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Assessing the impact of CT reconstruction kernel on radiomic features extracted from normal and fibrotic tissue in patients with diffuse lung disease

  • Spencer H. Welland
  • , Andrea Oh
  • , Lila Pourzand
  • , J. Webster Stayman
  • , Grace J. Gang
  • , Michael F. McNitt-Gray
  • , Grace Hyun J. Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Introduction: Patients with diffuse lung disease (DLD) undergo CT scans for diagnosis and evaluation. Attempts to characterize the radiographic appearance of these regions with quantitative features like radiomics have been hindered by variability in CT acquisition and reconstruction parameters. The purpose of this investigation is to characterize the effect of CT reconstruction kernel on radiomic features in normal and fibrotic regions in DLD patients. Methods: Raw CT projection data of DLD patients receiving a thoracic CT exam was collected from 3 CT scanners (Definition AS, Flash, and Force; Siemens Healthineers, Forchheim, Germany) and retrospectively reconstructed with 5 reconstruction kernels (smooth, medium-smooth, medium, medium-sharp, and sharp). The medium kernel is part of the clinical protocol at our institution and considered the reference kernel for this investigation. Regions of classic normal and classic fibrosis were annotated by 2 thoracic radiologists on images reconstructed with reference kernels. The annotations were copied across each reconstruction, and 72 radiomic features were extracted from each annotation using Pyradiomics. Agreement between features in reference and non-reference kernels was assessed with concordance correlation coefficient (CCC). Features were considered robust if average CCC was > 0.9. Results: There were 66 patients included with 116 regions of normal and 208 regions of fibrosis annotated across all patients. Across kernels in normal tissue, 0/16 GLSZM, 1/16 GLRLM, 0/22 GLCM, and 5/18 first-order features were robust (average CCC > 0.9). Across kernels in fibrotic tissue, 1/16 GLSZM, 3/16 GLRLM, 3/22 GLCM, and 7/18 first-order features were robust, however the effect magnitude of sharper kernels was greater than in normal tissue. Conclusion: First-order features were more robust than other features; GLCM features were the least robust. There are more robust features across kernels in fibrotic tissue, but the magnitude of kernel effect is greater in fibrotic tissue than in normal tissue.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2026
Subtitle of host publicationPhysics of Medical Imaging
EditorsArundhuti Ganguly, Ke Li, Shiva Abbaszadeh
PublisherSPIE
ISBN (Electronic)9781510697850
DOIs
StatePublished - Apr 2 2026
EventMedical Imaging 2026: Physics of Medical Imaging - Vancouver, Canada
Duration: Feb 15 2025Feb 19 2025

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume13924
ISSN (Print)1605-7422
ISSN (Electronic)2410-9045

Conference

ConferenceMedical Imaging 2026: Physics of Medical Imaging
Country/TerritoryCanada
CityVancouver
Period2/15/252/19/25

Keywords

  • Radiomic features
  • computed tomography
  • diffuse lung disease
  • fibrosis
  • quantitative image features
  • reconstruction kernel

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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