1,514
  9. 2015Spatial Transformer Networkscited by 1,423
  10. 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,194
  11. 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,132
  12. 2018Image Inpainting for Irregular Holes Using Partial Convolutionscited by 505
8 more
  1. 2021Coordinate Attention for Efficient Mobile Network Designcited by 239
  2. 2019DuDoNet: Dual Domain Network for CT Metal Artifact Reductioncited by 68
  3. 2018Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removalcited by 56
  4. 2018Deep-neural-network based sinogram synthesis for sparse-view CT image reconstructioncited by 47
  5. 2017Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completioncited by 31
  6. 2016Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Networkcited by 9
  7. 2019Sinogram interpolation for sparse-view micro-CT with deep learning neural networkcited by 9
  8. 2018JSR-Net: A Deep Network for Joint Spatial-Radon Domain CT Reconstruction from incomplete datacited by 6

Built on by

Not cited yet.