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Reduced order and sparse representations for patient-specific modeling in computational surgery

Abstract : This thesis investigates the use of model order reduction methods based on sparsity-related techniques for the development of real-time biophysical modeling. In particular, it focuses on the embedding of interactive biophysical simulation into patient-specific models of tissues and organs to enhance medical images and assist the clinician in the process of informed decision making. In this context, three fundamental bottlenecks arise. The first lies in the embedding of the shape parametrization into the parametric reduced order model to faithfully represent the patient’s anatomy. A non-intrusive approach relying on a sparse sampling of the space of anatomical features is introduced and validated. Then, we tackle the problem of data completion and image reconstruction from partial or incomplete datasets based on physical priors. The proposed solution has the potential to perform scene registration in the context of augmented reality for laparoscopy. Quasi-real-time computations are reached by using a new hyperreduction approach based on a sparsity promoting technique. Finally, the third challenge concerns the representation of biophysical systems under uncertainty of the underlying parameters. It is shown that traditional model order reduction approaches are not always successful in producing a low dimensional representation of a model, in particular in the case of electrosurgery simulation. An alternative is proposed using a metamodeling approach. To this end, we successfully extend the use of sparse regression methods to the case of systems with stochastic parameters.
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Submitted on : Thursday, March 5, 2020 - 4:23:13 PM
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  • HAL Id : tel-02499979, version 1



Nathan Lauzeral. Reduced order and sparse representations for patient-specific modeling in computational surgery. Mechanics of the solides [physics.class-ph]. École centrale de Nantes, 2019. English. ⟨NNT : 2019ECDN0062⟩. ⟨tel-02499979⟩



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