CHAMORRO-SERVENT, Judit; DUBOIS, Rémi; POTSE, Mark; COUDIÈRE, Yves

doi:10.1007/978-3-319-59448-4_28

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CHAMORRO-SERVENT, Judit
Institut de Mathématiques de Bordeaux [IMB]
IHU-LIRYC
Modélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]

DUBOIS, Rémi
Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
IHU-LIRYC

POTSE, Mark
IHU-LIRYC
Modélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]

Langue

Communication dans un congrès

Ce document a été publié dans

Lecture Notes in Computer Science, Lecture Notes in Computer Science, 9th International Conference on Functional Imaging and Modelling of the Heart - FIMH 2017, 2017-06-11, Toronto. 2017, vol. 10263, p. 289-300

Springer International Publishing

Résumé en anglais

The electrocardiographic imaging (ECGI) inverse problem is highly ill-posed and regularization is needed to stabilize the problem and to provide a unique solution. When Tikhonov regularization is used, choosing the regulariza-tion parameter is a challenging problem. Mathematically, a suitable value for this parameter needs to fulfill the Discrete Picard Condition (DPC). In this study, we propose two new methods to choose the regularization parameter for ECGI with the Tikhonov method: i) a new automatic technique based on the DPC, which we named ADPC, and ii) the U-curve method, introduced in other fields for cases where the well-known L-curve method fails or provides an over-regularized solution , and not tested yet in ECGI. We calculated the Tikhonov solution with the ADPC and U-curve parameters for in-silico data, and we compared them with the solution obtained with other automatic regularization choice methods widely used for the ECGI problem (CRESO and L-curve). ADPC provided a better correlation coefficient of the potentials in time and of the activation time (AT) maps, while less error was present in most of the cases compared to the other methods. Furthermore, we found that for in-silico spiral wave data, the L-curve method over-regularized the solution and the AT maps could not be solved for some of these cases. U-curve and ADPC provided the best solutions in these last cases.< Réduire

Mots clés en anglais

inverse problem

regularization

electrocardiographic imaging

po- tentials

Tikhonov regularization

ill-posed problems

discrete picard condition

u-curve

URI

https://oskar-bordeaux.fr/handle/20.500.12278/193629

DOI

http://dx.doi.org/10.1007/978-3-319-59448-4_28

Project ANR

L'Institut de Rythmologie et modélisation Cardiaque - ANR-10-IAHU-0004

Origine

Importé de hal

Unités de recherche

Institut de Mathématiques de Bordeaux (IMB) - UMR 5251