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.