This thesis investigates the requirements and techniques for effective visualisation of MARS spectral CT datasets. It extends traditional CT data visualisation methods and develops novel tools and graphical user interfaces tailored to spectral CT data. These tools have been used to visualise and analyse a range of datasets acquired during pre-clinical research with the MARS molecular imaging system. The results demonstrate that they are capable of meeting the goals of current pre-clinical studies. Spectral computed tomography (spectral CT) is a novel medical imaging technology that measures several energy ranges (energy bins) of the x-ray spectrum to produce multiple CT datasets. The primary advantage of spectral CT over singleand dual-energy CT is improved material discrimination, which allows for precise targeting of multiple materials of interest in a single scan. The MARS molecular imaging system is a small specimen spectral CT scanner capable of acquiring up to eight energy bins simultaneously. Energy bin data can be processed to identify and quantify multiple materials. This thesis is the first work to study the visualisation of spectral CT material data in detail, as no prior research into the process or the tools for performing this task could be identified. This thesis examines the use of various visualisation techniques for displaying the results of pre-clinical research with the MARS molecular imaging system. These techniques include 2D slice visualisation, direct volume rendering, magic lenses, and threshold intensity projection. Particular attention is paid to the fusion of energy and material information during visualisation, and a novel transfer function editor is developed for working with both energy, and material data comprising a MARS spectral CT dataset. This thesis also describes a suite of tools for improving the visualisation of MARS spectral CT data. This includes tools for reducing the effects of occlusion during 3D rendering, for editing volume data during visualisation, and measurement functions tailored to spectral CT data analysis.