[School of Engineering PhD Scholarships] Super-Resolution of 4D Flow MRI for Cardiovascular Disease using Machine Learning

About the project

About the Project Flow MRI (phase-contrast MRI magnetic resonance imaging) is a powerful and non-invasive imaging technique that measures blood flow in time and space. It provides vital insights into key metrics for cardiovascular disease diagnosis and management, such as velocity, wall shear stress, and turbulence. However, its clinical application is currently severely limited by high noise and low spatial-temporal resolution. This project aims to overcome these critical limitations by applying advanced machine learning techniques to denoise data and enhance spatial resolution. You will run high-fidelity computational fluid dynamics (CFD) simulations of blood flow through arteries and develop a cutting-edge super-resolution framework using convolutional neural networks (CNNs). The ultimate goal is to vastly improve the reliability of haemodynamic metrics derived from Flow MRI, enabling their direct use in clinic to support cardiovascular disease management. Expected Outcomes 1. Develop a machine learning framework for MR image super resolution using high fidelity CFD data. 2. Validate the developed software using MRI scans of arterial flow phantoms. 3. Collaborate directly with clinicians to maximise the translational and clinical impact of the research. Training Opportunities The student will benefit from working alongside a multidisciplinary team of clinicians and engineers at the University of Manchester. Training can be provided in computational fluid dynamics and machine learning. There will be opportunities for research visits to our collaborators in Europe. This project is ideally suited for a driven student with a strong background in data science, machine learning and/or fluid dynamics. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships . If you don’t already have an applicant account, please follow the instructions here. . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree (or international equivalent) in a discipline directly relevant to Biomedical Engineering OR any upper-second class (2:1) honours degree and a Master’s degree at merit (or international equivalent). in a discipline directly relevant to Biomedical Engineering Desirable: • Demonstrated excellence in fluid mechanics and/or data science. • Experience in programming (e.g., Python, MATLAB, C++, etc). • Strong written and verbal communication skills. This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoE

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