SPECT imaging and Automatic Classification Methods in Movement Disorders
Author(s)
Towey, David John
Type
Thesis
Abstract
This work investigates neuroimaging as applied to movement disorders by the
use of radionuclide imaging techniques. There are two focuses in this work:
1) The optimisation of the SPECT imaging process including acquisition and
image reconstruction.
2) The development and optimisation of automated analysis techniques
The first part has included practical measurements of camera performance using
a range of phantoms. Filtered back projection and iterative methods of image
reconstruction were compared and optimised. Compensation methods for
attenuation and scatter are assessed.
Iterative methods are shown to improve image quality over filtered back
projection for a range of image quality indexes. Quantitative improvements are
shown when attenuation and scatter compensation techniques are applied, but
at the expense of increased noise.
The clinical acquisition and processing procedures were adjusted accordingly.
A large database of clinical studies was used to compare commercially available
DaTSCAN quantification software programs.
A novel automatic analysis technique was then developed by combining
Principal Component Analysis (PCA) and machine learning techniques (including
Support Vector Machines, and Naive Bayes).
The accuracy of the various classification methods under different conditions is
investigated and discussed.
The thesis concludes that the described method can allow automatic
classification of clinical images with equal or greater accuracy to that of
commercially available systems.
use of radionuclide imaging techniques. There are two focuses in this work:
1) The optimisation of the SPECT imaging process including acquisition and
image reconstruction.
2) The development and optimisation of automated analysis techniques
The first part has included practical measurements of camera performance using
a range of phantoms. Filtered back projection and iterative methods of image
reconstruction were compared and optimised. Compensation methods for
attenuation and scatter are assessed.
Iterative methods are shown to improve image quality over filtered back
projection for a range of image quality indexes. Quantitative improvements are
shown when attenuation and scatter compensation techniques are applied, but
at the expense of increased noise.
The clinical acquisition and processing procedures were adjusted accordingly.
A large database of clinical studies was used to compare commercially available
DaTSCAN quantification software programs.
A novel automatic analysis technique was then developed by combining
Principal Component Analysis (PCA) and machine learning techniques (including
Support Vector Machines, and Naive Bayes).
The accuracy of the various classification methods under different conditions is
investigated and discussed.
The thesis concludes that the described method can allow automatic
classification of clinical images with equal or greater accuracy to that of
commercially available systems.
Date Issued
2013
Date Awarded
2013-05
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Nijran, Kuldip
Bain, Peter
Blake, Glen
Publisher Department
Institute of Clinical Sciences
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)