Examinando por Autor "Pérez, Jesús"

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A study of the sensitivity of biomechanical models of the spine for scoliosis brace design
(Elsevier, 2021) Koutras, Christos; Pérez, Jesús; Kardash, Kateryna; Otaduy, Miguel A.
Background and Objective: The development of biomechanical models of the torso and the spine opens the door to computational solutions for the design of braces for adolescent idiopathic scoliosis. However, the design of such biomechanical models faces several unknowns, such as the correct identification of relevant mechanical elements, or the required accuracy of model parameters. The objective of this study was to design a methodology for the identification of the aforementioned elements, with the purpose of creating personalized models suited for patient-specific brace design and the definition of parameter estimation criteria. Methods: We have developed a comprehensive model of the torso, including spine, ribcage and soft tissue, and we have developed computational tools for the analysis of the model parameters. With these tools, we perform an analysis of the model under typical loading conditions of scoliosis braces. Results: We present a complete sensitivity analysis of the models mechanical parameters and a comparison between a reference healthy subject and a subject suffering from scoliosis. Furthermore, we make a direct connection between error bounds on the deformation and tolerances for parameter estimation, which can guide the personalization of the model. Conclusions: Not surprisingly, the stiffness parameters that govern the lateral deformation of the spine in the frontal plane are some of the most relevant parameters, and require careful modeling. More surprisingly, their relevance is on par with the correct parameterization of the soft tissue of the torso. For scoliosis patients, but not for healthy subjects, we observe that the axial rotation of the spine also requires careful modeling.
Fast Numerical Coarsening with Local Factorizations
(Wiley, 2023) He, Zhongyun; Pérez, Jesús; Otaduy, Miguel A.
Numerical coarsening methods offer an attractive methodology for fast simulation of objects with high-resolution heterogeneity. However, they rely heavily on preprocessing, and are not suitable when objects undergo dynamic material or topology updates. We present methods that largely accelerate the two main processes of numerical coarsening, namely training data generation and the optimization of coarsening shape functions, and as a result we manage to leverage runtime numerical coarsening under local material updates. To accelerate the generation of training data, we propose a domain-decomposition solver based on substructuring that leverages local factorizations. To accelerate the computation of coarsening shape functions, we propose a decoupled optimization of smoothness and data fitting. We evaluate quantitatively the accuracy and performance of our proposed methods, and we show that they achieve accuracy comparable to the baseline, albeit with speed-ups of orders of magnitude. We also demonstrate our methods on example simulations with local material and topology updates.
High-Order Elasticity Interpolants for Microstructure Simulation
(Wiley, 2023) Chan-Lock, Antoine; Pérez, Jesús; Otaduy, Miguel Ángel
We propose a novel formulation of elastic materials based on high-order interpolants, which fits accurately complex elastic behaviors, but remains conservative. The proposed high-order interpolants can be regarded as a high-dimensional extension of radial basis functions, and they allow the interpolation of derivatives of elastic energy, in particular stress and stiffness. Given the proposed parameterization of elasticity models, we devise an algorithm to find optimal model parameters based on training data. We have tested our methodology for the homogenization of 2D microstructures, and we show that it succeeds to match complex behaviors with high accuracy.
Learning Contact Corrections for Handle-Based Subspace Dynamics
(ACM, 2021) Casas, Dan; Pérez, Jesús; Otaduy, Miguel A.; Romero, Cristian
This paper introduces a novel subspace method for the simulation of dynamic deformations. The method augments existing linear handle-based subspace formulations with nonlinear learning-based corrections parameterized by the same subspace. Together, they produce a compact nonlinear model that combines the fast dynamics and overall contact-based interaction of subspace methods, with the highly detailed deformations of learning-based methods. We propose a formulation of the model with nonlinear corrections applied on the local undeformed setting, and decoupling internal and external contact-driven corrections. We define a simple mapping of these corrections to the global setting, an efficient implementation for dynamic simulation, and a training pipeline to generate examples that efficiently cover the interaction space. Altogether, the method achieves unprecedented combination of speed and contact-driven deformation detail.
Parametric Skeletons with Reduced Soft-Tissue Deformations
(Wiley, 2021) Tapia, Javier; Romero, Cristian; Pérez, Jesús; Otaduy, Miguel A.
We present a method to augment parametric skeletal models with subspace soft-tissue deformations. We combine the benefits of data-driven skeletal models, i.e., accurate replication of contact-free static deformations, with the benefits of pure physicsbased models, i.e., skin and skeletal reaction to contact and inertial motion with two-way coupling. We succeed to do so in a highly efficient manner, thanks to a careful choice of reduced model for the subspace deformation. With our method, it is easy to design expressive reduced models with efficient yet accurate force computations, without the need for training deformation examples. We demonstrate the application of our method to parametric models of human bodies, SMPL, and hands, MANO, with interactive simulations of contact with nonlinear soft-tissue deformation and skeletal response
Soft-Tissue Simulation for Computational Planning of Orthognathic Surgery
(MDPI, 2021) Alcañiz, Patricia; Pérez, Jesús; Gutiérrez, Alessandro; Barreiro, Héctor; Villalobos, Ángel; Miraut, David; Illana, Carlos; Guiñales, Jorge; Otaduy, Miguel A.
Simulation technologies offer interesting opportunities for computer planning of orthognathic surgery. However, the methods used to date require tedious set up of simulation meshes based on patient imaging data, and they rely on complex simulation models that require long computations. In this work, we propose a modeling and simulation methodology that addresses model set up and runtime simulation in a holistic manner. We pay special attention to modeling the coupling of rigid-bone and soft-tissue components of the facial model, such that the resulting model is computationally simple yet accurate. The proposed simulation methodology has been evaluated on a cohort of 10 patients of orthognathic surgery, comparing quantitatively simulation results to post-operative scans. The results suggest that the proposed simulation methods admit the use of coarse simulation meshes, with planning computation times of less than 10 seconds in most cases, and with clinically viable accuracy.