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Description
Aims:
The module aims to give the students an in-depth introduction to Magnetic Resonance Imaging (MRI) from the computational perspective.
Intended learning outcomes:
On successful completion of the module, a student will be able to:
- Understand, in-depth, MRI through learning and implementing, in silico, all the key components of modern MRI systems.
- Gain significant experience in software development for general scientific computing and visualisation.
Indicative content:
The following are indicative of the topics the module will typically cover:
- 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 (concept of 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).
- Fourier imaging: the signal processing perspective (fundamentals of continuous and discrete Fourier transforms; sampling theory; image reconstruction).
- Noise modelling and contrast mechanisms.
Requisites:
To be eligible to select this module as an option or elective, a student must: (1) be registered on a programme and year of study for which it is a formally available; and (2) have suitable experience with Matlab.
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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