COMPGV20 - Computational MRI

This database contains the 2017-18 versions of syllabuses. Syllabuses from the 2016-17 session are available here.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

PrerequisitesFamiliarity with Matlab
Taught ByGary Zhang (65%)
Ivana Drobnjak (35%)
AimsThe module aims to give the students an in-depth introduction to MRI from the computational perspective. It is designed to be a core module for the students enrolled in the CDT in Medical Imaging who want to pursue a research project related to MRI.
Learning Outcomes

Students will develop an in-depth understanding of MRI through learning and implementing, in silico, all the key components of modern MRI systems.


  • Introduction to magnetic resonance imaging
  • Classical description of a magnetic field acting on a single nucleus
    • equation of motion
    • rotating frame of reference
    • concept of magnetic resonance
  • Macroscopic magnetization
    • relaxation
    • the Bloch equation
  • Introduction to signal detection and acquisition
    • free induction decay
    • spin echoes
    • inversion recovery
    • spectroscopy
  • Fourier imaging
    • the MR physics perspective
      • k-space
      • gradient echoes
      • slice excitation
    • the signal processing perspective
      • fundamentals of continuous and discrete Fourier transforms
      • sampling theory
      • image reconstruction
  • Noise modelling and contrast mechanisms
  • Radiofrequency pulses
  • Magnetic field inhomogeneity effects
  • Fast imaging
    • Echo planar imaging
  • Parallel imaging
  • Imaging applications
    • water/fat separation
    • spin density/T1/T2 quantification
    • diffusion
    • susceptibility
    • BOLD

Method of Instruction

3 hours of lectures and 2 hour problem class/tutorial per week.

The course content will be based on an established textbook (Magnetic Resonance Imaging – Physical Principles and Sequence Design) that is available online via UCL library subscription, making it readily accessible to the students.

The recommended readings (drawn primarily from the textbook above), assignments, and project works will be provided to the students via Moodle, with a tutor led forum to respond to student questions.

The weekly problem class/tutorial provides the opportunity for the students to raise questions and obtain support.


The course has the following assessment components:

  • Coursework (75%)
  • Project (25%)

To pass this course, students must:

  • Obtain an overall pass mark of 50% for all sections combined.


Reading list available via the UCL Library catalogue.