Introduction: Atrial fibrillation (AF) is a widespread cardiac arrhythmia that commonly affects the left atrium (LA), causing it to quiver instead of contracting effectively. This behavior is triggered by abnormal electrical impulses at a specific site in the atrial wall. Catheter ablation (CA) treatment consists of isolating this driver site by burning the surrounding tissue to restore sinus rhythm (SR). However, evidence suggests that CA can concur to the formation of blood clots by promoting coagulation near the heat source and in regions with low flow velocity and blood stagnation.Methods: A patient-specific modeling workflow was created and applied to simulate thermal-fluid dynamics in two patients pre- and post-CA. Each model was personalized based on pre- and post-CA imaging datasets. The wall motion and anatomy were derived from SSFP Cine MRI data, while the trans-valvular flow was based on Doppler ultrasound data. The temperature distribution in the blood was modeled using a modified Pennes bioheat equation implemented in a finite-element based Navier-Stokes solver. Blood particles were also classified based on their residence time in the LA using a particle-tracking algorithm.Results: SR simulations showed multiple short-lived vortices with an average blood velocity of 0.2-0.22 m/s. In contrast, AF patients presented a slower vortex and stagnant flow in the LA appendage, with the average blood velocity reduced to 0.08–0.14 m/s. Restoration of SR also increased the blood kinetic energy and the viscous dissipation due to the presence of multiple vortices. Particle tracking showed a dramatic decrease in the percentage of blood remaining in the LA for longer than one cycle after CA (65.9 vs. 43.3% in patient A and 62.2 vs. 54.8% in patient B). Maximum temperatures of 76° and 58°C were observed when CA was performed near the appendage and in a pulmonary vein, respectively.Conclusion: This computational study presents novel models to elucidate relations between catheter temperature, patient-specific atrial anatomy and blood velocity, and predict how they change from SR to AF. The models can quantify blood flow in critical regions, including residence times and temperature distribution for different catheter positions, providing a basis for quantifying stroke risks.

Atrial fibrillation (AF) is a common cardiac arrhythmia that affects 1% of the population worldwide and is associated with high levels of morbidity and mortality. Catheter ablation (CA) has become one of the first line treatments for AF, but its success rates are suboptimal, especially in the case of persistent AF. Computational approaches have shown promise in predicting the CA strategy using simulations of atrial models, as well as applying deep learning to atrial images. We propose a novel approach that combines image-based computational modelling of the atria with deep learning classifiers trained on patient-specific atrial models, which can be used to assist in CA therapy selection. Therefore, we trained a deep convolutional neural network (CNN) using a combination of (i) 122 atrial tissue images obtained by unfolding patient LGE-MRI datasets, (ii) 157 additional synthetic images derived from the patient data to enhance the training dataset, and (iii) the outcomes of 558 CA simulations to terminate several AF scenarios in the corresponding image-based atrial models. Four CNN classifiers were trained on this patient-specific dataset balanced using several techniques to predict three common CA strategies from the patient atrial images: pulmonary vein isolation (PVI), rotor-based ablation (Rotor) and fibrosis-based ablation (Fibro). The training accuracy for these classifiers ranged from 96.22 to 97.69%, while the validation accuracy was from 78.68 to 86.50%. After training, the classifiers were applied to predict CA strategies for an unseen holdout test set of atrial images, and the results were compared to outcomes of the respective image-based simulations. The highest success rate was observed in the correct prediction of the Rotor and Fibro strategies (100%), whereas the PVI class was predicted in 33.33% of the cases. In conclusion, this study provides a proof-of-concept that deep neural networks can learn from patient-specific MRI datasets and image-derived models of AF, providing a novel technology to assist in tailoring CA therapy to a patient.

Atrial fibrillation (AF) diminishes left atrial (LA) mechanical function and impairs blood flow. The latter can lead to blood stasis and increased risk of thrombus formation and stroke. We investigate this risk by studying the effects of LA flow in sinus rhythm (SR) and AF on blood coagulation dynamics.Patient-specific computational fluid dynamics (CFD) simulations were coupled with the reaction-diffusionconvection equation for thrombin. Patient LA wall motions driving the flow were reconstructed from Cine MRI data during SR and AF. 15 cardiac cycles were simulated for each patient to evaluate the likelihood of thrombus formation in the critical left atrial appendage (LAA) and right inferior pulmonary vein (RIPV) regions.The simulations showed that mean blood flow velocity in the LA cavity was substantially decreased (47%) during AF compared to SR. Specifically in LAA, mean flow velocities decreased from 0.06m/s in SR to 0.035m/s in AF, leading to enhanced thrombin generation. In the RIPV, higher mean flow velocities (0.16m/s) enabled thrombin washout through the mitral valve irrespective of SR or AF.This study proposes a novel modelling approach for quantifying the likelihood of AF-related thrombogenesis within LA and demonstrates increased risk of thrombus formation in the LAA when compared with the RIPV.

