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Related Experiment Video

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Contrast Enhanced Vessel Imaging using MicroCT
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Virtual monoenergetic micro-CT imaging in mice with artificial intelligence.

Brent van der Heyden1, Stijn Roden1, Rüveyda Dok1

  • 1Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Leuven, Belgium.

Scientific Reports
|February 12, 2022
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Summary

This study introduces vMonoCT, an AI tool to reduce imaging artifacts in micro cone-beam computed tomography (µCBCT) for preclinical research. vMonoCT effectively minimizes beam hardening artifacts, improving image quality for rodent studies.

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Area of Science:

  • Biomedical Imaging
  • Preclinical Research
  • Artificial Intelligence

Background:

  • Micro cone-beam computed tomography (µCBCT) is vital for preclinical rodent research, including precision irradiation and longitudinal outcome assessment.
  • Low-energy X-ray beams (60 kVp) in µCBCT cause beam hardening artifacts, particularly in 'pancake' imaging geometries.
  • These artifacts are challenging to correct, impacting quantitative preclinical applications.

Purpose of the Study:

  • To develop and evaluate an AI-based method (vMonoCT) for mitigating beam hardening artifacts in µCBCT.
  • To predict virtual monoenergetic X-ray projections from polyenergetic ones using a U-Net architecture.
  • To enhance image quality for improved reproducibility in preclinical research.

Main Methods:

  • A seven-layer U-Net based network (vMonoCT) was employed to predict virtual monoenergetic projections.
  • A Monte Carlo simulation generated a training dataset of 1890 projection pairs using digital mouse phantoms.
  • The model was trained on 1512 pairs and tested on 378 pairs, with additional evaluation on retrospective animal data.

Main Results:

  • The vMonoCT model achieved a percentage error of 1.7 ± 0.4% on the test dataset.
  • Reconstruction of vMonoCT-corrected projections significantly minimized beam hardening artifacts.
  • Anatomically incorrect gradient errors in the cranium were corrected by up to 15%.

Conclusions:

  • Artificial intelligence holds significant potential for enhancing µCBCT image quality in biomedical applications.
  • vMonoCT can improve the reproducibility of quantitative preclinical applications like precision irradiations.
  • The developed AI tool is expected to aid in the evaluation of longitudinal imaging data in extensive preclinical studies.