QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications.

Journal Article (Journal Article)

As the COVID-19 pandemic impacts global populations, computed tomography (CT) lung imaging is being used in many countries to help manage patient care as well as to rapidly identify potentially useful quantitative COVID-19 CT imaging biomarkers. Quantitative COVID-19 CT imaging applications, typically based on computer vision modeling and artificial intelligence algorithms, include the potential for better methods to assess COVID-19 extent and severity, assist with differential diagnosis of COVID-19 versus other respiratory conditions, and predict disease trajectory. To help accelerate the development of robust quantitative imaging algorithms and tools, it is critical that CT imaging is obtained following best practices of the quantitative lung CT imaging community. Toward this end, the Radiological Society of North America's (RSNA) Quantitative Imaging Biomarkers Alliance (QIBA) CT Lung Density Profile Committee and CT Small Lung Nodule Profile Committee developed a set of best practices to guide clinical sites using quantitative imaging solutions and to accelerate the international development of quantitative CT algorithms for COVID-19. This guidance document provides quantitative CT lung imaging recommendations for COVID-19 CT imaging, including recommended CT image acquisition settings for contemporary CT scanners. Additional best practice guidance is provided on scientific publication reporting of quantitative CT imaging methods and the importance of contributing COVID-19 CT imaging datasets to open science research databases.

Full Text

Duke Authors

Cited Authors

  • Avila, RS; Fain, SB; Hatt, C; Armato, SG; Mulshine, JL; Gierada, D; Silva, M; Lynch, DA; Hoffman, EA; Ranallo, FN; Mayo, JR; Yankelevitz, D; Estepar, RSJ; Subramaniam, R; Henschke, CI; Guimaraes, A; Sullivan, DC

Published Date

  • September 2021

Published In

Volume / Issue

  • 77 /

Start / End Page

  • 151 - 157

PubMed ID

  • 33684789

Pubmed Central ID

  • PMC7906537

Electronic International Standard Serial Number (EISSN)

  • 1873-4499

Digital Object Identifier (DOI)

  • 10.1016/j.clinimag.2021.02.017

Language

  • eng

Conference Location

  • United States