Quantitative Imaging for Interstitial Lung Disease

Quantitative imaging has emerged as a promising tool for the diagnosis, classification, and prognostication of interstitial lung disease (ILD). Both global and regional lung abnormalities can be objectively and reproducibly measured using quantitative imaging, which is particularly useful for early disease evaluation and assessment of subtle changes. Accurate ILD classification and identification of inconspicuous changes allow for more personalized treatment decisions and, ultimately, improved patient outcomes. Because CT is the primary imaging modality for ILD evaluation, most of the computer-aided support systems have been developed for this modality and are referred to as <i>quantitative CT</i>. While CT continues to advance with functional capability using dual-energy technology, new MRI techniques are being developed that offer the ability to further improve ILD evaluation. Recent advancements in the field of artificial intelligence underly the development of these new quantitative imaging tools. As quantitative imaging for ILD evaluation becomes more common, it will likely play an increasingly important role in the general clinical radiology workflow, necessitating a familiarity of its use for the general radiologist. This review summarizes current applications of quantitative CT in the evaluation of fibrotic ILDs, including idiopathic pulmonary fibrosis, hypersensitivity pneumonitis, and connective tissue disease-related ILD, and highlights emerging quantitative MRI techniques for ILD assessment. <b>Keywords:</b> Applications-CT, Deep Learning, Machine Learning, Radiomics, CT-Quantitative, Thorax, Lung © RSNA, 2025.

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Publication Details

Journal
Radiology Cardiothoracic Imaging
Published
2025-12-01
DOI
https://doi.org/10.1148/ryct.250041
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Quantitative Imaging for Interstitial Lung Disease

Chi Wan Koo, Renu Singh, C. M. Anderson
Radiology Cardiothoracic Imaging
Radiomics and Machine Learning in Medical Imaging
article

Quantitative Imaging for Interstitial Lung Disease

Chi Wan Koo, Renu Singh, C. M. Anderson
article en

Abstract

Quantitative imaging has emerged as a promising tool for the diagnosis, classification, and prognostication of interstitial lung disease (ILD). Both global and regional lung abnormalities can be objectively and reproducibly measured using quantitative imaging, which is particularly useful for early disease evaluation and assessment of subtle changes. Accurate ILD classification and identification of inconspicuous changes allow for more personalized treatment decisions and, ultimately, improved patient outcomes. Because CT is the primary imaging modality for ILD evaluation, most of the computer-aided support systems have been developed for this modality and are referred to as <i>quantitative CT</i>. While CT continues to advance with functional capability using dual-energy technology, new MRI techniques are being developed that offer the ability to further improve ILD evaluation. Recent advancements in the field of artificial intelligence underly the development of these new quantitative imaging tools. As quantitative imaging for ILD evaluation becomes more common, it will likely play an increasingly important role in the general clinical radiology workflow, necessitating a familiarity of its use for the general radiologist. This review summarizes current applications of quantitative CT in the evaluation of fibrotic ILDs, including idiopathic pulmonary fibrosis, hypersensitivity pneumonitis, and connective tissue disease-related ILD, and highlights emerging quantitative MRI techniques for ILD assessment. <b>Keywords:</b> Applications-CT, Deep Learning, Machine Learning, Radiomics, CT-Quantitative, Thorax, Lung © RSNA, 2025.

Radiology Cardiothoracic ImagingVol. 7(6)
Mayo Clinic (US), WinnMed (US)
Openalex Percentile: Top 47%
Radiomics and Machine Learning in Medical Imaging
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Quantitative Imaging for Interstitial Lung Disease — Chi Wan Koo, Renu Singh, et al. · Radiology Cardiothoracic Imaging (2025) | TGRS Research Map | TGRS