The identified CT and MRI imaging markers of arrhythmogenicity can be viewed as entirely novel and should result in highly unconventional methods in the clinical management of patients with arrhythmias. Indeed, these introduce a non-invasive identification of the myocardium at risk based on 3D structural assessment, while conventional approaches rely on 2D electrical assessment based on invasive catheter measurements.
This opens new avenues for curative therapy in cardiac arrhythmias.
Image-processing approaches that provide comprehensive data on ablation targets pre-operatively could drastically improve the management and outcome of ablation procedures in patients. In ventricular arrhythmias, conventional catheter ablation approaches, largely devoted to the identification of ablation targets through catheter mapping are lengthy. It requires specific and expensive catheters, and is poorly standardized. It is reserved to expert centers and dependent upon the operator’s experience in the management of life-threatening arrhythmias and in the interpretation of complex electrical signals. Therefore, the current ablation method is often inaccurate and incomplete, leading to procedural failure characterized by arrythmia recurrence rates between 40-50% and the need for additional invasive interventions.
The imaging markers identified within ECSTATIC will allow non-invasive, 3D image-guided ablation targeting, dramatically improving the precision and efficacy of catheter ablation.
Towards electro-structural non-invasive assessment in cardiac arrhythmias
Important developments were made to make image-based patient-specific simulations of cardiac electrical activation compatible with clinical practice, both in term of robustness to data heterogeneity, and in term of computing time. With respect to non-invasive body surface potential mapping, there are currently no means to take advantage of imaging information on cardiac structure to improve the reconstruction of electrical maps from body surface measurements. Within ECSTATIC, novel formulations of the inverse problem of electrocardiography (ECGI) have been proposed, based on learning or novel source formulation. These are expected to dramatically improve the accuracy of the reconstructed maps, while at the same time enabling the interpretation of images and signals within a common framework. This may significantly improve the diagnostic/prognostic performance of non-imaging methods in patients with cardiac arrhythmias, while at the same time introducing a novel semiology at the interface between cardiac imaging and electrophysiology.
