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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Updated: Jul 20, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Deep Learning-Based CT Reconstruction Kernel Conversion in the Quantification of Interstitial Lung Disease: Effect on

Yura Ahn¹, Sang Min Lee¹, Yujin Nam²

¹Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 138-736, Republic of Korea (Y.A., S.M.L., J.C., K.-H.D., J.B.S.).

Academic Radiology

|July 29, 2023

Summary

Different computed tomography (CT) reconstruction kernels impact interstitial lung disease (ILD) quantification. Deep learning-based kernel conversion significantly reduces measurement variability, enhancing the reproducibility of ILD analysis.

Keywords:

Deep learning Interstitial Lung disease

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Area of Science:

Radiology
Medical Imaging
Pulmonology

Background:

Computed tomography (CT) reconstruction kernels influence quantitative analysis of interstitial lung disease (ILD).
Variability in ILD quantification across different CT kernels poses challenges for consistent diagnosis and monitoring.
Standardization of CT image analysis is crucial for reliable ILD assessment.

Purpose of the Study:

To evaluate the impact of various CT reconstruction kernels on ILD quantification.
To determine the efficacy of deep learning-based kernel conversion in reducing measurement variability.
To assess the improvement in automated quantification reproducibility for ILD.

Main Methods:

Retrospective analysis of high-resolution CT scans from 194 patients with ILD or interstitial lung abnormality.
Image reconstruction using three kernels: B30f, B50f, and B60f (reference standard).
Deep learning-based conversion of B30f and B50f images to B60f, followed by quantification of ILD patterns and fibrotic scores using commercial software.

Main Results:

Different kernels led to variations in quantified ILD patterns, with under/overestimation of reticular opacity and honeycombing.
Kernel conversion substantially reduced measurement variability, narrowing the mean difference and 95% limits of agreement.
Deep learning-based conversion of B50f to B60f resulted in near-identical fibrotic scores to the original B60f.

Conclusions:

CT reconstruction kernels significantly affect quantitative ILD analysis.
Deep learning-based kernel conversion is an effective method to mitigate measurement variability.
This approach enhances the reproducibility of automated ILD quantification, improving diagnostic consistency.