scholarly journals Sensitivity Analysis of In Silico Fluid Simulations to Predict Thrombus Formation after Left Atrial Appendage Occlusion

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2304
Author(s):  
Jordi Mill ◽  
Victor Agudelo ◽  
Andy L. Olivares ◽  
Maria Isabel Pons ◽  
Etelvino Silva ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Boundary Conditions ◽  
In Silico ◽  
Left Atrial ◽  
Left Atrial Appendage ◽  
Thrombus Formation ◽  
Atrial Appendage ◽  
Patient Specific ◽  
Ground Truth Data ◽  
Left Atrial Appendage Occlusion

Atrial fibrillation (AF) is nowadays the most common human arrhythmia and it is considered a marker of an increased risk of embolic stroke. It is known that 99% of AF-related thrombi are generated in the left atrial appendage (LAA), an anatomical structure located within the left atrium (LA). Left atrial appendage occlusion (LAAO) has become a good alternative for nonvalvular AF patients with contraindications to anticoagulants. However, there is a non-negligible number of device-related thrombus (DRT) events, created next to the device surface. In silico fluid simulations can be a powerful tool to better understand the relation between LA anatomy, haemodynamics, and the process of thrombus formation. Despite the increasing literature in LA fluid modelling, a consensus has not been reached yet in the community on the optimal modelling choices and boundary conditions for generating realistic simulations. In this line, we have performed a sensitivity analysis of several boundary conditions scenarios, varying inlet/outlet and LA wall movement configurations, using patient-specific imaging data of six LAAO patients (three of them with DRT at follow-up). Mesh and cardiac cycle convergence were also analysed. The boundary conditions scenario that better predicted DRT cases had echocardiography-based velocities at the mitral valve outlet, a generic pressure wave from an AF patient at the pulmonary vein inlets, and a dynamic mesh approach for LA wall deformation, emphasizing the need for patient-specific data for realistic simulations. The obtained promising results need to be further validated with larger cohorts, ideally with ground truth data, but they already offer unique insights on thrombogenic risk in the left atria.

scholarly journals Deep Learning Framework for Real-Time Estimation of in-silico Thrombotic Risk Indices in the Left Atrial Appendage

2021 ◽  
Vol 12 ◽  
Author(s):  
Xabier Morales Ferez ◽  
Jordi Mill ◽  
Kristine Aavild Juhl ◽  
Cesar Acebes ◽  
Xavier Iriart ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Boundary Conditions ◽  
In Silico ◽  
Left Atrial ◽  
Left Atrial Appendage ◽  
Atrial Appendage ◽  
Superior Performance ◽  
Patient Specific ◽  
Cfd Simulations

Patient-specific computational fluid dynamics (CFD) simulations can provide invaluable insight into the interaction of left atrial appendage (LAA) morphology, hemodynamics, and the formation of thrombi in atrial fibrillation (AF) patients. Nonetheless, CFD solvers are notoriously time-consuming and computationally demanding, which has sparked an ever-growing body of literature aiming to develop surrogate models of fluid simulations based on neural networks. The present study aims at developing a deep learning (DL) framework capable of predicting the endothelial cell activation potential (ECAP), an in-silico index linked to the risk of thrombosis, typically derived from CFD simulations, solely from the patient-specific LAA morphology. To this end, a set of popular DL approaches were evaluated, including fully connected networks (FCN), convolutional neural networks (CNN), and geometric deep learning. While the latter directly operated over non-Euclidean domains, the FCN and CNN approaches required previous registration or 2D mapping of the input LAA mesh. First, the superior performance of the graph-based DL model was demonstrated in a dataset consisting of 256 synthetic and real LAA, where CFD simulations with simplified boundary conditions were run. Subsequently, the adaptability of the geometric DL model was further proven in a more realistic dataset of 114 cases, which included the complete patient-specific LA and CFD simulations with more complex boundary conditions. The resulting DL framework successfully predicted the overall distribution of the ECAP in both datasets, based solely on anatomical features, while reducing computational times by orders of magnitude compared to conventional CFD solvers.

Patient-specific 3D-printed Cardiac Model for Percutaneous Left Atrial Appendage Occlusion

2018 ◽  
Vol 71 (9) ◽  
pp. 762-764 ◽  
Author(s):  
Beatriz Vaquerizo ◽  
Carmen Escabias ◽  
Daniela Dubois ◽  
Gorka Gómez ◽  
Manuel Barreiro-Pérez ◽  
...  
Keyword(s):  
Left Atrial ◽  
Left Atrial Appendage ◽  
Atrial Appendage ◽  
Patient Specific ◽  
Left Atrial Appendage Occlusion ◽  
Cardiac Model ◽  
3D Printed

scholarly journals Thrombus Formation After Left Atrial Appendage Occlusion With the Amplatzer Amulet Device

2017 ◽  
Vol 3 (1) ◽  
pp. 71-75 ◽  
Author(s):  
Alexander Sedaghat ◽  
Jan-Wilko Schrickel ◽  
René Andrié ◽  
Robert Schueler ◽  
Georg Nickenig ◽  
...  
Keyword(s):  
Left Atrial ◽  
Left Atrial Appendage ◽  
Thrombus Formation ◽  
Atrial Appendage ◽  
Left Atrial Appendage Occlusion

scholarly journals Simultaneous Functional and Morphological Assessment of Left Atrial Appendage by 3D Virtual Models

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Giorgia Vivoli ◽  
Emanuele Gasparotti ◽  
Marco Rezzaghi ◽  
Elisa Cerone ◽  
Massimiliano Mariani ◽  
...  
Keyword(s):  
Left Atrial ◽  
Left Atrial Appendage ◽  
Thrombus Formation ◽  
User Interaction ◽  
Atrial Appendage ◽  
Patient Specific ◽  
3D Analysis ◽  
Current Standard ◽  
Laa Closure ◽  
Simultaneous Assessment

Purpose. The left atrial appendage (LAA) is responsible for thrombus formation in patients with atrial fibrillation. The evaluation of both LAA function and morphology is crucial for the patient characterization and the preprocedural planning of LAA closure intervention. Despite the availability of 3D imaging modalities, the current standard image analysis is based on manual delineation of the LAA contours on 2D views. Methods. In this study, a comprehensive approach based on a full 3D analysis of the tomographic dataset by surface extraction and processing (3D-S) is presented. The proposed method allows extracting functional and morphologic information in the entire cardiac cycle by minimalizing manual user interaction. The proposed methodology has been validated on ten computer tomography datasets. Results. The proposed 3D-S method was feasible in all cases. Reproducibility was improved with respect to the reference 2D manual procedure (2D-S) (coefficient of variation 2.9 vs. 4.1% for diastolic ostium area; 3.8 vs. 6.1% for systolic ostium area; 2.4 vs. 5.3% for diastolic LAA volume; 2.7 vs. 5.9% for systolic LAA volume; and 7.7 vs. 17.1% for LAA ejection fraction). No significant differences were found between 2D-S and 3D-S measurements. Conclusions. In this study, we introduced a fully 3D approach for LAA characterization, allowing the simultaneous assessment of LAA function and geometry. The proposed approach could be used to improve the patient selection and the best sizing of the device for LAA closure and to allow a patient-specific 3D printing.

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