Description

In this module, students will integrate theory with practice by drawing on their prior experience of imaging modalities, and re-interpreting their knowledge of imaging within a mathematical and scientific framework.

Students will develop a level of mathematical skill sufficient to analyse complex waveforms and appreciate the statistical consequences of the information stored in an image. They will develop a knowledge of the underlying algorithms used by image manipulation tools and the extent to which the use of these affect the qualities of the image. Finally, students will learn how each and every component of the imaging chain, from presentation of patient through to the interpretive skills of the radiographer/radiologist can affect the predictive diagnostic capabilities of a method.

Module Aims

This module pre-dates the current template; refer to the description above and the following ILO sections.

Intended Learning Outcomes (ILOs)

A student who has passed this module should be able to:

• Module Specific Skills and Knowledge:
  1. show that complex waveforms can be decomposed into sinusoidal waveforms;
  2. discuss the implications of image perception for medical imaging;
  3. quantify predictive diagnostic imaging capability using various mathematical concepts;
  4. solve complex problems involving digital imaging systems;
  5. identify causes of noise in digital imaging systems and methods of minimisation;
  6. predict the performance of a digital imaging systems from its specifications;
  7. show how various image manipulation algorithms can improve the diagnostic quality of an image;
  8. discuss applications of image coregistration;
• Discipline Specific Skills and Knowledge:
  9. use mathematical skills to solve problems;
  10. use appropriate sources of information to develop own knowledge;
• Personal and Key Transferable / Employment Skills and Knowledge:
  11. manage time and prioritise workloads.

Syllabus Plan

1. Advanced mathematical skills
   1. Outline of 1D Fourier techniques: decomposition and reconstruction.
   2. Fourier techniques in 2D and 3D.
   3. Statistical concepts: distributions, variance and uncertainty.
2. Image perception
   1. Outline of visual psychophysics.
   2. Spatial and grey-scale resolution.
   3. Colour scales and colour displays.
   4. Practical considerations: brightness and contrast of display, observation distance, lighting conditions, etc...
   5. Detection of pathology: sensitivity, specificity, predictive value.
   6. Discrimination index and ROC curves.
3. Image quality
   1. Technical evaluation of images: Spatial resolution, SNR, CNR, grey-scale histograms, etc.
   2. Acceptability of images in the clinical context.
   3. Time-quality and dose-quality trade-offs.
4. Image Acquisition and Processing
   1. Analog-to-digital converters, sampling.
   2. Storage: DICOM and PACS
   3. Windowing and similar grey-scale manipulations
   4. Histogram equalisation.
   5. Spatial and frequency domain filtering
   6. Image restoration
   7. Image coregistration
   8. Volume rendering and other 3D visualisations.
5. Developments and Trends
   1. Telemedicine and Teleradiology, Health online
   2. Computerised pattern recognition
   3. Future Imaging Modalities

Learning and Teaching

Learning Activities and Teaching Methods

Assessment

Resources

The following list is offered as an indication of the type & level of information that students are expected to consult. Further guidance will be provided by the Module Instructor(s).

Core text:

• Not applicable

Supplementary texts:

• Altman D.G. (2005), Practical Statistics for Medical Research, Chapman & Hall, ISBN 1-58-488039-2 (UL: W 150 ALT)
• Gonzalez R.C. and Woods R.E. (2001), Digital Image Processing (2^nd edition), Pearson Education, ISBN 0-13-094650-8 (UL: 001.534 GON)
• Hader D.P. (2001), Image Analysis: Methods and Applications (2^nd edition), CRC Pres, ISBN 0-84-930239-0 (UL: 578 HAD)

ELE:

• http://newton.ex.ac.uk/redirects/ELE/pam3012

Further Information

Prior Knowledge Requirements

Re-assessment

Re-assessment is not available except when required by referral or deferral.

Notes: See Medical Imaging Assessment Conventions.

KIS Data Summary

Miscellaneous