Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Computers and Biomedical Research
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Fluid-Structure Interaction Modelling of Left Ventricular Filling
ICCS '02 Proceedings of the International Conference on Computational Science-Part III
A Biomechanical Model of Muscle Contraction
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Volume modeling of myocard deformation with a spring mass system
IS4TM'03 Proceedings of the 2003 international conference on Surgery simulation and soft tissue modeling
Monodomain simulations of excitation and recovery in cardiac blocks with intramural heterogeneity
FIMH'05 Proceedings of the Third international conference on Functional Imaging and Modeling of the Heart
Model-based interpretation of cardiac beats by evolutionary algorithms: signal and model interaction
Artificial Intelligence in Medicine
FIMH '09 Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart
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Objective: Tissue Doppler imaging (TDI) is commonly used to evaluate regional ventricular contraction properties through the analysis of myocardial strain. During the clinical examination, a set of strain signals is acquired concurrently at different locations. However, the joint interpretation of these signals remains difficult. This paper proposes a model-based approach in order to assist the clinician in making an analysis of myocardial strain. Methods and materials: The proposed method couples a model of the left ventricle, which takes into account cardiac electrical, mechanical and hydraulic activities with an adapted identification algorithm, in order to obtain patient-specific model representations. The proposed model presents a tissue-level resolution, adapted to TDI strain analysis. The method is applied in order to reproduce TDI strain signals acquired from two healthy subjects and a patient presenting with dilated cardiomyopathy (DCM). Results: The comparison between simulated and experimental strains for the three subjects reflects a satisfying adaptation of the model on different strain morphologies. The mean error between real and synthesized signals is equal to 2.34% and 2.09%, for the two healthy subjects and 1.30% for the patient suffering from DCM. Identified parameters show significant electrical conduction and mechanical activation delays for the pathologic case and have shown to be useful for the localization of the failing myocardial segments, which are situated on the anterior and lateral walls of the ventricular base. Conclusion: The present study shows the feasibility of a model-based method for the analysis of TDI strain signals. The identification of delayed segments in the pathologic case produces encouraging results and may represent a way to better utilize the information included in strain signals and to improve the therapy assistance.