Atrial fibrillation (AF) is a major risk factor for ischemic stroke, with over 90% of thrombi in non-valvular AF patients originating from the left atrial appendage (LAA). However, current clinical risk stratification tools such as the CHA₂DS₂-VASc score do not incorporate patient-specific anatomical or hemodynamic information, limiting their predictive power. This thesis presents a comprehensive computational framework for assessing thrombus formation risk in the LAA using patient-specific imaging and computational fluid dynamics (CFD). Thirteen anatomical models were reconstructed from clinical CT datasets and subjected to transient and steady-state simulations to resolve intra-atrial flow patterns and compute key shear-derived indices: Time-Averaged Wall Shear Stress (TAWSS), Oscillatory Shear Index (OSI), Relative Residence Time (RRT), and Endothelial Cell Activation Potential (ECAP). A robust methodological pipeline was established, including segmentation, 3D reconstruction, mesh independence analysis, and sensitivity studies on blood rheology and turbulence modeling. The Carreau–Yasuda viscosity model and SST turbulence formulation were identified as optimal choices for physiological fidelity. Although LAA morphology is known to influence thrombogenic risk, this study did not include expert-guided classification of patient-specific LAA types. Therefore, no definitive conclusions were drawn regarding the relationship between LAA morphology and hemodynamic indices. Future work will focus on completing the morphological categorization to enable stratified risk analysis. Additionally, while steady-state simulations offer significant reductions in computational cost, only transient modeling accurately captured the complex, pulsatile flow phenomena critical for risk prediction. This work advances a reproducible, high-resolution framework for thrombus risk assessment in the LAA and supports the potential integration of patient-specific CFD into individualized stroke prevention strategies.
Atrial Fibrillation (AF) rappresenta un importante fattore di rischio per l’ictus ischemico, con oltre il 90% dei trombi nei pazienti con AF non valvolare che originano dalla Left Atrial Appendage (LAA). Tuttavia, gli attuali strumenti clinici di stratificazione del rischio, come il punteggio CHA₂DS₂-VASc, non tengono conto delle caratteristiche anatomiche o emodinamiche paziente-specifiche, limitandone l’accuratezza predittiva. Questa tesi propone un framework computazionale completo per valutare il rischio di formazione di trombi nella LAA, combinando imaging paziente-specifico e simulazioni di Computational Fluid Dynamics (CFD). Sono stati ricostruiti tredici modelli anatomici a partire da dati clinici di tomografia computerizzata (CT) e sottoposti a simulazioni sia transient che steady-state, al fine di risolvere i pattern di flusso intra-atriali e calcolare i principali indici emodinamici legati al gradiente di shear: Time-Averaged Wall Shear Stress (TAWSS), Oscillatory Shear Index (OSI), Relative Residence Time (RRT) ed Endothelial Cell Activation Potential (ECAP). È stata implementata una pipeline metodologica solida, comprendente segmentazione, ricostruzione 3D, analisi di indipendenza dalla mesh e studi di sensibilità sulla reologia del sangue e sulla modellazione della turbolenza. Tra i modelli testati, Carreau–Yasuda per la viscosità e SST per la turbolenza si sono dimostrati i più adatti a rappresentare realisticamente le condizioni fisiologiche. Sebbene la morfologia della LAA sia nota per influenzare il rischio trombogenico, in questo studio non è stata effettuata una classificazione morfologica convalidata da un esperto clinico. Pertanto, non è stato possibile trarre conclusioni definitive sulla relazione tra morfologia della LAA e gli indici emodinamici. L’attività futura si concentrerà sul completamento della categorizzazione morfologica per abilitare un’analisi del rischio stratificata. Inoltre, sebbene le simulazioni steady-state offrano una significativa riduzione dei costi computazionali, solo il modello transient ha permesso di catturare accuratamente i fenomeni di flusso pulsatile, fondamentali per una valutazione realistica del rischio. Questo lavoro propone un framework riproducibile e ad alta risoluzione per l’analisi del rischio trombotico nella LAA, ponendo le basi per l’integrazione della CFD paziente-specifica in strategie di prevenzione dell’ictus sempre più personalizzate.
Patient-specific hemodynamic analysis in the left atrium and the left atrial appendage: risk assessment for thrombus formation
Ghamari Arbati, Ehsan
2024/2025
Abstract
Atrial fibrillation (AF) is a major risk factor for ischemic stroke, with over 90% of thrombi in non-valvular AF patients originating from the left atrial appendage (LAA). However, current clinical risk stratification tools such as the CHA₂DS₂-VASc score do not incorporate patient-specific anatomical or hemodynamic information, limiting their predictive power. This thesis presents a comprehensive computational framework for assessing thrombus formation risk in the LAA using patient-specific imaging and computational fluid dynamics (CFD). Thirteen anatomical models were reconstructed from clinical CT datasets and subjected to transient and steady-state simulations to resolve intra-atrial flow patterns and compute key shear-derived indices: Time-Averaged Wall Shear Stress (TAWSS), Oscillatory Shear Index (OSI), Relative Residence Time (RRT), and Endothelial Cell Activation Potential (ECAP). A robust methodological pipeline was established, including segmentation, 3D reconstruction, mesh independence analysis, and sensitivity studies on blood rheology and turbulence modeling. The Carreau–Yasuda viscosity model and SST turbulence formulation were identified as optimal choices for physiological fidelity. Although LAA morphology is known to influence thrombogenic risk, this study did not include expert-guided classification of patient-specific LAA types. Therefore, no definitive conclusions were drawn regarding the relationship between LAA morphology and hemodynamic indices. Future work will focus on completing the morphological categorization to enable stratified risk analysis. Additionally, while steady-state simulations offer significant reductions in computational cost, only transient modeling accurately captured the complex, pulsatile flow phenomena critical for risk prediction. This work advances a reproducible, high-resolution framework for thrombus risk assessment in the LAA and supports the potential integration of patient-specific CFD into individualized stroke prevention strategies.File | Dimensione | Formato | |
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Master Thesis_LAA Hemodynamics_E. Ghamari Arbati_FinalVersion.pdf
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Executive_Summary_LAA Hemodynamics_E. Ghamari Arbati_FinalVersion.pdf
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https://hdl.handle.net/10589/239729