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Dual-Domain Reconstruction Network Incorporating Multi-Level Wavelet Transform and Recurrent Convolution for Sparse

Juncheng Lin1,2,3, Jialin Li1,2,3, Jiazhen Dou1,2,3

  • 1Institute of Advanced Photonics Technology, School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China.

Tomography (Ann Arbor, Mich.)
|January 22, 2024
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Summary

This study introduces a novel dual-domain network for sparse view computed tomography (SVCT) reconstruction. The method effectively reduces artifacts and enhances image details, improving diagnostic accuracy in low-dose CT scans.

Keywords:
CT reconstructionconvolutional long and short-term memory (Conv-LSTM)dual-domain networkmulti-level waveletself-attentionsparse view

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Image Reconstruction

Background:

  • Sparse view computed tomography (SVCT) reduces radiation dose and scan time.
  • Insufficient projection data in SVCT leads to artifacts and blurring, compromising diagnostic accuracy.

Purpose of the Study:

  • To develop an advanced deep learning method for high-quality image reconstruction in SVCT.
  • To address the challenges of artifacts and detail loss in low-dose CT imaging.

Main Methods:

  • A dual-domain reconstruction network (sinogram and image domains) utilizing multi-level wavelet transform.
  • Recurrent convolution units (RCU) with Conv-LSTM for modeling long-range dependencies.
  • Self-attention-based fusion block (MFNF) and Laplacian of Gaussian (LoG) edge loss for detail recovery.

Main Results:

  • Significant reduction in streak artifacts and blurring compared to existing methods.
  • Enhanced recovery of intricate structural details in reconstructed CT images.
  • Superior performance and robustness across various sparse views and noise levels.

Conclusions:

  • The proposed dual-domain network effectively improves SVCT image quality.
  • This method offers a promising solution for accurate and low-dose CT detection.
  • Outperforms current state-of-the-art reconstruction techniques in qualitative and quantitative assessments.