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Our Doctoral Training Programme

PhD projects are strongly multidisciplinary, bridging the gap between engineering, clinical sciences and industry. Over 100 non-clinical and clinical scientists partner across UCL partner co-supervise a new type of individual, ready to transform healthcare and build the future UK industry in this area

Our Training Programme Structure

The UCL EPSRC Centre for Doctoral Training in Medical Imaging consists of a 1-year MRes followed by a 3-year PhD. Our training covers everything from modular learning to critical assessment skills and is strongly multi-disciplinary, bridging the gap between engineering, clinical sciences and industry. Each project is co-led by an imaging and clinical specialist from our pool of over 100 supervisors.

The comprehensive training programme ensures our students develop the skills to confidently produce academic research papers, deliver presentations at conferences and make significant contributions to their chosen research project.

Our CDT is administratively affiliated with the UCL Department of Medical Physics and Biomedical Engineering.

Projects and Supervisors

Every student under our CDT is allocated a unique research project which aligns to both a methodological imaging and clinical theme.

Each project we accept is chosen for its translational potential, close clinical relevance and associated areas of expertise from the primary supervisors. The primary supervisors on every project must cover both an engineering and clinical specialism to ensure each students gets a broad understanding of both the technical aspects and real-world application of their research.

The project often (but not necessarily) forms the basis for the student’s PhD research. Explore the research themes below for available projects.

The MRes Year

During your first year a combination of taught courses and a research project will see you complete your MRes, building the foundation on which you develop your thesis over the coming years. You will gain a comprehensive introduction into your own area of research, and the complimentary surrounding study areas.

The MRes Year consists of compulsory units and transferable skills (135 units) and further optional modules (45 units) (see below for current options).

The MRes year must equal 180 credits in total.

MPhil & PhD

On successful completion of your MRes, your second year marks the start of your research thesis. This is usually a development of the MRes project. You will be guided in how to form the basis of your thesis successfully and continue working on research in a dynamic academic community.

Advanced Electives are available to all students in Year 2 & 3 (MPhil & PhD). They are designed to enhance your learning and provide additional skills to compliment your research in medical imaging. Examples of advanced electives taken by past students can be found below. Each student agrees participation in their own choice of advanced electives with their supervisor and the module lead. This ensures a truly tailored approach to learning for each individual student to develop the skills they need to succeed.

MPHYG099: Research Project

Compulsory Unit

Each student is allocated a research project and two primary supervisors with expertise in their project field. The project introduces independent research and provides an opportunity to explore a topic in greater depth. The project often (but not necessarily) forms the basis for the student’s PhD research.

Module Information

Course Credits: 105 units

Term: 1, 2, 3

The module is assessed by a project report. Recommended length 15,000 words.

MPHYGB25: Critical Review of Key Papers in Biomedical Imaging/The Journal Club

Compulsory Unit

Understanding and forming an accurate opinion of a journal article is a crucial skill for research; the CDT’s flagship journal club course aims to equip students with the skills necessary to carry out a critical review of literature relevant to their field.

Students will work together through group work and discussion to develop skills for critical thinking and reviewing, and understand the motivation for paper writing whilst developing academic presentation skills.

More details can be found on the course webpage click here

Module information

Course Credits: 15

Term: 1, 2, 3

Course Examiners: Dr Andrew Melbourne, Dr Ivana Drobnjak

MPHYG001: Research Software Engineering with Python

Transferable Skills (one unit from this area)

In this course, you will move beyond programming, to learn how to construct reliable, readable, efficient research software in a collaborative environment. The emphasis is on practical techniques, tips, and technologies to effectively build and maintain complex code. This is an intensive, practical course.

Module Information

Course Credits: 15

Term: 1

Course Organiser: Matt Clarkson/James Hetherington

MPHYGB24: Programming foundations for Medical Image Analysis

Transferable Skills (one unit from this area)

A course in programming aimed at those doing medical image analysis.

