PhD Research Studentships
Virtual Populations of Human Hearts for In-Silico Trials of Atrial Fibrillation Treatment Approaches
| Supervisors: | Caroline RONEY and Alexander ZOLOTAREV |
| Apply by: | 29 January 2025 |
| Start in: | September (Semester 1) |
Description
Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting over 37 million people worldwide. Treatment outcomes for AF patients are suboptimal because it is challenging to predict patient response to different treatments, making it difficult to tailor therapies to an individual patient. One solution is to use cardiac digital twins, combining biophysical simulations and deep learning approaches to develop therapies that are personalised to a patient. Unfortunately, the limited number of relevant clinical datasets and concerns about data privacy can hinder the predictive accuracy of these models. This challenge can be addressed by conducting extensive in-silico trials using virtual patients.
Specifically, high-quality virtual populations of human hearts are crucial for various applications, including biophysical simulations of cardiac electrical activity, data augmentation, and medical device development. The main challenge of this PhD proposal is to create realistic 3D atrial representations that can capture patient variability in atrial anatomy and fibrosis for successful biophysical simulations. Generating these meshes at scale can be evaluated as a new augmentation technique for our pipeline for AF treatment optimization. Training on the synthesized population could enhance the predictive accuracy for AF treatment outcome compared to training on smaller clinical datasets.
This PhD will focus on the following objectives:
O1. Create algorithms for 3D atrial mesh generation and train it on open-source MRI and CT datasets;
O2. Generate a cohort of artificial atrial meshes and run biophysical simulations on this cohort to obtain cardiac digital twins;
O3. Check the ability of artificial datasets to effectively augment the biophysical simulations for predicting personalized AF treatment such as anti-arrhythmic drugs or the optimal ablation strategy.
You'll be a part of the Personalised Cardiac Modelling Lab (https://pcmlab.co.uk) in the Digital Environment Research Institute, supervised by a team specialising in cardiac digital twins and synthetic data: Dr Caroline Roney, Dr Alexander Zolotarev, Prof Greg Slabaugh, Prof Venet Osmani (https://venetosmani.com/research/).
Funding
Funded by: China Scholarship CouncilCandidate will need to secure a CSC scholarship.
Under the scheme, Queen Mary will provide scholarships to cover all tuition fees, whilst the CSC will provide living expenses and one return flight ticket to successful applicants.
Eligibility
- The minimum requirement for this studentship opportunity is a good honours degree (minimum 2(i) honours or equivalent) or MSc/MRes in a relevant discipline.
- If English is not your first language, you will require a valid English certificate equivalent to IELTS 6.5+ overall with a minimum score of minimum score of 6.0 in each of Writing, Listening, Reading and Speaking).
- Candidates are expected to start in September (Semester 1).
Contact
For informal enquiries about this opportunity, please contact Caroline RONEY or Alexander ZOLOTAREV.
Apply
Start an application for this studentship and for entry onto the PhD Medical Engineering full-time programme (Semester 1 / September start):
Please be sure to quote the reference "SEMS-PHD-622" to associate your application with this studentship opportunity.
| Related website: | https://www.csc.edu.cn/ | |
| SEMS Research Centre: | ||
| Keywords: | Artificial Intelligence, Computer Vision, Machine Learning, Bioengineering, Biomedical Engineering, Computational Mathematics, Mathematical Modelling, Biomechanics, Cardiology, Computational Physics |