BIMMI16-Medical Image and Signal Processing

Module Provider: School of Biological Sciences
Number of credits: 10 [5 ECTS credits]
Terms in which taught: Spring term module
Non-modular pre-requisites:
Modules excluded:
Current from: 2018/9

Module Convenor: Prof Ying Zheng


Type of module:

Summary module description:

This module provides an overview of the fundamental data analysis methodologies employed in medical imaging, specifically those based around functional magnetic resonance imaging (fMRI). The general linear model methodology will be introduced based on the software SPM (Statistical Parametric Mapping), which is one of the most widely used software for fMRI data analysis. Other data analysis methods include de-convolution and principal component analysis. Issues concerning fMRI experimental design and efficiency will also be introduced.


  • To understand the need for pre-processing of the fMRI data and be able to perform all stages of pre-processing in SPM.

  • To understand the principles of general linear model and use it for modelling time series data.

  • To understand the concept of design efficiency in fMRI experiment.

  • To understand the principles and applications of principal component analysis.

Assessable learning outcomes:

  • Students should be able to view, process and analyse fMRI data from human fMRI experiments using the software SPM (Statistical Parameter Mapping).

  • Students should be able to conduct statistical inferences on a single subject, a group of subjects and multiple groups using SPM.

  • Students should be able to design an efficient human fMRI experiment given a cognitive task.

  • Students should be able to describe the aims and the procedures for conducting principal component analysis.

Additional outcomes:

Outline content:

  • Pre-processing of the imaging data.

  • The general linear model and how it is used for modelling fMRI data.

  • Design efficiency for fMRI experiments.

  • Estimating haemodynamic impulse response functions

  • The principles and applications of principal component analysis.

Brief description of teaching and learning methods:

Lectures with PC labs.

Contact hours:
  Autumn Spring Summer
Lectures 10
Seminars 1
Practicals classes and workshops 4
Guided independent study 85
Total hours by term 99.00 1.00
Total hours for module 100.00

Summative Assessment Methods:
Method Percentage
Written exam 100

Summative assessment- Examinations:
Two hours

Summative assessment- Coursework and in-class tests:

Formative assessment methods:

Penalties for late submission:

Penalties for late submission on this module are in accordance with the University policy.
The following penalties will be applied to coursework which is submitted after the deadline for submission:

where the piece of work is submitted up to one calendar week after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for the piece of work will be deducted from the mark;
where the piece of work is submitted more than one calendar week after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.
(Please refer to the Undergraduate Guide to Assessment for further information:

Assessment requirements for a pass:
MEng 50% overall module mark
MSc 50% overall module mark

Reassessment arrangements:
Examination only.
One 2-hour examination paper in August/September.

Additional Costs (specified where applicable):
1) Required text books:
2) Specialist equipment or materials:
3) Specialist clothing, footwear or headgear:
4) Printing and binding:
5) Computers and devices with a particular specification:
6) Travel, accommodation and subsistence:

Last updated: 20 April 2018


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