  • Introduction to programming
  • MATLAB
  • C/C++
  • MATLAB graphical user interfaces
  • MATLAB for publication quality figure
  • Software Engineering
  • Floating Point Arithmetic
  • Parallel Programming and graphics cards

Module Information

Course Credits: 15

Term: One

Course Organiser: Dr Gary Zhang

MPHYG002: Research Computing with C++

Transferable Skills (one unit from this area)

In this course, we build on your knowledge of C++ to enable you to work on complex numerical codes for research. Research software needs to handle complex mathematics quickly, so the course focuses on writing software to exploit the capabilities of modern supercomputers, accelerator chips, and cloud computing facilities. But research software is also very complicated, so the course also focuses on techniques which will enable you to build software which is easy for colleagues to understand and adapt.

Module information

Course Credits: 15

Term: 2

Course Organiser: Matt Clarkson/James Hetherington

COMPGV01: Mathematical Methods Algorithms and Implementations

Recommended Optional Unit

Aims

To provide a rigorous mathematical approach: in particular to define standard notations for consistent usage in other modules. To present relevant theories and results. To develop algorithmic approach from mathematical formulation through to hardware implications.

Learning Outcomes

To understand analytical and numerical methods for image processing, graphics and image reconstruction.

Module Information

Course Credits: 15 Units

Term: 1

Taught By: Dan Stoyanov

COMPGV12: Image Processing

Recommended Optional Unit

Aims

The first half of this course introduces the digital image, describes the main characteristics of monochrome digital images, how they are represented and how they differ from graphics objects. It covers basic algorithms for image manipulation, characterisation, segmentation and feature extraction in direct space. The second half of the course proceeds to a more formal treatment of image filtering with some indication of the role and implications of Fourier space, and more advanced characterisation and feature detection techniques such as edge and corner detection, together with multiresolution methods, treatment of colour images and template matching techniques. The course provides the orportunity for students to explore a range of practical techniques, by developing their own simple processing functions either in a language such as Java and/or by using library facilities and tools such as MatLab or IDL. NOTE. This is a core course for the MSc CGVI programme, and is an option course for the MRes VEIV.

Learning Outcomes

To understand (i.e., be able to describe, analyse and reason about) how digital images are represented, manipulated, encoded and processed, with emphasis on algorithm design, implementation and performance evaluation.

Module Information

Course Credits: 15 units

Term: 1

Taught By: Lourdes Agapito (100%)

COMPGI14: Machine Vision

Recommended Optional Unit

Aims

The course addresses algorithms for automated computer vision. It focuses on building mathematical models of images and objects and using these to perform inference. Students will learn how to use these models to automatically find, segment and track objects in scenes, perform face recognition and build three-dimensional models from images.

Learning Outcomes

To be able to understand and apply a series of probabilistic models of images and objects in machine vision systems. To understand the principles behind face recognition, segmentation, image parsing, super-resolution, object recognition, tracking and 3D model building.

Module Information

Course Credits: 15 units

Term: 1

Taught By: Gabriel Brostow (100%)

COMPGI08: Graphical Models

Recommended Optional Unit

Aims

The module provides an entry into probabilistic modeling and reasoning, primarily of discrete variable systems. Very little continuous variable calculus is required, and students more familiar with discrete mathematics should find the course digestible. The emphasis is to demonstrate the potential applications of the techniques in plausible real-world scenarios related to information retrieval and analysis. Concrete challenges include questionnaire analysis, low-density parity check error correction, and collaborative filtering of Netflix data.

Learning Outcomes

Students will learn the basics of discrete graphical models, in particular inference algorithms in both singly and multiply connected structures. To cement understanding, the students must demonstrate their acquired skills by attacking several real-world challenges using the techniques acquired. The course should inspire and motivate students to the real-world applications of the theories and provide a strong enough platform for studies of more complex real-valued models.

