Can Compton Scatter Imaging be incorporated with the Micro-x CT scanner prototype to aid in the detection of stroke?

Author: Erica Nunn

Nunn, Erica, 2022 Can Compton Scatter Imaging be incorporated with the Micro-x CT scanner prototype to aid in the detection of stroke?, Flinders University, College of Science and Engineering

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Micro-X is developing a computed tomography (CT) scanner for the detection of stroke in

ambulances to assist in the global recognised need for earlier detection and treatment of

strokes. Whilst often thought to be an unwanted component in radiological imaging,

Compton scattered radiation can provide helpful information as the process is dependent on

the scattering medium. To add a secondary image contrast to aid in the detection of stroke,

this study will investigate how the scatter produced during the CT scan can be used to the

diagnostic team’s advantage through the implementation of Compton Scatter Imaging (CSI).

To complete a thorough investigation, a literature review of the current CSI technology and

stroke pathophysiology was conducted. Differential scatter cross-sections were then

calculated to hypothesise what the Compton scatter signal would be. CSI data was then

modelled through Monte Carlo simulations performed using the GEANT4 toolkit and

statistical data analysis was conducted. The mean and standard deviations of the region of

interest selected in the output images of the Monte Carlo simulations was calculated. It was

found that using traditional X-ray systems, the difference in photon intensities at the detector

is not significantly different for ischemia and healthy tissue at 0.11% (P = 0.7428).

Comparatively, the difference between ischemic and healthy tissue in the Compton scatter

imaging system was found to be statistically significant at 2.06% (P=0.0007). It was

concluded that this preliminary research showed promising results that CSI could be

incorporated with the Micro-X CT scanner.

Keywords: Compton Scatter, X-ray, Stroke

Subject: Engineering thesis

Thesis type: Masters
Completed: 2022
School: College of Science and Engineering
Supervisor: Egon Perilli