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CT artifact correction for sparse and truncated projection data using generative adversarial networks.

Alexander R Podgorsak1,2,3, Mohammad Mahdi Shiraz Bhurwani1,3, Ciprian N Ionita1,2,3

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Deep convolutional generative adversarial networks (DCGANs) effectively correct computed tomography (CT) images reconstructed from sparse or truncated data. This machine learning approach preserves image quality, enabling reduced radiation dose and improved diagnostic accuracy.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Image Reconstruction

Background:

  • Computed tomography (CT) image reconstruction often uses truncated or sparse projection data to minimize radiation dose, iodine volume, and motion artifacts.
  • Developing advanced reconstruction techniques is crucial for enhancing CT imaging efficiency and patient safety.

Purpose of the Study:

  • To investigate the efficacy of deep convolutional generative adversarial networks (DCGANs) for correcting CT images reconstructed from incomplete projection data.
  • To evaluate the impact of DCGAN-based correction on standard imaging metrics and image quality.

Main Methods:

  • Utilized 10,000 head CT scans; simulated truncated and sparse sinograms.
  • Applied DCGANs for correction in either the sinogram or reconstruction domain.
  • Assessed performance using MAE, SSIM, PSNR, MTF, NPS, and HU linearity on phantom and patient data.

Main Results:

  • DCGANs significantly improved image quality, with better agreement for sparse data corrected in the sinogram domain and truncated data in the reconstruction domain.
  • Quantitative metrics (MAE, SSIM, PSNR) showed marked improvement.
  • Modulation transfer function (MTF) cutoff frequencies increased substantially for both sparse and truncated corrected reconstructions.

Conclusions:

  • DCGANs successfully correct CT images reconstructed from simulated sparse and truncated projection data.
  • The proposed method preserves the imaging quality comparable to fully sampled data.