8 Reduced-order modeling for applications to the cardiovascular system

doi:10.1515/9783110499001-008

Cited by 3 publications

(1 citation statement)

References 59 publications

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“…Reduced order modelling is also gaining interest in computer-aided surgery thanks to its capability of reducing the computational complexity and cost of numerical problems while preserving their inner physics ( Niroomandi et al., 2012a ; Niroomandi et al., 2012b ; Mena et al., 2015 ; Santo et al., 2020 ). One of the most powerful and widespread techniques to build reduced-order models (ROMs) is the reduced-basis (RB) method.…”

Section: Introductionmentioning

confidence: 99%

Machine learning and reduced order modelling for the simulation of braided stent deployment

et al. 2023

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Endoluminal reconstruction using flow diverters represents a novel paradigm for the minimally invasive treatment of intracranial aneurysms. The configuration assumed by these very dense braided stents once deployed within the parent vessel is not easily predictable and medical volumetric images alone may be insufficient to plan the treatment satisfactorily. Therefore, here we propose a fast and accurate machine learning and reduced order modelling framework, based on finite element simulations, to assist practitioners in the planning and interventional stages. It consists of a first classification step to determine a priori whether a simulation will be successful (good conformity between stent and vessel) or not from a clinical perspective, followed by a regression step that provides an approximated solution of the deployed stent configuration. The latter is achieved using a non-intrusive reduced order modelling scheme that combines the proper orthogonal decomposition algorithm and Gaussian process regression. The workflow was validated on an idealized intracranial artery with a saccular aneurysm and the effect of six geometrical and surgical parameters on the outcome of stent deployment was studied. We trained six machine learning models on a dataset of varying size and obtained classifiers with up to 95% accuracy in predicting the deployment outcome. The support vector machine model outperformed the others when considering a small dataset of 50 training cases, with an accuracy of 93% and a specificity of 97%. On the other hand, real-time predictions of the stent deployed configuration were achieved with an average validation error between predicted and high-fidelity results never greater than the spatial resolution of 3D rotational angiography, the imaging technique with the best spatial resolution (0.15 mm). Such accurate predictions can be reached even with a small database of 47 simulations: by increasing the training simulations to 147, the average prediction error is reduced to 0.07 mm. These results are promising as they demonstrate the ability of these techniques to achieve simulations within a few milliseconds while retaining the mechanical realism and predictability of the stent deployed configuration.

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Section: Introductionmentioning

confidence: 99%

Machine learning and reduced order modelling for the simulation of braided stent deployment

et al. 2023

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Towards a reduced order model for EVAR planning and intra-operative navigation

Emendi,

Kardampiki,

Støverud

et al. 2024

Medical Engineering & Physics

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Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

Regazzoni,

Pagani,

Salvador

et al. 2024

Nat Commun

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Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equations, thus demanding extensive computational resources. In contrast, data-driven approaches leverage deep learning algorithms to describe system evolution in low-dimensional spaces. We introduce an architecture, termed Latent Dynamics Network, capable of uncovering low-dimensional intrinsic dynamics in potentially non-Markovian systems. Latent Dynamics Networks automatically discover a low-dimensional manifold while learning the system dynamics, eliminating the need for training an auto-encoder and avoiding operations in the high-dimensional space. They predict the evolution, even in time-extrapolation scenarios, of space-dependent fields without relying on predetermined grids, thus enabling weight-sharing across query-points. Lightweight and easy-to-train, Latent Dynamics Networks demonstrate superior accuracy (normalized error 5 times smaller) in highly-nonlinear problems with significantly fewer trainable parameters (more than 10 times fewer) compared to state-of-the-art methods.

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8 Reduced-order modeling for applications to the cardiovascular system

Cited by 3 publications

References 59 publications

Machine learning and reduced order modelling for the simulation of braided stent deployment

Machine learning and reduced order modelling for the simulation of braided stent deployment

Towards a reduced order model for EVAR planning and intra-operative navigation

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

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