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Computed Tomography01:10

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Hybrid µCT-FMT imaging and image analysis
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Sparse-View CT Reconstruction Based on a Hybrid Domain Model with Multi-Level Wavelet Transform.

Jielin Bai1, Yitong Liu1, Hongwen Yang1

  • 1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Haidian, Beijing 100876, China.

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Summary

This study introduces a hybrid domain method using multi-level wavelet transform networks for sparse-view computed tomography (CT) reconstruction. The novel approach effectively reduces streaking artifacts, significantly improving image quality and diagnostic accuracy in low-projection scenarios.

Keywords:
CT reconstructiondirectional and global artifacthybrid domain methodmulti-level wavelet transformsparsely sampled projections

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Area of Science:

  • Medical Imaging
  • Image Reconstruction
  • Signal Processing

Background:

  • Sparsely sampled projection data in CT reconstruction leads to streaking artifacts, degrading image quality and impacting medical diagnoses.
  • Wavelet transform offers effective decomposition of directional image components, aiding detection of artifact features and edge details.

Purpose of the Study:

  • To propose a hybrid domain method for sparse-view CT reconstruction to address image quality degradation caused by artifacts.
  • To enhance image details and restore projection consistency by combining wavelet, spatial, and radon domains.

Main Methods:

  • A hybrid domain reconstruction model integrating wavelet, spatial, and radon domains was developed.
  • A multi-level wavelet transform network (MWCNN) was employed to handle global artifact distribution and large receptive field requirements.
  • Wavelet transform was used for feature map reduction in the encoder, and inverse wavelet transform for detail recovery in the decoder.

Main Results:

  • The proposed method achieved a PSNR of 41.049 dB and SSIM of 0.958 with 120 projections.
  • Numerical analysis and reconstructed images demonstrated the superiority of the hybrid domain method over single-domain approaches.
  • The multi-level wavelet transform model proved more effective for CT reconstruction than single-level wavelet transform.

Conclusions:

  • The hybrid domain method effectively reconstructs sparse-view CT data, significantly reducing artifacts and improving image quality.
  • The multi-level wavelet transform network architecture is well-suited for sparse-view CT reconstruction, outperforming single-level approaches.