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X-ray image decomposition for improved magnetic navigation.

Wenyao Xia1, Shuwei Xing2, Uditha Jarayathne3

  • 1Robarts Research Institute, Western University, 100 Perth St., London, ON, N6A 5B7, Canada. wxia43@uwo.ca.

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|May 24, 2023
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Summary
This summary is machine-generated.

This study introduces a learning-based method to remove field generator (FG) artifacts from X-ray images, improving visualization for X-ray-guided interventions. The approach significantly enhances image clarity for better magnetic navigation.

Keywords:
Image decompositionMagnetic trackingX-ray simulationX-ray-guided interventions

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Interventional Radiology

Background:

  • Field generators (FGs) used in magnetic tracking create artifacts in X-ray images.
  • Radio-lucent FG components reduce but do not eliminate these imaging artifacts.
  • Visible FG traces can hinder precise visualization during X-ray-guided interventions.

Purpose of the Study:

  • To develop a learning-based approach to further reduce field generator (FG) component traces in X-ray images.
  • To improve visualization and image guidance for X-ray-guided interventions using magnetic tracking.
  • To enhance X-ray images by removing FG-induced artifacts.

Main Methods:

  • An adversarial decomposition network was trained to separate residual FG components from X-ray images.
  • A novel data synthesis method generated 20,000 synthetic images by combining patient chest X-rays and FG X-rays.
  • Ground truth images (without FG) were generated to train the network effectively.

Main Results:

  • The enhanced X-ray images achieved an average local Peak Signal-to-Noise Ratio (PSNR) of 35.04 and a Structural Similarity Index Measure (SSIM) of 0.97.
  • Unenhanced X-ray images averaged a local PSNR of 31.16 and SSIM of 0.96.
  • Experiments were conducted on 30 real images of a torso phantom.

Conclusions:

  • A generative adversarial network-based X-ray image decomposition method was proposed to remove FG-induced artifacts.
  • The method effectively enhances X-ray images for improved magnetic navigation.
  • Experiments on synthetic and real phantom data confirmed the method's efficacy.