Optimization and planning of radiation therapy is performed in a treatment planning system. This includes the definition of target structures to be irradiated and organs at risk to be protected, typically performed by contouring structures slice by slice in the image data. Conversions between contours and their volume representations are needed for visualizations and computations, but will however introduce a loss of information due to the sampling to a uniform voxel grid. The number of conversions performed can be large, causing errors to accumulate. The aim of this thesis is to examine volume reconstruction methods and sparse voxel representations for the purpose of volume reconstruction and representation with better accuracy than currently used algorithms in treatment planning systems. A prototype has been shown to be more accurate on contours and potentially cheaper in memory compared to the current method in RayStation in the case where contours represent non-smooth objects.