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Atrial fibrillation (AF) is responsible for deterioration of left atrial (LA) mechanical function. Sinus rhythm (SR) can be restored by terminating AF using catheter ablation (CA) therapy. CA often targets fibrotic tissue by creating scar tissue which is similar to fibrosis. We propose the use of myocardial strain to identify regions of fibrosis and understand its role in atrial mechanics.Patient-specific LA models were reconstructed from Cine and Late Gadolinium Enhanced (LGE) MRI data for two groups of patients: AF and SR pre-CA. LGE intensities represented atrial fibrosis and feature-tracking was applied on the Cine images to produce a series of 3D deforming LA meshes. The myocardial area strain (MAS) was calculated as a measure of regional contractile ability. 24 regions of clinical interest were assigned for inter-and intra-patient comparisons on the effects of CA and fibrosis on LA mechanical function.Correlation was found between low strain and dense fibrosis in the LA posterior wall for both patient groups ( ! = −0.74). MAS increased (8.9%) after CA in the AF group but decreased (10%) in the SR group.This study suggests that myocardial strain can be used as a biomarker for atrial fibrosis and also identifies the detrimental effect of intentional CA-induced damage to the LA on its mechanical function.

Background Atrial fibrillation (AF) is responsible for almost one third of all strokes, with the left atrial appendage (LAA) being the primary thromboembolic source due to localised stimulation of prothrombotic mechanisms; blood stasis, hypercoagulability and endothelial damage, known as Virchow's triad. Aim We propose an in-silico modelling pipeline that leverages clinical imaging data to mechanistically assess patient thrombogenicity for all aspects of Virchow's triad to improve the prediction and prevention of AF-related stroke. Methods Two AF patients undergoing Cine magnetic resonance imaging (sinus rhythm (SR) N=1 or AF N=1 during imaging) were selected for 3D left atrial (LA) modelling with patient-specific myocardial deformation prescribed from image-derived wall motion. Blood stasis was quantified by computational fluid dynamics (CFD) simulations of 5 cardiac cycles [1]. Generation of three key coagulation proteins; thrombin, fibrinogen and fibrin, were modelled to represent thrombus growth and hypercoagulability [2]. Regions prone to thrombogenesis by endothelial damage were identified by the oscillatory shear index (OSI), time averaged wall shear stress (TAWSS) and endothelial cell activation potential (ECAP) metrics in the LAA [3]. Results Patient-specific LA simulations enabled the assessment of differences between SR and AF conditions, quantified as numerical characteristics of each aspect of Virchow's triad. In SR, blood flow velocities were in the range 0–2.6 m/s with mean of 0.85 m/s in the LA cavity, while AF had a range between 0–1.6 m/s with mean of 0.55 m/s. The peak and mean LAA velocities in SR were 0.85 m/s and 0.14 m/s, while AF had a peak LAA velocity of 0.32 m/s and mean of 0.09 m/s, showing a 38% decrease during AF. The thrombin concentration reached its steady state at 1.26 mmol/m3 in the AF case after 4.7 seconds, while thrombin was washed away from the initial injury site in SR. After 5 cardiac cycles of thrombus growth dynamics, the peak fibrin concentration in the LAA was 1.3 mmol/m3 in SR and 3.8 mmol/m3 in AF, with the thrombus area in AF being 40% larger. Fibrinogen concentration decreased at a rate equal to fibrin generation in both SR and AF solely in the area of thrombus formation. ECAP in the LAA had peak values of 2.9 in SR and 3.7 in AF, with the location at highest risk of thrombogenesis above the LAA entrance. LAA OSI had an average value of 0.45 in AF versus 0.36 in SR, showing a 26% increase. Similarly, the TAWSS was 3.5x10–3 Pa on average over the LAA in AF compared to 1.4x10–3 Pa in SR. Conclusions Patient-specific LA models combining these three quantitative characteristics can be used to predict the higher thrombogenic risk in AF. After further validation, this novel approach for quantitative assessment of AF patient thrombogenicity based on modelling all factors in Virchow's triad can personalise and improve management of AF patients with a risk of stroke. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UK Engineering and Physical Sciences Research Council

