Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-04271-3_33
Title: Predictive simulation of bidirectional Glenn shunt using a hybrid blood vessel model
Authors: Li, H.
Leow, W.K. 
Chiu, I.-S.
Issue Date: 2009
Source: Li, H.,Leow, W.K.,Chiu, I.-S. (2009). Predictive simulation of bidirectional Glenn shunt using a hybrid blood vessel model. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5762 LNCS (PART 2) : 266-274. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-04271-3_33
Abstract: This paper proposes a method for performing predictive simulation of cardiac surgery. It applies a hybrid approach to model the deformation of blood vessels. The hybrid blood vessel model consists of a reference Cosserat rod and a surface mesh. The reference Cosserat rod models the blood vessel's global bending, stretching, twisting and shearing in a physically correct manner, and the surface mesh models the surface details of the blood vessel. In this way, the deformation of blood vessels can be computed efficiently and accurately. Our predictive simulation system can produce complex surgical results given a small amount of user inputs. It allows the surgeon to easily explore various surgical options and evaluate them. Tests of the system using bidirectional Glenn shunt (BDG) as an application example show that the results produced by the system are similar to real surgical results. © 2009 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/41491
ISBN: 3642042708
ISSN: 03029743
DOI: 10.1007/978-3-642-04271-3_33
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

2
checked on Dec 13, 2017

Page view(s)

43
checked on Dec 16, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.