There have been many successful attempts to improve ray casting and ray tracing performance in the last decades. Many of these improvements form important steps towards high-performance interactive visualisation. However, growing challenges keep pace with enhancements: display resolutions skyrocket with modern technology and applications become more and more sophisticated. With the limits of Moore's law moving into sight, there have been many considerations about speeding up well-known algorithms, including a plenitude of publications on frameless rendering.

In frameless renderers sampling is not synchronised with display refreshes. That allows for both spatially and temporally varying sample rates. One basic approach simply randomises samples entirely. This increases liveliness and reduces input delay, but also leads to distorted and blurred images during movements. Dayal et al. tackle this problem by focusing samples on complex regions and by applying approximating filters to reconstruct an image from incoherent buffer content. Their frameless ray tracer vastly reduces latency and yet produces outstanding image quality. In this thesis we transfer the concepts to volume ray casting. Volume data often poses different challenges due to its lack of plains and surfaces, and its fine granularity. We experiment with both Dayal's sampling and reconstruction techniques and examine their applicability on volume data. In particular, we examine whether their adaptive sampler performs as well on volume data and which adaptions might be necessary.

Further, we develop another reconstruction filter which is designed to remove artefacts that frequently occur in our frameless renderer. Instead of assuming certain properties due to local sampling rates and colour gradients, our filter detects artefacts by their age signature in the buffer. Our filter seems to be more targeted and yet requires only constant time per pixel.