Effects of left ventricle wall thickness uncertainties on cardiac mechanics

Campos, Joventino Oliveira; Sundnes, Joakim; Santos, Rodrigo Weber dos; Rocha, Bernardo Martins

doi:10.1007/s10237-019-01153-1

Cited by 21 publications

(31 citation statements)

References 37 publications

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“…A análise de sensibilidadeé usada para avaliar a contribuição de uma entrada em alguma quantidade de interesse. Para realizar esta análise, o método dosíndices Sobol foi utilizado [Campos et al 2019]. Oíndice Sobol principal S i mé usado para identificar entradas do modelo que têm alto impacto em quantidades de interesse, as quais deveriam ser medidas ou estimadas com maior precisão (Figura 1 (e)).…”

Section: Caracterização Do Problemaunclassified

“…Estas geometrias foram construídas através de um algoritmo baseado no diagrama AHA de 17 segmentos [Cerqueira et al 2002], que divide o ventrículo esquerdo em 6 segmentos para a região basal, 6 para a região medial, 4 para a região apical e 1 segmento para oápice, como apresentado na Figura 2 (a). Para criar a geometria, o algoritmo em questão utiliza uma parametrização elipsoidal através de informações da espessura da parede do ventrículo nestes 17 segmentos, além do diâmetro da base no eixo curto e o comprimentoápice-base no eixo longo, como apresentado em [Campos et al 2019].…”

Section: Caracterização Do Problemaunclassified

“…As condições de contorno neste problema foram os descolamentos prescritos na base do ventrículo e a pressão aplicada na superfície do endocárdio. Todos os pontos da base do ventrículo foram restringidos de se deslocar nas direções x e y, enquanto os pontos da base referentes ao epicárdio também foram impedidos de se deslocar na direção z, como realizado em [Campos et al 2019]. A superfície do endocárdio está sujeita a uma pressão exercida pelo sangue que entra e sai da cavidade do ventrículo.…”

Section: Caracterização Do Problemaunclassified

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Impact of uncertainties in cardiac mechanics simulations

Campos¹,

Sundnes²,

Santos³

et al. 2020

Anais Estendidos Do Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2020)

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As simulações do coração têm grande potencial de aplicações em diagnósticos e planejamento de terapias. Porém, tal uso clínico precisa conhecer a confiabilidade das predições devido a erros e incertezas nas entradas destes modelos. Os parâmetros do modelo são difíceis de ser medidos e estão associados a uma incerteza considerável. Além disso, geometrias para pacientes específicos são geradas a partir de imagens médicas envolvendo algum processamento manual, o que as tornam uma potencial fonte de incertezas. Com o objetivo de contribuir para a avaliação da confiabilidade de modelos da mecânica cardíaca, neste estudo foram realizadas análises de quantificação de incertezas e sensibilidade de simulações do ventrículo esquerdo durante o ciclo cardíaco.

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Section: Caracterização Do Problemaunclassified

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Impact of uncertainties in cardiac mechanics simulations

Campos¹,

Sundnes²,

Santos³

et al. 2020

Anais Estendidos Do Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2020)

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“…While ventricular volumes and myocyte dimensions are relatively comparable between pigs and humans, it remains to be shown to which extent the timeline of cellular and ventricular adaptation to volume overloaded pigs translates into the timeline of heart failure in humans. Finally, when creating our subject-specific ventricle models at baseline, multiple modeling aspects including the constitutive material parameters, the ventricular geometry, the myofiber architecture, and the boundary conditions, can have a potential effect on the predicted ventricular mechanics [5,10,41,49]. Quantifying the effect of each of these modeling aspects is beyond the scope of this study.…”

Section: Limitationsmentioning

confidence: 99%

Using machine learning to characterize heart failure across the scales

Peirlinck

Costabal

Sack

et al. 2019

Biomech Model Mechanobiol

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Heart failure is a progressive chronic condition in which the heart undergoes detrimental changes in structure and function across multiple scales in time and space. Multiscale models of cardiac growth can provide a patient-specific window into the progression of heart failure and guide personalized treatment planning. Yet, the predictive potential of cardiac growth models remains poorly understood. Here we quantify predictive power of a stretch-driven growth model using a chronic porcine heart failure model, subject-specific multiscale simulation, and machine learning techniques. We combine hierarchical modeling, Bayesian inference, and Gaussian process regression to quantify the uncertainty of our experimental measurements during an eight-week long study of volume-overload in six pigs. We then propagate the experimental uncertainties from the organ scale through our computational growth model and quantify the agreement between experimentally measured and computationally predicted alterations on the cellular scale. Our study suggests that stretch is the major stimulus for myocyte lengthening

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“…Moreover, since they can start from the output of any medical image segmentation algorithm, they can accurately reproduce detailed patient‐specific geometries of both healthy or pathological hearts. This is of fundamental importance since, among all the parameters considered for a cardiovascular simulation, recent studies demonstrate how the geometry has a significant impact on the outputs 63‐65 . As a drawback, these methods require triangulated surfaces of sufficient good quality, whereas thin or intersecting surfaces, typical of cardiac segmentation outputs, can be problematic.…”

Section: Introductionmentioning

confidence: 99%

Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function

Fedele

Quarteroni

2021

Numer Methods Biomed Eng

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In order to simulate the cardiac function for a patient‐specific geometry, the generation of the computational mesh is crucially important. In practice, the input is typically a set of unprocessed polygonal surfaces coming either from a template geometry or from medical images. These surfaces need ad‐hoc processing to be suitable for a volumetric mesh generation. In this work we propose a set of new algorithms and tools aiming to facilitate the mesh generation process. In particular, we focus on different aspects of a cardiac mesh generation pipeline: (1) specific polygonal surface processing for cardiac geometries, like connection of different heart chambers or segmentation outputs; (2) generation of accurate boundary tags; (3) definition of mesh‐size functions dependent on relevant geometric quantities; (4) processing and connecting together several volumetric meshes. The new algorithms—implemented in the open‐source software vmtk—can be combined with each other allowing the creation of personalized pipelines, that can be optimized for each cardiac geometry or for each aspect of the cardiac function to be modeled. Thanks to these features, the proposed tools can significantly speed‐up the mesh generation process for a large range of cardiac applications, from single‐chamber single‐physics simulations to multi‐chambers multi‐physics simulations. We detail all the proposed algorithms motivating them in the cardiac context and we highlight their flexibility by showing different examples of cardiac mesh generation pipelines.

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Effects of left ventricle wall thickness uncertainties on cardiac mechanics

Cited by 21 publications

References 37 publications

Impact of uncertainties in cardiac mechanics simulations

Impact of uncertainties in cardiac mechanics simulations

Using machine learning to characterize heart failure across the scales

Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function

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