TY - GEN
T1 - Assessing the impact of CT reconstruction kernel on radiomic features extracted from normal and fibrotic tissue in patients with diffuse lung disease
AU - Welland, Spencer H.
AU - Oh, Andrea
AU - Pourzand, Lila
AU - Stayman, J. Webster
AU - Gang, Grace J.
AU - McNitt-Gray, Michael F.
AU - Kim, Grace Hyun J.
N1 - Publisher Copyright:
© 2026 SPIE.
PY - 2026/4/2
Y1 - 2026/4/2
N2 - 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.
AB - 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.
KW - Radiomic features
KW - computed tomography
KW - diffuse lung disease
KW - fibrosis
KW - quantitative image features
KW - reconstruction kernel
UR - https://www.scopus.com/pages/publications/105039292266
UR - https://www.scopus.com/pages/publications/105039292266#tab=citedBy
U2 - 10.1117/12.3087923
DO - 10.1117/12.3087923
M3 - Conference contribution
AN - SCOPUS:105039292266
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2026
A2 - Ganguly, Arundhuti
A2 - Li, Ke
A2 - Abbaszadeh, Shiva
PB - SPIE
T2 - Medical Imaging 2026: Physics of Medical Imaging
Y2 - 15 February 2025 through 19 February 2025
ER -