Efficient Medical Volume Visualization : An Approach Based on Domain Knowledge

Direct Volume Rendering (DVR) is a visualization technique that has proved to be a very powerful tool in many scientific visualization applications. Diagnostic medical imaging is one such domain where DVR provides unprecedented possibilities for analysis of complex cases and highly efficient workflow. Due to limitations in conventional DVR methods and tools the full potential of DVR in the clinical environment has not been reached. This thesis presents methods addressing four major challenges for DVR in clinical use. The foundation of all technical methods is the domain knowledge of the medical professional. The first challenge is the increasingly large data sets routinely produced in medical imaging today. To this end a multiresolution DVR pipeline is proposed, which dynamically prioritizes data according to the actual impact on the quality of rendered image to be reviewed. Using this prioritization the system can reduce the data requirements throughout the pipeline and provide both high performance and high visual quality. Another problem addressed is how to achieve simple yet powerful interactive tissue classification in DVR. The methods presented define additional attributes that effectively capture readily available medical knowledge. The third area covered is tissue detection, which is also important to solve in order to improve efficiency and consistency of diagnostic image review. Histogram-based techniques that exploit spatial relations in the data to achieve accurate and robust tissue detection are presented in this thesis. The final challenge is uncertainty visualization, which is very pertinent in clinical work for patient safety reasons. An animation method has been developed that automatically conveys feasible alternative renderings. The basis of this method is a probabilistic interpretation of the visualization parameters. Several clinically relevant evaluations of the developed techniques have been performed demonstrating their usefulness. Although there is a clear focus on DVR and medical imaging, most of the methods provide similar benefits also for other visualization techniques and application domains.