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Voxel-wise assessment of lung aeration changes on CT images using image registration: application to acute respiratory distress syndrome (ARDS)

  • Maciej OrkiszEmail author
  • Alfredo Morales Pinzón
  • Jean-Christophe Richard
  • Claude Guérin
  • Leslie Evelyn Solórzano Vargas
  • Daniela Florentina Sicaru
  • Camila García Hernández
  • Margarita M. Gómez Ballén
  • Bruno Neyran
  • Eduardo E. Dávila Serrano
  • Marcela Hernández Hoyos
Original Article

Abstract

Purpose

(1) To improve the accuracy of global and regional alveolar-recruitment quantification in CT scan pairs by accounting for lung-tissue displacements and deformation, (2) To propose a method for local-recruitment calculation.

Methods

Recruitment was calculated by subtracting the quantity of non-aerated lung tissues between expiration and inspiration. To assess global recruitment, lung boundaries were first interactively delineated at inspiration, and then they were warped based on automatic image registration to define the boundaries at expiration. To calculate regional recruitment, the lung mask defined at inspiration was cut into pieces, and these were also warped to encompass the same tissues at expiration. Local-recruitment map was calculated as follows: For each voxel at expiration, the matching location at inspiration was determined by image registration, non-aerated voxels were counted in the neighborhood of the respective locations, and the voxel count difference was normalized by the neighborhood size. The methods were evaluated on 120 image pairs of 12 pigs with experimental acute respiratory distress syndrome.

Results

The dispersion of global- and regional-recruitment values decreased when using image registration, compared to the conventional approach neglecting tissue motion. Local-recruitment maps overlaid onto the original images were visually consistent, and the sum of these values over the whole lungs was very close to the global-recruitment estimate, except four outliers.

Conclusions

Image registration can compensate lung-tissue displacements and deformation, thus improving the quantification of alveolar recruitment. Local-recruitment calculation can also benefit from image registration, and its values can be overlaid onto the original image to display a local-recruitment map. They also can be integrated over arbitrarily shaped regions to assess regional or global recruitment.

Keywords

Acute respiratory distress syndrome Alveolar recruitment Computed tomography Image processing Image registration 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This study was approved by our Institutional Review Board for the care of animal subjects (Comité d’Expérimentation Animale de l’Université Lyon 1). All procedures performed in studies involving animals were in accordance with the ethical standards of the institution at which the studies were conducted. All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.

Supplementary material

11548_2019_2064_MOESM1_ESM.pdf (15.8 mb)
Supplementary material 1 (pdf 16196 KB)

Supplementary material 2 (mp4 12130 KB)

Supplementary material 3 (mp4 5658 KB)

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Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.CREATIS UMR 5220, U1206Univ Lyon, Université Claude Bernard Lyon 1, INSA-Lyon, UJM-Saint Etienne, CNRS, InsermVilleurbanneFrance
  2. 2.Systems and Computing Engineering DepartmentUniversidad de los AndesBogotáColombia
  3. 3.Service de Réanimation MédicaleHospices Civils de Lyon, Hôpital de la Croix RousseLyonFrance
  4. 4.Université de Lyon, Université Lyon 1LyonFrance
  5. 5.IMRB U955 Eq13INSERMCréteilFrance
  6. 6.HP2 U1042INSERMGrenobleFrance
  7. 7.Service de médecine intensive réanimationCHU Grenoble-AlpesGrenobleFrance
  8. 8.Faculty of Electronics, Telecommunications and Information TechnologyUniversity Politehnica of BucharestBucharestRomania

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