Raman Group: Enhancing Traditional and Photon-Counting CT for the Early Diagnosis and Phenotyping of Cardiomyopathies

Adapted from Kotronias et al2 with permission. Images illustrate the potential for PCCT for assessing myocardial fibrosis and tissue abnormalities in cardiac diseases.

About the Research 

Our lab is committed to harnessing cutting-edge cardiovascular imaging (CT and CMR) and data science to advance the diagnosis, characterisation, and precision treatment of cardiomyopathies.

While cardiac MRI (CMR) remains the reference standard for tissue characterisation, access limitations and contraindications in some patients have prompted growing interest in alternative modalities. Computed tomography (CT), and particularly emerging photon-counting CT (PCCT), offer new possibilities for high-resolution myocardial imaging, coronary assessment, and tissue differentiation.

This MSc project will support our broader ambition of enhancing the diagnostic and prognostic utility of CT imaging in cardiomyopathy, especially hypertrophic, dilated, and infiltrative forms.

Traditional CT is limited in myocardial tissue characterisation, but recent advances in spectral imaging, iodine quantification, and radiomics may enable the identification of phenotypic markers previously accessible only through MRI.

The student will explore the integration of CT-derived structural, functional, and textural features including attenuation-based fibrosis surrogates, chamber geometry, wall thickness, and spectral imaging data, with other modalities such as ECG, clinical profiles, and CMR (where available) and integrating vascular signatures. Leveraging machine learning and AI, the project will aim to improve phenotype classification, identify imaging biomarkers of disease progression, and develop predictive tools for risk stratification.

Area skills and experience gained:

1)      Pre-processing and harmonising cardiac CT datasets (including conventional and photon-counting platforms).

2)      Extracting quantitative features and radiomic signatures from myocardial and coronary regions

3)      Applying supervised and unsupervised learning models for disease classification and outcome prediction.

4)      Benchmarking CT findings against CMR-derived metrics such as LGE, T1 mapping, and ECV for myocardial diseases.

5)      Exploring explainability and model generalisability across imaging platforms and patient subgroups.

6)      Identifying structural features predictive of cardiovascular outcomes (heart failure, sudden cardiac death, atrial fibrillation, stroke) among patients with myocardial diseases.

The student will work closely with a multidisciplinary team of cardiologists, radiologists, and imaging scientists, contributing to ongoing efforts to establish CT as a viable alternative or adjunct to MRI for patients with cardiomyopathy. Ultimately, this project aims to support the development of scalable, AI-assisted imaging pipelines for precision cardiovascular medicine.

This project is well suited to students or clinical research fellow with a background in biomedical engineering, physics, computer science, medical imaging, or cardiovascular sciences. Experience with Python or image analysis tools (e.g. PyRadiomics, SimpleITK) is desirable. A keen interest in translational imaging and cardiovascular disease is essential.

Interested candidates are encouraged to reach out to PI Betty.raman@cardiov.ox.ac.uk with a copy of their CV to discuss project details.

This MSc by Research project may be suitable for part-time research.

Training Opportunities 

1. Photon-counting CT acquisition – Learn to acquire and interpret high-resolution PCCT scans optimised for myocardial imaging.
2. Multicentre Registry – Getting familiar with working with the ORFAN multicentre registry for validation of novel signatures.
3. Spectral tissue characterisation – Use iodine maps and spectral data to identify fibrosis and infiltration.
4. CT-derived extracellular volume (ECV) – Estimate ECV from CT and compare with MRI-based measurements.
5. Radiomics feature extraction – Extract myocardial texture and shape features from CT images.
6. Machine learning on CT data – Apply ML models to classify cardiomyopathy subtypes using CT features.
7. CT perfusion – Explore myocardial perfusion assessment using static and dynamic CT methods.
8. Myocardial strain from CT – Investigate biomechanical strain estimation from 4D or gated CT data.
9. Cross-modality validation – Validate CT features against MRI, clinical outcomes, or genetic data.
10. CT protocol optimisation – Optimise scan parameters to balance image quality and radiation dose.
11. Coronary and myocardial integration – Combine coronary and myocardial CT data for integrated risk scores.
12. Collaboration with industry – Work with imaging physicists and vendors to advance PCCT protocols.
13. AI-assisted diagnostic tool development – Build CT-based clinical decision support tools for cardiomyopathy.
14. Ethics in advanced imaging – Gain insight into ethical and regulatory aspects of emerging imaging technologies.

Students are encouraged to attend the MRC Weatherall Institute of Molecular Medicine DPhil Course, which takes place in the autumn of their first year. Running over several days, this course helps students to develop basic research and presentation skills, as well as introducing them to a wide range of scientific techniques and principles, ensuring that students have the opportunity to build a broad-based understanding of differing research methodologies.

Generic skills training is offered through the Medical Sciences Division's Skills Training Programme. This programme offers a comprehensive range of courses covering many important areas of researcher development: knowledge and intellectual abilities, personal effectiveness, research governance and organisation, and engagement, influence, and impact. Students are actively encouraged to take advantage of the training opportunities available to them.

As well as the specific training detailed above, students will have access to a wide range of seminars and training opportunities through the many research institutes and centres based in Oxford.

The Department has a successful mentoring scheme, open to graduate students, which provides an additional possible channel for personal and professional development outside the regular supervisory framework. We hold an Athena SWAN Silver Award in recognition of our efforts to build a happy and rewarding environment where all staff and students are supported to achieve their full potential.

Additional Supervisors 

1. Charalambos Antoniades 

2. Stefan Neubauer 

Publications