liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Construction of a coronary artery atlas from CT angiography.
Dept. Anatomy with Radiology, University of Auckland, New Zealand.
Auckland Heart Group, Auckland, New Zealand.
Auckland City Hospital, Auckland, New Zealand.
Dept. Anatomy with Radiology, University of Auckland, New Zealand.
Show others and affiliations
2014 (English)In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, Springer, 2014, Vol. 8674, no 2014, 513-520 p.Conference paper, Published paper (Refereed)
Abstract [en]

Describing the detailed statistical anatomy of the coronary artery tree is important for determining the aetiology of heart disease. A number of studies have investigated geometrical features and have found that these correlate with clinical outcomes, e.g. bifurcation angle with major adverse cardiac events. These methodologies were mainly two-dimensional, manual and prone to inter-observer variability, and the data commonly relates to cases already with pathology. We propose a hybrid atlasing methodology to build a population of computational models of the coronary arteries to comprehensively and accurately assess anatomy including 3D size, geometry and shape descriptors. A random sample of 122 cardiac CT scans with a calcium score of zero was segmented and analysed using a standardised protocol. The resulting atlas includes, but is not limited to, the distributions of the coronary tree in terms of angles, diameters, centrelines, principal component shape analysis and cross-sectional contours. This novel resource will facilitate the improvement of stent design and provide a reference for hemodynamic simulations, and provides a basis for large normal and pathological databases.

Place, publisher, year, edition, pages
Springer, 2014. Vol. 8674, no 2014, 513-520 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 8674
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-115596DOI: 10.1007/978-3-319-10470-6_64PubMedID: 25485418OAI: oai:DiVA.org:liu-115596DiVA: diva2:796190
Conference
Med Image Comput Comput Assist Interv (MICCAI) Boston 2014
Available from: 2015-03-18 Created: 2015-03-17 Last updated: 2017-03-01

Open Access in DiVA

No full text

Other links

Publisher's full textPubMed

Authority records BETA

Wang, Chunliang

Search in DiVA

By author/editor
Wang, Chunliang
By organisation
Division of Radiological SciencesFaculty of Health SciencesCenter for Medical Image Science and Visualization (CMIV)
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 109 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf