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Ultra-Low-Dose CTPA Using Sparse Sampling CT Combined with the U-Net for Deep Learning-Based Artifact Reduction: An

Andreas Philipp Sauter1, Johannes Thalhammer2,3,4,5, Felix Meurer1

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This study shows U-Net artifact reduction significantly improves image quality and diagnostic performance in low-dose CT pulmonary angiography. This supports substantial dose reduction for CT pulmonary angiography (CTPA) scans.

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Computed tomography pulmonary angiogramConvolutional neural networkDose reductionPulmonary embolismSparse sampling

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Computed Tomography

Background:

  • Dose reduction in CT pulmonary angiography (CTPA) is crucial but can compromise image quality due to artifacts.
  • Sparse-sampling CT (SpSCT) offers dose reduction but introduces significant artifacts.
  • Deep learning, specifically U-Net architectures, shows promise for image reconstruction and artifact reduction.

Purpose of the Study:

  • To evaluate the efficacy of a U-Net-based approach for artifact reduction in dose-reduced SpSCT.
  • To assess the impact of U-Net post-processing on image quality and diagnostic performance in CTPA.
  • To compare U-Net performance against traditional filtered back projection (FBP) and evaluate automated pulmonary embolism (PE) detection.

Main Methods:

  • A dual-frame U-Net model was trained on 69 patient datasets and validated on 16.
  • SpSCT data were generated from 89 patients with varying views (16 to 512).
  • Image quality was assessed using structural similarity index (SSIM) and a reader study; diagnostic performance was evaluated using Sørensen-Dice coefficient and automated PE detection.

Main Results:

  • U-Net significantly improved image quality across all view subsets (e.g., SSIM increased from 0.378 to 0.892 at 64 views).
  • Reader studies confirmed enhanced image quality, increased diagnostic confidence, and reduced artifacts with U-Net post-processing (P < 0.05).
  • Diagnostic performance improved significantly for 64- and 32-view images (Sørensen-Dice: 0.44 vs. 0.23 and 0.09 vs. 0.00, respectively).

Conclusions:

  • U-Net-based artifact reduction effectively enhances image quality and diagnostic performance in low-dose SpSCT.
  • The findings support the potential for substantial radiation dose reduction in CT pulmonary angiography.
  • U-Net post-processing offers a viable solution for improving the diagnostic utility of sparse-sampling CT.