Healthy lung vessel morphology derived from thoracic computed tomography

Show simple item record

dc.contributor.author Pienn, M en
dc.contributor.author Burgard, C en
dc.contributor.author Payer, C en
dc.contributor.author Avian, A en
dc.contributor.author Urschler, Martin en
dc.contributor.author Stollberger, R en
dc.contributor.author Olschewski, A en
dc.contributor.author Olschewski, H en
dc.contributor.author Johnson, T en
dc.contributor.author Meinel, FG en
dc.contributor.author Bálint, Z en
dc.date.accessioned 2019-10-01T22:53:03Z en
dc.date.issued 2018-04 en
dc.identifier.citation Frontiers in Physiology 9:10 pages Article number 346 Apr 2018 en
dc.identifier.issn 1664-042X en
dc.identifier.uri http://hdl.handle.net/2292/48319 en
dc.description.abstract Knowledge of the lung vessel morphology in healthy subjects is necessary to improve our understanding about the functional network of the lung and to recognize pathologic deviations beyond the normal inter-subject variation. Established values of normal lung morphology have been derived from necropsy material of only very few subjects. In order to determine morphologic readouts from a large number of healthy subjects, computed tomography pulmonary angiography (CTPA) datasets, negative for pulmonary embolism, and other thoracic pathologies, were analyzed using a fully-automatic, in-house developed artery/vein separation algorithm. The number, volume, and tortuosity of the vessels in a diameter range between 2 and 10 mm were determined. Visual inspection of all datasets was used to exclude subjects with poor image quality or inadequate artery/vein separation from the analysis. Validation of the algorithm was performed manually by a radiologist on randomly selected subjects. In 123 subjects (men/women: 55/68), aged 59 ± 17 years, the median overlap between visual inspection and fully-automatic segmentation was 94.6% (69.2-99.9%). The median number of vessel segments in the ranges of 8-10, 6-8, 4-6, and 2-4 mm diameter was 9, 34, 134, and 797, respectively. Number of vessel segments divided by the subject's lung volume was 206 vessels/L with arteries and veins contributing almost equally. In women this vessel density was about 15% higher than in men. Median arterial and venous volumes were 1.52 and 1.54% of the lung volume, respectively. Tortuosity was best described with the sum-of-angles metric and was 142.1 rad/m (138.3-144.5 rad/m). In conclusion, our fully-automatic artery/vein separation algorithm provided reliable measures of pulmonary arteries and veins with respect to age and gender. There was a large variation between subjects in all readouts. No relevant dependence on age, gender, or vessel type was observed. These data may provide reference values for morphometric analysis of lung vessels. en
dc.format.medium Electronic-eCollection en
dc.language eng en
dc.publisher Frontiers Media S.A. en
dc.relation.ispartofseries Frontiers in Physiology en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.rights.uri https://creativecommons.org/licenses/by/4.0/ en
dc.rights.uri https://www.frontiersin.org/journals/physiology#author-guidelines en
dc.title Healthy lung vessel morphology derived from thoracic computed tomography en
dc.type Journal Article en
dc.identifier.doi 10.3389/fphys.2018.00346 en
pubs.volume 9 en
dc.rights.holder Copyright: The authors en
pubs.publication-status Published en
dc.rights.accessrights http://purl.org/eprint/accessRights/OpenAccess en
pubs.subtype Article en
pubs.elements-id 776165 en
pubs.org-id Science en
pubs.org-id School of Computer Science en
dc.identifier.eissn 1664-042X en
pubs.number 346 en
pubs.record-created-at-source-date 2019-10-09 en
pubs.dimensions-id 29755360 en


Files in this item

Find Full text

This item appears in the following Collection(s)

Show simple item record

Share

Search ResearchSpace


Browse

Statistics