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Voxel-wise assessment of lung aeration changes on CT images using image registration: application to acute respiratory distress syndrome (ARDS)
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2019 (English)In: International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410, E-ISSN 1861-6429, p. 1-9Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2019. p. 1-9
Keywords [en]
Acute respiratory distress syndrome, Alveolar recruitment, Computed tomography, Image processing, Image registration
National Category
Bioinformatics (Computational Biology)
Research subject
Bioinformatics; Radiology
Identifiers
URN: urn:nbn:se:uu:diva-392944DOI: 10.1007/s11548-019-02064-3OAI: oai:DiVA.org:uu-392944DiVA, id: diva2:1350405
Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-09-11

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Publisher's full texthttps://link.springer.com/article/10.1007%2Fs11548-019-02064-3

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Solorzano, Leslie

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Orkisz, MaciejMorales, AlfredoRichard, Jean-ChristopheGuerin, ClaudeSolorzano, LeslieHernández Hoyos, Marcela
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Computerized Image Analysis and Human-Computer InteractionScience for Life Laboratory, SciLifeLabDivision of Visual Information and Interaction
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International Journal of Computer Assisted Radiology and Surgery
Bioinformatics (Computational Biology)

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