Direct Volume Rendering has proved to be an effective visualization method for medical data sets and has reached wide-spread clinical use. The diagnostic exploration, in essence, corresponds to a tissue classification task, which is often complex and time-consuming. Moreover, a major problem is the lack of information on the uncertainty of the classification, which can have dramatic consequences for the diagnosis. In this paper this problem is addressed by proposing animation methods to convey uncertainty in the rendering. The foundation is a probabilistic Transfer Function model which allows for direct user interaction with the classification. The rendering is animated by sampling the probability domain over time, which results in varying appearance for uncertain regions. A particularly promising application of this technique is a "sensitivity lens" applied to focus regions in the data set. The methods have been evaluated by radiologists in a study simulating the clinical task of stenosis assessment, in which the animation technique is shown to outperform traditional rendering in terms of assessment accuracy.

Collisionless quasiperpendicular shocks with magnetoacoustic Mach numbers exceeding a certain threshold are known to reflect a fraction of the upstream ion population. These reflected ions drive instabilities which, in a magnetized plasma, can give rise to electron acceleration. In the case of shocks associated with supernova remnants (SNRs), electrons energized in this way may provide a seed population for subsequent acceleration to highly relativistic energies. If the plasma is weakly magnetized, in the sense that the electron cyclotron frequency is much smaller than the electron plasma frequency ωp, a Buneman instability occurs at ωp. The nonlinear evolution of this instability is examined using particle-in-cell simulations, with initial parameters which are representative of SNR shocks. For simplicity, the magnetic field is taken to be strictly zero. It is shown that the instability saturates as a result of electrons being trapped by the wave potential. Subsequent evolution of the waves depends on the temperature of the background protons Ti and the size of the simulation box L. If Ti is comparable to the initial electron temperature Te, and L is equal to one Buneman wavelength λ0, the wave partially collapses into low frequency waves and backscattered waves at around ωp. If, on the other hand, Ti≫Te and L=λ0, two high frequency waves remain in the plasma. One of these waves, excited at a frequency slightly lower than ωp, may be a Bernstein–Greene–Kruskal mode. The other wave, excited at a frequency well above ωp, is driven by the relative streaming of trapped and untrapped electrons. In a simulation with L=4λ0, the Buneman wave collapses on a time scale consistent with the excitation of sideband instabilities. Highly energetic electrons were not observed in any of these simulations, suggesting that the Buneman instability can only produce strong electron acceleration in a magnetized plasma.

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Direct Volume Rendering (DVR) is of increasing diagnostic value in the analysis of data sets captured using the latest medical imaging modalities. The deployment of DVR in everyday clinical work, however, has so far been limited. One contributing factor is that current Transfer Function (TF) models can encode only a small fraction of the user's domain knowledge. In this paper, we use histograms of local neighborhoods to capture tissue characteristics. This allows domain knowledge on spatial relations in the data set to be integrated into the TF. As a first example, we introduce Partial Range Histograms in an automatic tissue detection scheme and present its effectiveness in a clinical evaluation. We then use local histogram analysis to perform a classification where the tissue-type certainty is treated as a second TF dimension. The result is an enhanced rendering where tissues with overlapping intensity ranges can be discerned without requiring the user to explicitly define a complex, multidimensional TF.