Introductory description

This module introduces students to the different facets of image analysis required to understand the outputs of imaging systems. Imaging has been a fundamental innovation for medical diagnostics and interventions since its inception. In the last few decades it has been further popularised as an industrial tool for non-living objects given its non-destruction capability to observe internal structures. To this end it is now commonly used to assess bio-medical devices and implants for imperfections and to check it meets the required standard.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

1. Introduction to X-ray imaging systems and other modalities (1 lecture)
   a. Different systems that generate images and process
   b. X-ray generation
   c. Difference between “medical” and “industrial systems”
2. Grey values and histograms (2 lectures)
   a. X-ray interaction with matter (lambert beer law)
   b. Dosimetry
   c. Imaging outputs – grey level, histogram, image statistics
   d. The data problem, quality vs dose
3. Image Quality Metrics (3 lectures, 2hr demo session)
   a. Parameter impacts
   b. Sharpness, contrast, noise
   c. Resolution and MTF
4. Reconstruction (4 lectures. 8hrs labs)
   a. Analytical Reconstruction
   b. Iterative Reconstruction (exact and semi-covergence)
   c. Limited data, Region of Interest and advanced methods
5. Analysis (2 lectures. 8hrs labs)
   a. Visualisation
   b. Filters – denoising, edge detection, grey-scale transformation
   c. Segmentation methods and feature extraction
   d. Porosity measurement
   e. Deep learning

Includes

• lab tour
• physical introduction with imaging systems, changing parameters
• Guest speaker on forensic’s and X-ray CT

Learning outcomes

By the end of the module, students should be able to:

• Describe the imaging process from sample to image stack
• Explain the impact of parameter choices on image quality
• Calculate different image quality metrics and statistics
• Explain and implement analytical and iterative reconstruction
• Implement image analysis techniques on reconstructed data to produce outputs dependent on the task
• Appraise the impacts of limited data on reconstruction and analysis

Indicative reading list

None are required, but are supplementary material to acquire a deeper knowledge
Buzug, T (2008) Computed tomography: from photon statistics to modern cone-beam CT
Herman, G T (2009) Fundamentals of computerized tomography: image reconstruction from projections
Mamourian, A C (2013) CT imaging: practical physics, artifacts and pitfalls
Carmignato, S (2018) Industrial X-ray Computed Tomography
Hansen, P C (2021) Computed Tomography: Algorithms, Insight and Just Enough Theory
Toda, H (2021) X-ray CT: Hardware and Software Techniques

Interdisciplinary

A split of clinical and engineering focussed image reconstruction and analysis methodologies. These will require a mix of mathematical and computational approaches, while developing an image based intuition.

Subject specific skills

1. Understand how medical systems acquire and reconstruct imaging data
2. Characterise the image quality and advise how this can be improved
3. Learn how to implement analysis techniques in Matlab and Avizo
4. Interpret the outputs of X-ray radiography and CT data in medical contexts

Transferable skills

1. Formulate imaging analysis workflows for specific tasks across a variety of healthcare and industrial data
2. Apply image reconstruction (Python) to other trajectories and modalities
3. Adapt methods to interrogate image data for interdisciplinary research