ONBI CDT provides a tailored training programme for graduates from a diverse range of science backgrounds (including Mathematics, Computing, Statistics, Chemistry, Biochemistry, Engineering and Physics) who wish to conduct biomedical imaging research. The programme facilitates the development of leading-edge research in all aspects of biomedical imaging technology and application, either as purely academic projects, or in cooperation with the programme's Industrial Partners.

ONBI students undertake a four-year doctoral training programme. For the first year all students train together in Oxford in the fundamentals of biomedical imaging. In particular, the first two terms are devoted to acquiring advanced theoretical and technical skills in modern biomedical imaging techniques, spanning the imaging of single cells to whole humans, along with background knowledge training in the life sciences. This training is aimed at allowing ONBI CDT students to tackle some of the Grand Challenges that have been identified as part of a wide consultation when constructing this programme.

After completion of the modules by Easter of the first year, students who have not already selected an industry-linked project undertake two extended laboratory rotations of 11 weeks duration associated with one or two of the research themes. These are similar in scope to a master's level project.

On completion of the projects students undertake their substantive doctoral research project primarily in Oxford or Nottingham, with the students based within the research groups of their principal supervisor. Since all projects must be collaborative in some way (e.g. involving different imaging scales, or different imaging modalities, or linking technology development with biomedical/clinical application), all students will have a second supervisor for their projects. Students undertaking an industry-linked project will also have an industry supervisor. Throughout this period, the ONBI CDT provides continuing training and support, tailored to each student.

Grand Challenges

1. Imaging the structure and function of complex tissues and organs - How the brain works (e.g. Human Connectome; Obama BRAIN Initiative; ageing) - Building integrative mathematical models of physiological systems (e.g. Physiome Project) - Understanding biological function in cellular systems (e.g. live cell imaging)

2. Imaging disease and response to therapy in intact organisms - Monitoring cellular and organism radiation damage and repair, (e.g. improved radiotherapy) - Imaging the delivery of stem cells in living animals (e.g. cardiac or retinal stem cell delivery) - Probing immune responses from the molecular to multi-cellular level (e.g. T cell activation)

3. Bridging resolution gaps - Integrating macroscopic and microscopic information (e.g. understanding how tissue microstructure influences macroscopic MRI signal) - Linking fluorescence microscopy to atomic structure (e.g. understanding viruses) - Imaging cellular machines at the atomic level (e.g. applying AFM to living cells)

4. Acquiring, handling and analysing very large image data sets - Automated processing, analysis and annotation of very large data sets (e.g. cell tracking) - Visualisation and statistical analysis of multi-dimensional data across modalities and scales - Automated interpretation of image data (e.g. UK Biobank; high-throughput super-resolution)

5. Imaging disease models for personalised medicine - Linking imaging phenotypes to diseases (e.g. APOE risk factors in Alzheimer's disease) - Improving image-guided therapy and theranostics (e.g. image-guided HIFU) - Developing targeted contrast agents (e.g. imaging VCAM expression for inflammation)

6. Optimizing sensitivity and contrast with minimal compromises - Improving resolution (e.g. novel super-resolution methods in live cells, aberration correction) - Improving SNR (e.g. hyper-polarized 13C magnetic resonance, brighter optical labels) - Improving speed (e.g. compressed sensing data acquisition)

7. Extracting information from images - Distinguishing signal from noise and distorting phenomena (e.g. MEG inverse problem; motion correction; new and improved de-noising and de-convolution algorithms, resting state fMRI) - Integrating across modalities (e.g. fluorescence and EM microscopy; MEG and MRI; PET/CT tumour localization with multi-photon characterization; combined PET-MRI-optical agents) - Quantitative image analysis and feature detection (e.g. robust image segmentation)


Students will undertake a series of intensive training modules during their first year. These modules cover a range of skills, providing ONBI CDT students with a uniquely broad appreciation of modern biomedical imaging, and helping them tackle the above Grand Challenges in a way that would not be possible as part of a conventional doctoral training programme. These modules include training in the mathematical foundations of imaging, the biomedical and industrial significance of what can be measured, the different types of imaging technique, the analysis of imaging data, and details of the principal imaging technologies (light microscopy, MRI, PET, SPECT, CT, Ultrasound and MEG). Specific training will also be given in research ethics, study design, advanced scientific computing, and mathematical modelling of processes using imaging data.

All courses will be taught by leading researchers, drawn primarily from the academic and industrial supervisor pool associated with ONBI CDT. The modules use an intensive, one- or two-week module structure that enables students to rapidly obtain an excellent level of core knowledge and conceptual understanding.

Modules will typically incorporate some or all of the following; directed reading; interactive lectures; problem solving classes; laboratory-based experimental work; programming practicals; workshops; journal clubs; student-led presentations; and discussion groups. They will also make use of the diverse backgrounds of the students as they learn together and from each other.

Monitoring and Assessment

All modules will involve some aspect of formal assessment. This takes a wide variety of forms depending on the module, but in most cases will involve problem-based assessments. Assessment guidelines have been developed to ensure that students are assessed on the progress relative to their background within any given module.