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Leveraging deep learning-based kernel conversion for more precise airway quantification on CT.

Jooae Choe1, Jihye Yun2, Myeong Jun Kim3

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Summary
This summary is machine-generated.

Different CT reconstruction kernels significantly impact automated airway measurements. Deep learning-based kernel conversion reduces variability across vendors for lung-dedicated kernels, enhancing quantitative CT (QCT) analysis consistency.

Keywords:
Airway quantificationDeep learningQuantitative CT

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Area of Science:

  • Radiology
  • Medical Imaging
  • Computational Pathology

Background:

  • Quantitative CT (QCT) analysis of airways is crucial for diagnosing and monitoring respiratory diseases.
  • Variability in QCT measurements due to different reconstruction kernels can hinder accurate automated airway quantification.
  • Standardizing QCT analysis across different imaging protocols and vendors is essential for reliable clinical interpretation.

Purpose of the Study:

  • To assess the impact of varying CT reconstruction kernels on fully automated airway quantitative CT (QCT) measures.
  • To evaluate the effectiveness of deep learning-based kernel conversion in reducing measurement variability.
  • To identify factors influencing the success of kernel conversion for airway QCT.

Main Methods:

  • Retrospective analysis of 96 non-enhanced chest CT scans from two centers.
  • Reconstruction of CT scans using four kernels from three vendors.
  • Application of deep learning-based kernel conversion to sharp kernel images, targeting medium soft kernel as reference.
  • Evaluation of automated airway quantification before and after kernel conversion using statistical analysis and concordance correlation coefficient (CCC).

Main Results:

  • Sharper kernels led to decreased airway QCT measures (e.g., Pi10, wall thickness) with significant variability across vendors (p < 0.001).
  • Kernel conversion substantially reduced variability for lung-dedicated kernels across vendors A, B, and C (pooled CCC improved from 0.26-0.59 to 0.71-0.92).
  • Kernel conversion was less effective for non-lung-dedicated kernels and showed limited improvement for subsegmental airways.

Conclusions:

  • Deep learning-based kernel conversion effectively reduces measurement variability in automated airway QCT across different kernels and vendors for lung-dedicated kernels.
  • The effectiveness of kernel conversion is limited for non-lung-dedicated kernels and subsegmental airways, necessitating further refinement.
  • Consistent airway segmentation and precise anatomic labeling are critical for enhancing the reproducibility of automated airway quantification.