Module Information

Course Credits: 15 units

Term: 1

Taught By: David Barber (100%)

MPHYG900: Ultrasound in Medicine

Recommended Optional Unit

The purpose of this course is to provide a complete introduction to the physics and clinical application of biomedical ultrasound. Clinically, ultrasound is already the most widely used imaging modality, and its application to therapy has grown rapidly over the last decade. Students who take this course will have a solid grounding in ultrasound to take into research or clinical work.

Module Information

Course Credits: 15 Units

Term: 1

Taught By: Ben Cox (module organiser), Bradley Treeby, Paul Beard, Andrew Plumb

MPHYG910: MRI & Biomedical Optics

Recommended Optional Unit

This module is an introduction to both magnetic resonance imaging (MRI) and Biomedical Optics as used in clinical applications, with an emphasis on the underlying physical principles. It will provide a solid foundation for students who wish to:

  • understand the physical principles of MRI and Biomedical Optics,
  • understand how medical physics can be used to improve clinical practice,
  • pursue research, or develop clinical or industrial applications, in MRI or Biomedical Optics.

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Course Credits: 15 units

Term: 2

Taught by: Karin Shmueli and John Thornton (MRI)
Ben Cox (Biomedical Optics, and module organiser)

MPHYGB07: Computer-assisted Surgery and Therapy

Recommended Optional Unit

This module focuses on algorithms and software to extract information from pre-operative and intra-operative medical images as well as other medical sensors and the use of that information to guide clinicians in their interventions such as surgery and other forms of therapy. The module is provided by members of the Centre for Medical Image Computing. Topics include:

  • Surgical Planning
  • Surgical Navigation and Instrument Tracking
  • Intra-operative Multimodal Image Fusion
  • Applications of Computer Vision Tools to Endoscopic Imaging and Robotic Surgery
  • Image-guided Radiotherapy

Module Information

Course Credits: 15 units

Term: 2

Taught by: Tom Vercauteren

MPHYGB06: Information Processing in Medical Imaging

Recommended Optional Unit

The essence of medical image computing is to derive information from medical images for clinical diagnosis, therapy or to improve our understanding of function and disease. This module focusses on algorithms and software for obtaining this information. The module is provided by members of the Centre for Medical Image Computing and is also offered as an option for students on the MSc in Computer Graphics, Vision and Imaging. Topics include:

  • Introduction: medical imaging modalities, clinical challenges, statistics relating to medical imaging (t-tests, Bland Altman, sensitivity and specificity, ROC), clinical trials.
  • Image registration: rigid, non-rigid, fluid, free-form deformation, registration theory and practice.
  • Image segmentation and classification. Statistical shape model, k-means, principal component analysis.
  • Statistical Parametric Mapping (SPM): neuroimaging practical.
  • Insight Toolkit (ITK): CMake, ITK.
  • High Performance Computing: Graphics cards (GPUs) and the NVidia CUDA language.

Module Information

Course Credits: 15 units

Term: 2

Course Organiser: Marc Modat

COMPGV08: Inverse Problems in Imaging

Recommended Optional Unit

This module aims to introduce the concepts of optimisation, and appropriate mathematical and numerical tool applications in image processing and image reconstruction. The expected learning outcomes of this module include understanding the principles of optimisation and to acquiring skills in mathematical methods and programming techniques.

Module Information

Course Credits: 15 units

Term: 2

Taught By: Simon Arridge (100%)

COMPGV17: Computational Modelling in Biomedical Imaging

Recommended Optional Unit

This course aims to expose students to the challenges and potential of computational modelling in a key application area. To explain how to use models to learn about the world. To teach parameter estimation techniques through practical examples. To familiarize students with handling real data sets.

Students successfully completing this module should be able to:

  • Understand the aims of biomedical imaging
  • Understand the advantages and limitations of model-based approaches and data-driven approaches
  • Have knowledge of standard techniques in modelling, experimental design and parameter estimation.
  • Understand the challenges of data modelling, experiment design and parameter estimation in practical situations
  • Gain knowledge of handling real-world data in computer programs.

Module Information

Course Credits: 15 units

Term: 2

Taught By: Danny Alexander, Ivana Drobnjak, Gary Zhang

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