Estimation of Diastolic Biomarkers: Sensitiviy to Fibre Orientation

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Citations (Scopus)

Abstract

An accurate estimation of myocardial stiffness and decaying active tension is critical for the characterization of the diastolic function of the heart. Computational cardiac models can be used to analyse deformation and pressure data from the left ventricle in order to estimate these diastolic metrics. The results of this methodology depend on several model assumptions. In this work we reveal a nominal impact of the choice of myocardial fibre orientation between a rule-based description and personalised approach based on diffusion-tensor magnetic resonance imaging. This result suggests the viability of simplified clinical imaging protocols for the model-based estimation of diastolic biomarkers.
Original languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart
Subtitle of host publicationImaging and Modelling Challenges
PublisherSpringer
Pages105-113
Number of pages9
VolumeLNCS 8896
ISBN (Electronic)9783319146782
ISBN (Print)9783319146775
DOIs
Publication statusPublished - 1 Jan 2015
Event5th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2014 Held in Conjunction with Medical Image Computing and Computer Assisted Intervention Conference, MICCAI 2014 - Boston, United States
Duration: 18 Sept 201418 Sept 2014

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
Volume8896
ISSN (Print)0302-9743

Conference

Conference5th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2014 Held in Conjunction with Medical Image Computing and Computer Assisted Intervention Conference, MICCAI 2014
Country/TerritoryUnited States
CityBoston
Period18/09/201418/09/2014

Fingerprint

Dive into the research topics of 'Estimation of Diastolic Biomarkers: Sensitiviy to Fibre Orientation'. Together they form a unique fingerprint.

Cite this