Image-based computational system for guiding ablation treatment of atrial arrhythmias
Project reference: DTP_SIE_03
First supervisor: Oleg Aslanidi
Second supervisor: David Nordsletten
Project summary: Atrial fibrillation (AF) the most common sustained cardiac arrhythmia that affects about 33 million people worldwide. The disease is associated with substantial levels of morbidity and mortality, high risks of developing heart failure and stroke, and therefore extremely high rates of patient hospitalizations. The project will build up on recent advanced in patient MR imaging and image-based modelling of AF, to create a workflow for the identification of the most probable arrhythmogenic locations in the atria of a given patient, and to design a minimally-invasive intervention that efficiently restores the electro-mechanical function.
Project description: The economic burden of AF amounts to about 1% of total healthcare costs in the UK. Even advanced therapies, such as catheter ablation (CA), are highly empirical and have poor long-term outcomes, with about half of AF patients returning for the repeated procedures, which further contributes to the healthcare burden. Moreover, invasive CA therapy often results in extensive damage of atrial tissue, effectively restoring atrial electrical function at the expense of impairing its mechanical function.
We have recently demonstrated that magnetic resonance imaging (MRI) and image-based 3D computational modelling can help: (i) Identify locations of electrical drivers sustaining AF in relation to image-derived atrial fibrosis, and (ii) evaluate atrial mechanical properties due to the presence of fibrosis and AF drivers, and how these change after CA. The current project will advance this research from the stage of mechanistic understanding AF electro-mechanics to the creation of patient-specific approaches for minimally-invasive CA therapy.
Main objectives of the current project are to:
Simulate patient-specific probability maps for the locations of AF drivers – target maps for CA therapy;
Simulate minimally-invasive CA procedures that terminate AF with minimal damage to atrial mechanical function;
Validate the model predictions using patient electro-anatomical mapping and imaging data and known clinical outcomes;
Train deep learning algorithms on patient-specific MRI datasets and image-derived AF models to optimise the CA procedures;
Develop a CA guidance system that integrates patient MRI data with the the predicted ablation lesion patterns to inform therapy.
We will use MRI data from 100 AF patients. An efficient workflow has been developed in our group to reconstruct 3D atrial geometry and fibrosis distribution from LGE MRI, and to create image-based 3D atrial models. The workflow will be applied to each of 100 cases, and the resulting models will be used to simulate multiple AF scenarios. The most likely special locations of AF drivers across scenarios will be recorded to create a patient-specific CA target map. The targets will be used to simulate optimal CA scenarios for each patient, which aims to minimise the procedure time and the damage to atrial mechanical function. The latter will be facilitated by 3D cardiac mechanics simulations using our CHeart platform. The simulated electrical activations and CA patterns will be validated against electro-anatomical mapping data from the same patients. Advanced deep learning algorithm will then be trained using all the patient imaging and image-based modelling data to provide an efficient tool for predicting patient-specific CA lesions.
The image-based computational workflow will be integrated with commercial systems for therapy guidance developed by our two industry partners, Galgo Medical and Abbott. Galgo’s ADAS software, which will be used as a framework, is fully compatible with Abbott’s EnSite Precision™ system used for cardiac intervention guidance in the clinics worldwide. Hence, novel technology developed in this project will be integrated with this advanced system, enabling its delivery at the point of care for the benefit of clinicians and patients.