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Quantitative CT imaging and advanced visualization methods: potential application in novel coronavirus disease 2019 (COVID-19) pneumonia.
Nagpal, Prashant; Guo, Junfeng; Shin, Kyung Min; Lim, Jae-Kwang; Kim, Ki Beom; Comellas, Alejandro P; Kaczka, David W; Peterson, Samuel; Lee, Chang Hyun; Hoffman, Eric A.
  • Nagpal P; Department of Radiology, University of Iowa, Carver College of Medicine, Iowa City, IA, USA.
  • Lim JK; Department of Radiology, School of Medicine, Kyungpook National University, Daegu, South Korea.
  • Kim KB; Department of Radiology, Daegu Fatima Hospital, Daegu, South Korea.
  • Comellas AP; Department of Internal Medicine, University of Iowa, Carver College of Medicine, Iowa City, IA, USA.
  • Peterson S; VIDA Diagnostics, Coralville, IA, USA.
BJR Open ; 3(1): 20200043, 2021.
Article in English | MEDLINE | ID: covidwho-1133651
ABSTRACT
Increasingly, quantitative lung computed tomography (qCT)-derived metrics are providing novel insights into chronic inflammatory lung diseases, including chronic obstructive pulmonary disease, asthma, interstitial lung disease, and more. Metrics related to parenchymal, airway, and vascular anatomy together with various measures associated with lung function including regional parenchymal mechanics, air trapping associated with functional small airways disease, and dual-energy derived measures of perfused blood volume are offering the ability to characterize disease phenotypes associated with the chronic inflammatory pulmonary diseases. With the emergence of COVID-19, together with its widely varying degrees of severity, its rapid progression in some cases, and the potential for lengthy post-COVID-19 morbidity, there is a new role in applying well-established qCT-based metrics. Based on the utility of qCT tools in other lung diseases, previously validated supervised classical machine learning methods, and emerging unsupervised machine learning and deep-learning approaches, we are now able to provide desperately needed insight into the acute and the chronic phases of this inflammatory lung disease. The potential areas in which qCT imaging can be beneficial include improved accuracy of diagnosis, identification of clinically distinct phenotypes, improvement of disease prognosis, stratification of care, and early objective evaluation of intervention response. There is also a potential role for qCT in evaluating an increasing population of post-COVID-19 lung parenchymal changes such as fibrosis. In this work, we discuss the basis of various lung qCT methods, using case-examples to highlight their potential application as a tool for the exploration and characterization of COVID-19, and offer scanning protocols to serve as templates for imaging the lung such that these established qCT analyses have the best chance at yielding the much needed new insights.

Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Topics: Long Covid Language: English Journal: BJR Open Year: 2021 Document Type: Article Affiliation country: Bjro.20200043

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Topics: Long Covid Language: English Journal: BJR Open Year: 2021 Document Type: Article Affiliation country: Bjro.20200043