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Hyeongmin Jin1,2, Changyong Heo3, Jong Hyo Kim1,4,3,5

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This study introduces a deep learning method to normalize CT reconstruction kernel effects for accurate emphysema quantification. The approach reduces variability in lung density measurements, improving emphysema surveillance in lung cancer screening.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pulmonology

Background:

  • Lung densitometry in CT scans is crucial for emphysema quantification.
  • Reconstruction kernel choice significantly impacts emphysema measurement accuracy.
  • Variability in CT acquisition protocols hinders consistent lung density analysis.

Purpose of the Study:

  • To develop and validate a deep learning architecture for normalizing CT reconstruction kernel effects.
  • To improve the accuracy of emphysema quantification in low-dose CT scans.
  • To enable reliable emphysema surveillance across different CT scanners and protocols.

Main Methods:

  • A two-step deep learning model was designed to convert sharp kernel CT images to standard kernel equivalents.
  • Truncation artifact correction was achieved using histogram extrapolation and a deep learning model.
  • Frequency domain zero-padding normalized field of view effects without image smoothing.

Main Results:

  • Kernel normalization significantly reduced differences in lung density metrics (RA950, perc15) between standard and sharp kernels.
  • Mean difference in RA950 reduced from 10.75% to -0.07%.
  • Mean difference in perc15 decreased from -31.03 HU to -0.30 HU.

Conclusions:

  • Deep learning effectively normalizes CT kernel effects, reducing variability in lung density measurements.
  • This method enhances the accuracy of emphysema quantification.
  • The model supports reliable emphysema surveillance in lung cancer screening, irrespective of follow-up CT acquisition parameters.