About these lectures

This is a series of guest lectures given as part of the University of Bergen's Porous Media Short Course Series in the Spring of 2021 . The lectures are given online via Zoom on Jan 15, 22 and 29 at 14-16 CET.

Update: Also includes the slides of a Guest Lecture given as part of MAE 207: Finite Element Analysis for Coupled Problems at the University of California San Diego, Nov 8 2022.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Lecture schedule

I will roughly follow the following schedule each week:

• 14:00-14:45: Lecture
• 14:45-14:55: Break
• 14:55-15:00: Questions
• 15:00-15:45: Lecture
• 15:45-15:55: More questions

Please post questions in the chat. I will address questions from the chat after the break and at the end.

Prior knowledge

These lectures assume that you, the reader or audience, have

• Some knowledge of partial differential equations (PDEs) and finite element methods (FEM)
• Some knowledge of programming (Python, bash)
• No to some knowledge of brain physiology and imaging
• Knowledge of/Interest in using command-line tools

The FEniCS tutorial by Langtangen and Logg (2017) is a very good introduction to all of the above.

YouTube resources

Tutorial video snippets are posted on my mri2fem YouTube channel and include:

1. Downloading the MRI2FEM data set, viewing and extracting MR series using DICOM Browser
2. Viewing the FreeSurfer output
3. Creating a mesh of the left hemisphere using SVM-Tk from FreeSurfer surfaces
4. Simulating diffusion over the left hemisphere using FEniCS
5. Creating a mesh of the left hemisphere conforming to gray and white matter
6. Extracting the ventricular system and removing it from the left hemisphere
7. Creating a full brain mesh conforming to the gray/white matter and ventricles

Tips for lecturing via Zoom

These lectures are given via two devices: one laptop (screen) mainly used for seeing the audience and the chat; an iPad and iPencil running Goodnotes for showing slides with easy annotation, following links to online material (Zenodo, Github) including videos from YouTube.

Do a practice run of each lecture in before-hand.