Ventricular geometry and fiber orientation may undergo global or local
remodeling in cardiac disease. However, there are as yet no mathematical
and computational methods for quantifying variation of geometry and
fiber orientation or the nature of their remodeling in disease. Toward
this goal, a landmark and image intensity-based large deformation
diffeomorphic metric mapping (LDDMM) method to transform heart
geometry into common coordinates for quantification of shape and
form was developed. Two automated landmark placement methods for
modeling tissue deformations expected in different cardiac pathologies
are presented. The transformations, computed using the combined use
of landmarks and image intensities, yields high-registration accuracy
of heart anatomies even in the presence of significant variation
of cardiac shape and form. Once heart anatomies have been registered,
properties of tissue geometry and cardiac fiber orientation in corresponding
regions of different hearts may be quantified.
%0 Journal Article
%1 Beg_2004_1167
%A Beg, Mirza Faisal
%A Helm, Patrick A
%A McVeigh, Elliot
%A Miller, Michael I
%A Winslow, Raimond L
%D 2004
%J Magn. Reson. Med.
%K 15508155 Algorithms, Animals, Biology, Computational Computer-Assisted, Diffusion Diseases, Dogs, Gov't, Heart Heart, Image Imaging, Magnetic Male, Models, Non-U.S. P.H.S., Processing, Reference Research Resonance Support, Theoretical, U.S. Values,
%N 5
%P 1167--1174
%R 10.1002/mrm.20255
%T Computational cardiac anatomy using MRI.
%U http://dx.doi.org/10.1002/mrm.20255
%V 52
%X Ventricular geometry and fiber orientation may undergo global or local
remodeling in cardiac disease. However, there are as yet no mathematical
and computational methods for quantifying variation of geometry and
fiber orientation or the nature of their remodeling in disease. Toward
this goal, a landmark and image intensity-based large deformation
diffeomorphic metric mapping (LDDMM) method to transform heart
geometry into common coordinates for quantification of shape and
form was developed. Two automated landmark placement methods for
modeling tissue deformations expected in different cardiac pathologies
are presented. The transformations, computed using the combined use
of landmarks and image intensities, yields high-registration accuracy
of heart anatomies even in the presence of significant variation
of cardiac shape and form. Once heart anatomies have been registered,
properties of tissue geometry and cardiac fiber orientation in corresponding
regions of different hearts may be quantified.
@article{Beg_2004_1167,
abstract = {Ventricular geometry and fiber orientation may undergo global or local
remodeling in cardiac disease. However, there are as yet no mathematical
and computational methods for quantifying variation of geometry and
fiber orientation or the nature of their remodeling in disease. Toward
this goal, a landmark and image intensity-based large deformation
diffeomorphic metric mapping ({LDDMM}) method to transform heart
geometry into common coordinates for quantification of shape and
form was developed. Two automated landmark placement methods for
modeling tissue deformations expected in different cardiac pathologies
are presented. The transformations, computed using the combined use
of landmarks and image intensities, yields high-registration accuracy
of heart anatomies even in the presence of significant variation
of cardiac shape and form. Once heart anatomies have been registered,
properties of tissue geometry and cardiac fiber orientation in corresponding
regions of different hearts may be quantified.},
added-at = {2009-06-03T11:20:58.000+0200},
author = {Beg, Mirza Faisal and Helm, Patrick A and McVeigh, Elliot and Miller, Michael I and Winslow, Raimond L},
biburl = {https://www.bibsonomy.org/bibtex/2af05ae75a5dbb6bdd02920726b42f1a2/hake},
description = {The whole bibliography file I use.},
doi = {10.1002/mrm.20255},
file = {Beg_2004_1167.pdf:Beg_2004_1167.pdf:PDF},
interhash = {d610512a4e042ebcbf86cc749123ef1a},
intrahash = {af05ae75a5dbb6bdd02920726b42f1a2},
journal = {Magn. Reson. Med.},
keywords = {15508155 Algorithms, Animals, Biology, Computational Computer-Assisted, Diffusion Diseases, Dogs, Gov't, Heart Heart, Image Imaging, Magnetic Male, Models, Non-U.S. P.H.S., Processing, Reference Research Resonance Support, Theoretical, U.S. Values,},
month = Nov,
number = 5,
pages = {1167--1174},
pmid = {15508155},
timestamp = {2009-06-03T11:21:02.000+0200},
title = {Computational cardiac anatomy using {MRI}.},
url = {http://dx.doi.org/10.1002/mrm.20255},
volume = 52,
year = 2004
}