Abstract
Statistical descriptions of regional wall motion abnormalities of the heart are key to understanding both sub-clinical and clinical progression of dysfunction. In this paper we establish a temporal registration framework of the cardiac cycle to build a spatio-temporal atlas of 300 asymptomatic volunteers and 300 symptomatic patients with myocardial infarction. A finite-element model was customised to each person's magnetic resonance images with expert-guided semi-automatic spatial and temporal registration of model parameters. A piece-wise linear temporal registration from user-defined key frames was followed by a Fourier series temporal estimation, providing temporal continuity. All spatial and temporal data were then statistically analysed by means of principal component analysis. Results show differences in sphericity, wall thickening and mitral valve dynamics between the two groups. The modes are available from www.cardiacatlas.org. These atlases can be readily applied to abnormality detection and quantification and can also aid in anatomically constrained shape-based algorithms in automatic planning or segmentation.
| Original language | English (US) |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer Verlag |
| Pages | 143-151 |
| Number of pages | 9 |
| Volume | 8330 LNCS |
| ISBN (Print) | 9783642542671 |
| DOIs | |
| State | Published - 2014 |
| Event | 4th International Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM 2013, Held in Conjunction with MICCAI 2013 - Nagoya, Japan Duration: Sep 26 2013 → Sep 26 2013 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 8330 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 4th International Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM 2013, Held in Conjunction with MICCAI 2013 |
|---|---|
| Country | Japan |
| City | Nagoya |
| Period | 9/26/13 → 9/26/13 |
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ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Continuous spatio-temporal atlases of the asymptomatic and infarcted hearts. / Medrano-Gracia, Pau; Cowan, Brett R.; Bluemke, David A.; Finn, J. Paul; Kadish, Alan H.; Lee, Daniel C.; Lima, Joao; Suinesiaputra, Avan; Young, Alistair A.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8330 LNCS Springer Verlag, 2014. p. 143-151 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8330 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Continuous spatio-temporal atlases of the asymptomatic and infarcted hearts
AU - Medrano-Gracia, Pau
AU - Cowan, Brett R.
AU - Bluemke, David A.
AU - Finn, J. Paul
AU - Kadish, Alan H.
AU - Lee, Daniel C.
AU - Lima, Joao
AU - Suinesiaputra, Avan
AU - Young, Alistair A.
PY - 2014
Y1 - 2014
N2 - Statistical descriptions of regional wall motion abnormalities of the heart are key to understanding both sub-clinical and clinical progression of dysfunction. In this paper we establish a temporal registration framework of the cardiac cycle to build a spatio-temporal atlas of 300 asymptomatic volunteers and 300 symptomatic patients with myocardial infarction. A finite-element model was customised to each person's magnetic resonance images with expert-guided semi-automatic spatial and temporal registration of model parameters. A piece-wise linear temporal registration from user-defined key frames was followed by a Fourier series temporal estimation, providing temporal continuity. All spatial and temporal data were then statistically analysed by means of principal component analysis. Results show differences in sphericity, wall thickening and mitral valve dynamics between the two groups. The modes are available from www.cardiacatlas.org. These atlases can be readily applied to abnormality detection and quantification and can also aid in anatomically constrained shape-based algorithms in automatic planning or segmentation.
AB - Statistical descriptions of regional wall motion abnormalities of the heart are key to understanding both sub-clinical and clinical progression of dysfunction. In this paper we establish a temporal registration framework of the cardiac cycle to build a spatio-temporal atlas of 300 asymptomatic volunteers and 300 symptomatic patients with myocardial infarction. A finite-element model was customised to each person's magnetic resonance images with expert-guided semi-automatic spatial and temporal registration of model parameters. A piece-wise linear temporal registration from user-defined key frames was followed by a Fourier series temporal estimation, providing temporal continuity. All spatial and temporal data were then statistically analysed by means of principal component analysis. Results show differences in sphericity, wall thickening and mitral valve dynamics between the two groups. The modes are available from www.cardiacatlas.org. These atlases can be readily applied to abnormality detection and quantification and can also aid in anatomically constrained shape-based algorithms in automatic planning or segmentation.
UR - http://www.scopus.com/inward/record.url?scp=84898855416&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84898855416&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-54268-8_17
DO - 10.1007/978-3-642-54268-8_17
M3 - Conference contribution
AN - SCOPUS:84898855416
SN - 9783642542671
VL - 8330 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 143
EP - 151
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -