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Deep learning for x-ray scatter correction in dedicated breast CT.

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  • 1Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.

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|December 24, 2022
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A deep learning model accurately corrects x-ray scatter in breast computed tomography (bCT) imaging, enhancing image contrast and reducing artifacts. This advancement allows for improved visual interpretation and quantitative accuracy in clinical practice.

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Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence

Background:

  • Accurate x-ray scatter correction is vital for improving image interpretation and quantitative accuracy in breast computed tomography (bCT).
  • Scatter degrades image quality, hindering precise analysis in bCT imaging.

Purpose of the Study:

  • To develop and validate a deep learning (DL) model for correcting x-ray scatter in bCT projection images.
  • The model aims to enhance image quality for better diagnostic and quantitative assessments.

Main Methods:

  • A U-Net based DL model was trained using Monte Carlo (MC) simulated bCT projection images from segmented patient phantoms.
  • The model utilized simulated images, thickness maps, and breast location for scatter estimation.
  • Validation was performed on internal MC-simulated data and an external dataset from a different bCT system.

Main Results:

  • The DL model demonstrated low mean relative difference (MRD) and mean absolute error (MAE) on internal (0.04%, 2.94%) and external (-0.64%, 2.84%) validation sets.
  • Reconstructed images showed high structural similarity (SSIM 0.99) and low MAE (0.11%) compared to MC-simulations.
  • Patient images exhibited improved contrast (+25%) and reduced cupping artifacts, with a non-significant increase in contrast-to-noise ratio (CNR).

Conclusions:

  • The developed DL model effectively estimates scatter in bCT projection images.
  • The model enhances contrast and corrects artifacts in reconstructed images, facilitating quantitative analysis.
  • The rapid correction time (0.2s/projection) supports its integration into daily clinical workflows.