Background Atrial fibrillation (AF) is responsible for almost one third of all strokes, with the left atrial appendage (LAA) being the primary thromboembolic source due to localised stimulation of prothrombotic mechanisms; blood stasis, hypercoagulability and endothelial damage, known as Virchow's triad. Aim We propose an in-silico modelling pipeline that leverages clinical imaging data to mechanistically assess patient thrombogenicity for all aspects of Virchow's triad to improve the prediction and prevention of AF-related stroke. Methods Two AF patients undergoing Cine magnetic resonance imaging (sinus rhythm (SR) N=1 or AF N=1 during imaging) were selected for 3D left atrial (LA) modelling with patient-specific myocardial deformation prescribed from image-derived wall motion. Blood stasis was quantified by computational fluid dynamics (CFD) simulations of 5 cardiac cycles [1]. Generation of three key coagulation proteins; thrombin, fibrinogen and fibrin, were modelled to represent thrombus growth and hypercoagulability [2]. Regions prone to thrombogenesis by endothelial damage were identified by the oscillatory shear index (OSI), time averaged wall shear stress (TAWSS) and endothelial cell activation potential (ECAP) metrics in the LAA [3]. Results Patient-specific LA simulations enabled the assessment of differences between SR and AF conditions, quantified as numerical characteristics of each aspect of Virchow's triad. In SR, blood flow velocities were in the range 0–2.6 m/s with mean of 0.85 m/s in the LA cavity, while AF had a range between 0–1.6 m/s with mean of 0.55 m/s. The peak and mean LAA velocities in SR were 0.85 m/s and 0.14 m/s, while AF had a peak LAA velocity of 0.32 m/s and mean of 0.09 m/s, showing a 38% decrease during AF. The thrombin concentration reached its steady state at 1.26 mmol/m3 in the AF case after 4.7 seconds, while thrombin was washed away from the initial injury site in SR. After 5 cardiac cycles of thrombus growth dynamics, the peak fibrin concentration in the LAA was 1.3 mmol/m3 in SR and 3.8 mmol/m3 in AF, with the thrombus area in AF being 40% larger. Fibrinogen concentration decreased at a rate equal to fibrin generation in both SR and AF solely in the area of thrombus formation. ECAP in the LAA had peak values of 2.9 in SR and 3.7 in AF, with the location at highest risk of thrombogenesis above the LAA entrance. LAA OSI had an average value of 0.45 in AF versus 0.36 in SR, showing a 26% increase. Similarly, the TAWSS was 3.5x10–3 Pa on average over the LAA in AF compared to 1.4x10–3 Pa in SR. Conclusions Patient-specific LA models combining these three quantitative characteristics can be used to predict the higher thrombogenic risk in AF. After further validation, this novel approach for quantitative assessment of AF patient thrombogenicity based on modelling all factors in Virchow's triad can personalise and improve management of AF patients with a risk of stroke. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UK Engineering and Physical Sciences Research Council

Atrial fibrillation (AF) is the most common cardiac arrhythmia and currently affects more than 650,000 people in the United Kingdom alone. Catheter ablation (CA) is the only AF treatment with a long-term curative effect as it involves destroying arrhythmogenic tissue in the atria. However, its success rate is suboptimal, approximately 50% after a 2-year follow-up, and this high AF recurrence rate warrants significant improvements. Image-guidance of CA procedures have shown clinical promise, enabling the identification of key patient anatomical and pathological (such as fibrosis) features of atrial tissue, which require ablation. However, the latter approach still suffers from a lack of functional information and the need to interpret structures in the images by a clinician. Deep learning plays an increasingly important role in biomedicine, facilitating efficient diagnosis and treatment of clinical problems. This study applies deep reinforcement learning in combination with patient imaging (to provide structural information of the atria) and image-based modelling (to provide functional information) to design patient-specific CA strategies to guide clinicians and improve treatment success rates. To achieve this, patient-specific 2D left atrial (LA) models were derived from late-gadolinium enhancement (LGE) MRI scans of AF patients and were used to simulate patient-specific AF scenarios. Then a reinforcement Q-learning algorithm was created, where an ablating agent moved around the 2D LA, applying CA lesions to terminate AF and learning through feedback imposed by a reward policy. The agent achieved 84% success rate in terminating AF during training and 72% success rate in testing. Finally, AF recurrence rate was measured by attempting to re-initiate AF in the 2D atrial models after CA with 11% recurrence showing a great improvement on the existing therapies. Thus, reinforcement Q-learning algorithms can predict successful CA strategies from patient MRI data and help to improve the patient-specific guidance of CA therapy.