Intra-procedural updating of cardiac digital-twins for automated arrhythmia ablation target guidance using novel electroanatomical system

Aim of the PhD Project:
To develop the next generation of cardiac digital twin models which are automatically updated and enhanced using patient intra-procedural imaging and electrophysiological mapping data from the latest novel hybrid electroanatomical mapping-imaging system, facilitated by AI.
Apply our enhanced digital twin models to optimise target identification in ventricular tachycardia ablation.
Project Description:
Clinical Motivation: Ischemic heart disease (IHD) results in the formation of scar tissue in the heart. Scar-related arrhythmias, such as VT, are responsible for ~6 million sudden cardiac deaths annually. Often, the only effective therapy for patients suffering from incessant VT is highly-invasive catheter ablation. However, procedure times and complication rates are high, whilst success rates are punitively low: typically, >50% of patients will present with VT recurrence within 1-year post-procedure. Patient-specific computational simulation provides an exciting opportunity for integrating the wealth of existing imaging and electrical data for more accurate and beneficial pre-procedure and in-procedure planning, increasing success and reducing risk.

State-of-the-art: Computational cardiology will play a leading role in the future of cardiovascular medicine as it allows integrating comprehensive multi-model imaging data and functional measurements through digital twins: in-silico replicas of a patient's heart. Currently, such models are only created pre-procedurally, and then used as static entities during invasive procedures. Automated updating from on-going clinical data acquisition is currently lacking, but is important in order to refine and augment their utility, particularly in progressive diseases such as IHD.

Specific Goal: In this project, we will focus on the development of a new class of digital twins which are automatically updated from data recorded during VT ablation. Increasing the fidelity of the digital twin in this manner will allow accurate and up-to-date parameterisation of properties such as slow conduction through diastolic channels and repolarisation properties of scar border-zone tissue, both of which are known to have vital mechanistic roles in VT dynamics. Their accurate representation is essential to improve the accuracy of the in-silico model prediction. In addition to updates through functional measurements, a novel hybrid imaging-mapping system will provide imaging data which may be used to augment local structural formation within the model, potentially also identifying tissue type information (scar, healthy myocardium, border-zone), pivotal for faithful representation of VT circuits within the model.

Process: Initial anatomical models will be generated from pre-procedural imaging (MRI) data, augmented from electrophysiological recordings (ECG) to embellish functional properties such as tissue conductivity and repolarisation properties. Technological workflows for updating these initial models will then be developed, using the in-procedure functional and structural data from the hybrid imaging-mapping system. Creation of initial digital twin models, and techniques for updating, will be driven by AI, utilizing simulation data for forward training, and existing extensive experimental measurements for later tuning. Simulations of VT circuits may then be performed within the models, utilising and further developing novel methods for rapid VT simulation, allowing optimal ablation targets to be identified. Performing such simulations with both initial and updated digital twin models will facilitate a direct comparison which may be used to further optimise and guide the collection of clinical data.

Strains on the healthcare system in the UK create an acute need for finding more effective, efficient, safe, and accurate non-invasive imaging solutions for clinical decision-making, both in terms of diagnosis and prognosis, and to reduce unnecessary treatment procedures and associated costs. Medical imaging is currently undergoing a step-change facilitated through the advent of artificial intelligence (AI) techniques, in particular deep learning and statistical machine learning, the development of targeted molecular imaging probes and novel "push-button" imaging techniques. There is also the availability of low-cost imaging solutions, creating unique opportunities to improve sensitivity and specificity of treatment options leading to better patient outcome, improved clinical workflow and healthcare economics. However, a skills gap exists between these disciplines which this CDT is aiming to fill.

Consistent with our vision for the CDT in Smart Medical Imaging to train the next generation of medical imaging scientists, we will engage with the key beneficiaries of the CDT: (1) PhD students & their supervisors; (2) patient groups & their carers; (3) clinicians & healthcare providers; (4) healthcare industries; and (5) the general public. We have identified the following areas of impact resulting from the operation of the CDT.

- Academic Impact: The proposed multidisciplinary training and skills development are designed to lead to an appreciation of clinical translation of technology and generating pathways to impact in the healthcare system. Impact will be measured in terms of our students' generation of knowledge, such as their research outputs, conference presentations, awards, software, patents, as well as successful career destinations to a wide range of sectors; as well as newly stimulated academic collaborations, and the positive effect these will have on their supervisors, their career progression and added value to their research group, and the universities as a whole in attracting new academic talent at all career levels.

- Economic Impact: Our students will have high employability in a wide range of sectors thanks to their broad interdisciplinary training, transferable skills sets and exposure to industry, international labs, and the hospital environment. Healthcare providers (e.g. the NHS) will gain access to new technologies that are more precise and cost-efficient, reducing patient treatment and monitoring costs. Relevant healthcare industries (from major companies to SMEs) will benefit and ultimately profit from collaborative research with high emphasis on clinical translation and validation, and from a unique cohort of newly skilled and multidisciplinary researchers who value and understand the role of industry in developing and applying novel imaging technologies to the entire patient pathway.

- Societal Impact: Patients and their professional carers will be the ultimate beneficiaries of the new imaging technologies created by our students, and by the emerging cohort of graduated medical imaging scientists and engineers who will have a strong emphasis on patient healthcare. This will have significant societal impact in terms of health and quality of life. Clinicians will benefit from new technologies aimed at enabling more robust, accurate, and precise diagnoses, treatment and follow-up monitoring. The general public will benefit from learning about new, cutting-edge medical imaging technology, and new talent will be drawn into STEM(M) professions as a consequence, further filling the current skills gap between healthcare provision and engineering.

We have developed detailed pathways to impact activities, coordinated by a dedicated Impact & Engagement Manager, that include impact training provision, translational activities with clinicians and patient groups, industry cooperation and entrepreneurship training, international collaboration and networks, and engagement with the General Public.