The MPhil in Precision Medical Imaging and AI (PREMIA) is taught by the Department of Radiology in partnership with Cambridge University Hospitals Trust and the University’s advanced imaging facilities. Advanced medical imaging technologies, driven by rapid developments in precision medicine and artificial intelligence, will transform clinical diagnostics and healthcare delivery over the next decade.
This MPhil has been developed to provide a multi-disciplinary taught course that focuses on the underlying principles of medical imaging technologies—including its physics, engineering, and mathematics—and their integration with state-of-the-art AI methodologies. The course will provide directly relevant professional training and research, educating suitably-qualified applicants through formal teaching and hands-on experience to prepare them for the future of precision imaging technologies across academic research, clinical practice, and industry.
The key educational objective of this course is to provide a comprehensive, interdisciplinary foundation in the physics, mathematics, and clinical applications of precision medical imaging techniques and AI. Students will develop through practical experience with exercises encompassing data acquisition, reconstruction, and critical evaluation across medical imaging modalities.
Learning outcomes
Knowledge and Understanding
At the end of the course, the students will be expected to have:
▪ acquired a comprehensive knowledge of the physical principles underpinning medical imaging modalities, including MRI, CT, PET, SPECT, ultrasound, and emerging technologies, with a focus on instrumentation, data acquisition fundamentals, quantitative biomarkers, and clinical applications;
▪ acquired an in-depth understanding of image formation, reconstruction and processing techniques, including knowledge of imaging biomarkers, their extraction, validation, and visualisation;
▪ acquired a thorough knowledge of statistics relevant to medical imaging and AI, including diagnostic accuracy metrics, reproducibility assessments, multiple comparisons, regression, and validation frameworks;
▪ acquired expertise in machine learning methodologies tailored to precision medical imaging, including artificial neural networks, and image-based tasks like feature extraction, segmentation, denoising, classification, and prediction;
▪ acquired an appreciation of the interdisciplinary integration of imaging physics and computational analysis with clinical contexts, including patient anatomy, physiology, pathology, and evidence-based practices;
▪ acquired a familiarity with the regulatory and ethical frameworks governing medical imaging research and AI deployment;
▪ carried out practical work related to the subjects above, and produced written and computational reports of that work.
Skills and other attributes
At the end of the course, the students will be expected to have:
Intellectual Skills:
▪ critically appraised scientific literature in medical imaging and artificial intelligence, identifying strengths, limitations, biases, and clinical generalisability;
▪ designed and justified novel research hypotheses and experimental protocols that integrate imaging physics, statistical rigour, and deep learning methodologies;
▪ evaluated trade-offs between competing imaging techniques, reconstruction algorithms, and AI models in terms of diagnostic performance, computational efficiency, patient safety, and regulatory acceptability;
▪ synthesised complex, multidisciplinary information from physics, clinical radiology, statistics, and computer science to address real-world healthcare challenges.
Practical and Technical Skills
▪ conducted quantitative analysis of imaging data, including dynamic imaging analysis and statistical features;
▪ implemented, trained, and optimised machine learning models for medical imaging;
▪ performed advanced image reconstruction and processing on medical imaging datasets;
▪ conducted robust statistical analysis of imaging-derived data, including diagnostic accuracy studies, survival modelling, and multiple-comparison correction;
▪ developed end-to-end AI pipelines compliant with clinical informatics standards;
▪ produced reproducible code adhering to open-science best practice;
▪ presented their work in a seminar of peers, supervisors, and instructors.
Transferable and Professional Skills
▪ communicated technical concepts and research findings effectively through written reports, oral presentations, posters, and a viva;
▪ applied principles of research integrity and responsible use of AI throughout a research project.
Personal Attributes
▪ demonstrated intellectual curiosity and independence in precision medical imaging and AI;
▪ developed problem-solving abilities for technical challenges;
▪ developed an understanding of evidence-based decisions in healthcare.
Continuing
Progression to a PhD is not automatic. Students interested in pursuing PhDs must make an application to the relevant Department, including the Department of Radiology with the School of Clinical Medicine.
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