Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A Biomechanical Model of Muscle Contraction
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Sparse Approximation of Currents for Statistics on Curves and Surfaces
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Measuring brain variability via sulcal lines registration: a diffeomorphic approach
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
International Journal of Computer Vision
Constitutive parameter estimation methodology using tagged-MRI data
FIMH'11 Proceedings of the 6th international conference on Functional imaging and modeling of the heart
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Cardiac mechanical parameter calibration based on the unscented transform
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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Patient-specific models of the heart may lead to better understanding of cardiovascular diseases and better planning of therapy. A machine-learning approach to the personalization of an electro-mechanical model of the heart, from the kinematics of the endo- and epicardium, is presented in this paper. We use 4D mathematical currents to encapsulate information about the shape and deformation of the heart. The method is largely insensitive to initialization and does not require on-line simulation of the cardiac function. In this work, we demonstrate the performance of our approach for the joint estimation of three parameters on one heart geometry. We manage to retrieve parameters such that the model matches the 4D observations with an accuracy below the voxel size, in less than three minutes of computation.