Linked Life Data

15519339

Source:http://linkedlifedata.com/resource/pubmed/id/15519339

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2004-11-2
pubmed:abstractText
Abnormal haemodynamic conditions are implicated in the development of anastomotic myointimal hyperplasia (MIH). However, these conditions are difficult to determine in vivo, prompting research using ex vivo idealised models. To relate the understanding gained in idealised geometries to anatomically correct conditions we have investigated a reproducible approach to classify in vivo distal graft anastomoses and their inter-patient variability. In vivo distal anastomotic geometries were acquired by magnetic resonance (MR) angiography from 13 patients who had undergone infrageniculate autologous venous by-pass surgery. On average, the images were acquired 2 weeks post-operatively. Five patients also underwent repeat examinations 2 to 7 weeks later. For each geometry, the surface of the arterial lumen is represented by the zero level set of an implicit function constructed from radial basis functions that minimise curvature. The three-dimensional binary image created from the interpolated surface is processed using a skeletonisation algorithm to obtain the centreline of each branch in the geometry. This allows for the measurement of the branching angles between straight line approximations of the centrelines of each vessel, averaging them over a characteristic length of each anastomosis. The main finding in the application of the proposed classification methodology to this set of patients is that the spectrum of anastomoses can be reduced to a small subset of cases characterised by two angles: the angle between the graft and the plane of the host artery and the angle between the graft and the proximal branch of the artery.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
0021-9290
pubmed:author
pubmed:issnType
Print
pubmed:volume
38
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
47-62
pubmed:dateRevised
2009-11-11
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Automated classification of peripheral distal by-pass geometries reconstructed from medical data.
pubmed:affiliation
Department of Aeronautics, Biofluids Group, Imperial College London, London SW7 2AZ, UK.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't