Page 247 - Artificial Intelligence for Computational Modeling of the Heart

220 Bibliography

Rinaldi, et al., The estimation of patient-specific on Statistical Atlases and Computational Models
cardiac diastolic functions from clinical of the Heart, Springer, 2010, pp. 281–290.
measurements, Medical Image Analysis 17 (2) 201. E. Konukoglu, J. Relan, U. Cilingir, B.H. Menze, P.
(2013) 133–146. Chinchapatnam, A. Jadidi, H. Cochet, M. Hocini,
193. P. Chinchapatnam, K.S. Rhode, M. Ginks, C.A. H. Delingette, P. Jaïs, M. Haïssaguerre, N. Ayache,
Rinaldi, P. Lambiase, R. Razavi, S. Arridge, M. M. Sermesant, Efficient probabilistic model
Sermesant, Model-based imaging of cardiac personalization integrating uncertainty on data
apparent conductivity and local conduction and parameters: application to Eikonal-diffusion
velocity for diagnosis and planning of therapy, models in cardiac electrophysiology, Progress in
IEEE Transactions on Medical Imaging 27 (11) Biophysics and Molecular Biology 107 (1) (2011)
(2008) 1631–1642. 134–146.
194. J. Relan, P. Chinchapatnam, M. Sermesant, K. 202. B. Georgescu, X.S. Zhou, D. Comaniciu, A. Gupta,
Rhode, M. Ginks, H. Delingette, C.A. Rinaldi, R. Database-Guided Segmentation of Anatomical
Razavi, N. Ayache, Coupled personalization of Structures With Complex Appearance, 2005 IEEE
cardiac electrophysiology models for prediction of Computer Society Conference on Computer
ischaemic ventricular tachycardia, Interface Focus Vision and Pattern Recognition (CVPR’05), vol. 2,
1 (3) (2011) 396–407. IEEE, 2005, pp. 429–436.
195. P. Seegerer, T. Mansi, M.-P. Jolly, D. Neumann, B. 203. Z. Tu, Probabilistic boosting-tree: learning
Georgescu, A. Kamen, E. Kayvanpour, A. Amr, F. discriminative models for classification,
Sedaghat-Hamedani, J. Haas, H. Katus, B. Meder, recognition, and clustering, in: Proceedings of the
D. Comaniciu, Estimation of regional electrical IEEE International Conference on Computer
properties of the heart from 12-lead ECG and Vision, IEEE Computer Society, 2005,
images, in: Statistical Atlases and Computational pp. 1589–1596.
Models of the Heart – Imaging and Modelling 204. R.H. Anderson, Clinical anatomy of the aortic root,
Challenges, in: LNCS, vol. 8896, Springer, 2015, Heart 84 (6) (2000) 670–673.
pp. 204–212. 205. C. Stamm, R.H. Anderson, S.Y. Ho, Clinical
196. H. Delingette, F. Billet, K.C. Wong, M. Sermesant, anatomy of the normal pulmonary root compared
K. Rhode, M. Ginks, C.A. Rinaldi, R. Razavi, N. with that in isolated pulmonary valvular stenosis,
Ayache, Personalization of cardiac motion and Journal of the American College of Cardiology
contractility from images using variational data 31 (6) (1998) 1420–1425.
assimilation, IEEE Transactions on Biomedical 206. A.H.A.W.G. on Myocardial Segmentation, R. for
Engineering 59 (1) (2011) 20–24. Cardiac Imaging, M.D. Cerqueira, N.J. Weissman,
197. P.Moireau,D.Chapelle,P.LeTallec, Jointstate and V. Dilsizian, A.K. Jacobs, S. Kaul, W.K. Laskey, D.J.
parameter estimation for distributed mechanical Pennell, J.A. Rumberger, T. Ryan, et al.,
systems, Computer Methods in Applied Standardized myocardial segmentation and
Mechanics and Engineering 197 (6–8) (2008) nomenclature for tomographic imaging of the
659–677. heart: a statement for healthcare professionals
198. R.Chabiniok,P.Moireau,P.-F. Lesault, A. from the cardiac imaging committee of the council
Rahmouni, J.-F. Deux, D. Chapelle, Estimation of on clinical cardiology of the American heart
tissue contractility from cardiac cine-mri using a association, Circulation 105 (4) (2002) 539–542.
biomechanical heart model, Biomechanics and 207. V. Arsigny, P. Fillard, X. Pennec, N. Ayache,
Modeling in Mechanobiology 11 (5) (2012) Log-Euclidean metrics for fast and simple calculus
609–630. on diffusion tensors, Magnetic Resonance in
199. A. Prakosa, M. Sermesant, P. Allain, N. Villain, C.A. Medicine: An Official Journal of the International
Rinaldi, K. Rhode, R. Razavi, H. Delingette, N. Society for Magnetic Resonance in Medicine 56 (2)
Ayache, Cardiac electrophysiological activation (2006) 411–421.
pattern estimation from images using a 208. A.J. Pullan, L.K. Cheng, M.L. Buist, Mathematically
patient-specific database of synthetic image Modeling the Electrical Activity of the Heart: From
sequences, IEEE Transactions on Biomedical Cell to Body Surface and Back, World Scientific
Engineering 61 (2) (2013) 235–245. Publishing Company, 2005.
200. O. Camara, A. Pashaei, R. Sebastian, A.F. Frangi, 209. R.Clayton,O.Bernus, E. Cherry,H.Dierckx,F.
Personalization of fast conduction Purkinje system Fenton,L.Mirabella, A.Panfilov,F.Sachse, G.
in Eikonal-based electrophysiological models with Seemann, H. Zhang, Models of cardiac tissue
optical mapping data, in: International Workshop electrophysiology: progress, challenges and open