Student Pages
Postgraduate course

In Vivo Imaging and Physiological Modelling

  • ECTS credits10
  • Teaching semesterSpring
  • Course codeBMED360
  • Number of semesters1
  • LanguageEnglish
  • Resources

Main content

Teaching semester

SpringĀ (minimum 2 students). This course will not be taught in the spring of 2022

Objectives and Content

Obtain theoretical and practical knowledge on functional and quantitative in vivo imaging in man and animal using magnetic resonance imaging (MRI) and computer-based image analysis. The focus is on brain imaging (perfusion, diffusion, permeability mapping) and structural and functional connectivity, but also examples from functional kidney imaging and (image-based) systems biology will be presented. A major objective is also to give insight about the importance of mathematical models and computations in analysis and understanding of complex physiological processes, and the need of cross-disciplinary collaborations.

Learning Outcomes

The students will be able to explain the physical principles of MRI and functional MRI imaging for exploration / estimation of the physiological and morphological parameters (e.g. k-space and pulse sequences, tensor estimation and eigen-decomposition, deconvolution, compartment modeling, statistical modeling, and geometric modeling). Students will also achieve practical knowledge of software and algorithms for quantitative analysis of image-based information in space and time, and be able to install and modify these programs for use in analysis and visualization of their own data. This knowledge is particularly linked to MATLAB and Python.

After completion of the course the student should be able to:

Present knowledge about

  • Basic principles of MRI.
  • Methdos for image analysis.
  • Software tools for image processing.

Skills and general competence

  • Perform simple scripting and programming.
  • Install and adapt image processing tools.
  • Communicate and translate imaging problems between (i) a biological or medical formulation and (ii) a computational & algorithmic formulation.

Required Previous Knowledge

Physics, computer science, mathematics or statistics, on bachelor level. Students with bachelor's degree in biology/life sciences, and strong interest and some previous experience in biomedical image analysis and programming could also be qualified.

Recommended Previous Knowledge

General human physiology dealing with diffusion, perfusion and microcirculation. Basic knowledge of brain anatomy and neurophysiology, and molecular and cell biology. Background in mathematics (e.g. calculus & linear algebra) and some experience with using computers in biomedical applications (e.g. statistical packages, signal processing, etc.). Access to a computer with MATLAB and Image Processing Toolbox installed, in addition to Python/Sage.

Teaching Methods and Extent of Organized Teaching

Lectures + programming lab and demonstrations. Term and project-task. Course materials, including links to software and data, are available from the course web page.

Compulsory Assignments and Attendance

Mid-term project on given topic.

Forms of Assessment

Oral presentation of personal project (20 min. + 5 min.discussion) (80% of final grading) + multiple choice (20% of final grading).

Grading Scale


Due to the outbreak of COVID-19 (corona virus) the scale of pass/fail will be utilized in the spring semester of 2021.

Subject Overlap



Reading List




Paul Tofts (Ed.) Quantitative MRI of the Brain: Measuring Changes Caused by Disease http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470014296.html

Jirsa VK, McIntosh AR (Editors) Handbook of Brain Connectivity.

http://www.springerlink.com/content/978-3-540-71462-0 .

Gonzalez RC, Woods RE. Digital Image Processing, 3rd ed., Prentice Hall, 2008


Gonzalez RC, Woods RE, Eddins SL. 2nd ed. Digital Image Processing Using MATLAB, Prentice Hall, 2009. http://www.imageprocessingplace.com

Course Evaluation

Written evaluation using electronic/digital evaluation tool.


Department of Biomedicine

Course coordinator:
Arvid Lundervold, http://uib.no/persons/Arvid.Lundervold