Direct Visualization and Quantitative Imaging of Small Airway Anatomy In Vivo Using Deep Learning Assisted Diffractive OCT.
Journal
IEEE transactions on bio-medical engineering
ISSN: 1558-2531
Titre abrégé: IEEE Trans Biomed Eng
Pays: United States
ID NLM: 0012737
Informations de publication
Date de publication:
04 Jul 2022
04 Jul 2022
Historique:
pmc-release:
04
01
2024
entrez:
5
7
2022
pubmed:
6
7
2022
medline:
6
7
2022
Statut:
aheadofprint
Résumé
In vivo imaging and quantification of the microstructures of small airways in three dimensions (3D) allows a better understanding and management of airway diseases, such as asthma and chronic obstructive pulmonary disease (COPD). At present, the resolution and contrast of the currently available conventional optical coherence tomography (OCT) imaging technologies operating at 1300 nm remain challenging to directly visualize the fine microstructures of small airways in vivo. We developed an ultrahigh-resolution diffractive endoscopic OCT at 800 nm to afford a resolving power of 1.7 µm (in tissue) with an improved contrast and a custom deep residual learning based image segmentation framework to perform accurate and automated 3D quantification of airway anatomy. The 800-nm diffractive OCT enabled the direct delineation of the structural components in the small airway wall in vivo. We further first demonstrated the 3D anatomic quantification of critical tissue compartments of small airways in sheep using the automated segmentation method. The deep learning assisted diffractive OCT provides a unique ability to access the small airways, directly visualize and quantify the important tissue compartments, such as airway smooth muscle, in the airway wall in vivo in 3D. These pilot results suggest a potential technology for calculating volumetric measurements of small airways in patients in vivo.
Sections du résumé
OBJECTIVE/BACKGROUND
OBJECTIVE
In vivo imaging and quantification of the microstructures of small airways in three dimensions (3D) allows a better understanding and management of airway diseases, such as asthma and chronic obstructive pulmonary disease (COPD). At present, the resolution and contrast of the currently available conventional optical coherence tomography (OCT) imaging technologies operating at 1300 nm remain challenging to directly visualize the fine microstructures of small airways in vivo.
METHODS
METHODS
We developed an ultrahigh-resolution diffractive endoscopic OCT at 800 nm to afford a resolving power of 1.7 µm (in tissue) with an improved contrast and a custom deep residual learning based image segmentation framework to perform accurate and automated 3D quantification of airway anatomy.
RESULTS
RESULTS
The 800-nm diffractive OCT enabled the direct delineation of the structural components in the small airway wall in vivo. We further first demonstrated the 3D anatomic quantification of critical tissue compartments of small airways in sheep using the automated segmentation method.
CONCLUSION
CONCLUSIONS
The deep learning assisted diffractive OCT provides a unique ability to access the small airways, directly visualize and quantify the important tissue compartments, such as airway smooth muscle, in the airway wall in vivo in 3D.
SIGNIFICANCE
CONCLUSIONS
These pilot results suggest a potential technology for calculating volumetric measurements of small airways in patients in vivo.
Identifiants
pubmed: 35786546
doi: 10.1109/TBME.2022.3188173
pmc: PMC9842112
mid: NIHMS1860973
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NHLBI NIH HHS
ID : F32 HL144121
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA153023
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL121788
Pays : United States
Organisme : NHLBI NIH HHS
ID : T32 HL007534
Pays : United States
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