UCL Summer School: Diffusion MRI

Welcome to the University College London and Centre for Medical Imaging and Computing (CMIC) summer school! This course is focused on magnetic resonance imaging (MRI), more specifically diffusion MRI. By the end of this week, you should be able to understand how diffusion MRI works, how to perform tractography, limitations within this field, and the future directions the field might be heading in.

Who are we?

Ellie Thompson (She/her) studied Physics for her undergraduate degree, where she first became interested in brain imaging. She completed a PhD at the University of Nottingham, using tractography to study white matter development in babies, before joining UCL as a postdoc in 2021. She is currently working on new ways of measuring brain connectivity to help model the spread of Alzheimer's disease pathology.

Tiantian He (She/her) studied Statistics for her undergraduate degree. She then came to the UK and completed MSc Scientific and Data Intensive Computing at UCL. She’s now doing her PhD at the Centre for Medical Image Computing at UCL, constructing a new Bayesian inference framework to deal with existing limitations in computational models of neurodegenerative diseases.


We have designed this course to accessible and engaging to all audiences: there will be optional coding tasks along the way for those of you who want a challenge and there will also be the answers available for those who want to see how the magic is done. The structure of this classs will be the following:

Day One: Diffusion

  1. Presentation: Introduction to the History of Diffusion MRI

  2. Test: The Diffusion Tensor

  3. Practical: Diffusion Tensor Estimation

Day Two: Tractography

  1. Presentation: Introduction to Tractography

  2. Test: Basics of Tractography

  3. Practical: Deterministic Tractography Algorithm

Day Three: Advanced Tractography

  1. Workshop: Advantages and Disadvantages of DTI

  2. MRtrix3: Diffusion processing & Tractography and Connectome Tutorial

  3. Practical: Comparing algorithms

Day Four: Deep Learning

  1. Presentation: Introduction to Machine Learning

  2. Practical: TractSeg


We would recommend following the tutorial with the data we have provided. This is because we have used a fake diffusion scan (known as a phantom) which will be quick to run on everyone's computers. Please download it using this link.

We hope you have fun! If you have any questions, please contact us using our emails below:




We would like to thank Anna Schroder and Lawrence Binding who created the original version of this course, which can be found at https://diffusion-tractography.github.io .