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.

QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications / Avila, Ricardo S; Fain, Sean B; Hatt, Chuck; Armato, Samuel G; Mulshine, James L; Gierada, David; Silva, Mario; Lynch, David A; Hoffman, Eric A; Ranallo, Frank N; Mayo, John R; Yankelevitz, David; Estepar, Raul San Jose; Subramaniam, Raja; Henschke, Claudia I; Guimaraes, Alex; Sullivan, Daniel C. - In: CLINICAL IMAGING. - ISSN 0899-7071. - 77(2021), pp. 151-157-157. [10.1016/j.clinimag.2021.02.017]

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

Silva, Mario
Writing – Original Draft Preparation
;
2021

Abstract

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.
QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications / Avila, Ricardo S; Fain, Sean B; Hatt, Chuck; Armato, Samuel G; Mulshine, James L; Gierada, David; Silva, Mario; Lynch, David A; Hoffman, Eric A; Ranallo, Frank N; Mayo, John R; Yankelevitz, David; Estepar, Raul San Jose; Subramaniam, Raja; Henschke, Claudia I; Guimaraes, Alex; Sullivan, Daniel C. - In: CLINICAL IMAGING. - ISSN 0899-7071. - 77(2021), pp. 151-157-157. [10.1016/j.clinimag.2021.02.017]
File in questo prodotto:
File Dimensione Formato  
Avila-2021-QIBA guidance_ Computed tomography.pdf

non disponibili

Tipologia: Versione (PDF) editoriale
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 442.6 kB
Formato Adobe PDF
442.6 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2889460
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 5
social impact