Atrial fibrillation (AF) is a major cause of morbidity and mortality, contributing significantly to global health expenditure. Although catheter ablation has emerged as a promising treatment strategy for patients who suffer from AF, approximately 30% of procedures fail to terminate the arrhythmia. Clinical evidence demonstrates that the extent of left atrial (LA) fibrosis is correlated with the success of treatment outcomes in patients with AF1,2
, suggesting that information regarding the extent and morphology of fibrosis in the patient’s atrium may be a valuable tool in guiding catheter ablation techniques. Recent advances in in vivo imaging techniques allow for the visualization of fibrosis throughout the patient’s atria, providing impetus for image-based computational modeling to accurately represent both the geometry of the patient’s atria and the specific fibrosis distribution. The ability to develop such a customized atrial model has important implications for personalized AF treatment, a long-term goal of the clinical community, which could significantly improve the success rates of ablation procedures.
Personalized computational models of the heart allow for the integration of insight obtained from cutting-edge developments in various realms of cardiac research. The ability to replicate the exact geometry of a living patient’s heart, and the structural remodeling therein, capitalizes on recent advances in late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) methodology, which uses gadolinium contrast to localize and quantify the degree of fibrosis and structural remodeling.3
Experimental findings obtained in cellular-, tissue-, and organ-level studies represent crucial inputs to the computational model. Clinical knowledge of the patient’s arrhythmia etiology, disease progression, and therapy outcome allows for the validation of patient-specific heart models. With the methodology for developing personalized computational models of the heart well underway, better understanding of the mechanisms underlying cardiac disease and arrhythmias will enable computational approaches to provide a powerful new tool in the clinic: a novel way to predict disease and guide treatments.
In this study, we use state-of-the-art techniques to develop the methodology for generation of patient-specific atrial models with accurate fibrotic lesion distribution. MR images were obtained from a patient suffering from persistent AF. Following image segmentation and interpolation, patient geometries were reconstructed. Atrial fiber orientation was estimated using a novel image-based method whereby the patient atrial geometry is registered with atlas atria whose geometry and fiber orientations are known. As one possible scenario of fibrotic remodeling, fibrotic lesions are modeled to contain non-conducting collagenous septa, connexin redistribution, and myofibroblast proliferation. Finally, we show a proof-of-concept simulation using a model of an AF patient’s atrium, whereby pulmonary vein ectopy, indeed, resulted in arrhythmia. This simulation demonstrates that the methodology is fully operational.