Approximated and User Steerable tSNE for Progressive Visual Analytics

Progressive Visual Analytics aims at improving the interactivity in existing analytics techniques by means of visualization as well as interaction with intermediate results. One key method for data analysis is dimensionality reduction, for example, to produce 2D embeddings that can be visualized and analyzed efficiently. t-Distributed Stochastic Neighbor Embedding (tSNE) is a well-suited technique for the visualization of several high-dimensional data. tSNE can create meaningful intermediate results but suffers from a slow initialization that constrains its application in Progressive Visual Analytics. We introduce a controllable tSNE approximation (A-tSNE), which trades off speed and accuracy, to enable interactive data exploration. We offer real-time visualization techniques, including a density-based solution and a Magic Lens to inspect the degree of approximation. With this feedback, the user can decide on local refinements and steer the approximation level during the analysis. We demonstrate our technique with several datasets, in a real-world research scenario and for the real-time analysis of high-dimensional streams to illustrate its effectiveness for interactive data analysis

Stochastic neighbor embedding as a tool for visualizing the encoding capability of magnetic resonance fingerprinting dictionaries

Objective To visualize the encoding capability of magnetic resonance fingerprinting (MRF) dictionaries. Materials and methods High-dimensional MRF dictionaries were simulated and embedded into a lower-dimensional space using t-distributed stochastic neighbor embedding (t-SNE). The embeddings were visualized via colors as a surrogate for location in low-dimensional space. First, we illustrate this technique on three different MRF sequences. We then compare the resulting embeddings and the color-coded dictionary maps to these obtained with a singular value decomposition (SVD) dimensionality reduction technique. We validate the t-SNE approach with measures based on existing quantitative measures of encoding capability using the Euclidean distance. Finally, we use t-SNE to visualize MRF sequences resulting from an MRF sequence optimization algorithm. Results t-SNE was able to show clear differences between the color-coded dictionary maps of three MRF sequences. SVD showed smaller differences between different sequences. These findings were confirmed by quantitative measures of encoding. t-SNE was also able to visualize differences in encoding capability between subsequent iterations of an MRF sequence optimization algorithm. Discussion This visualization approach enables comparison of the encoding capability of different MRF sequences. This technique can be used as a confirmation tool in MRF sequence optimization.Radiolog

Analysis and compensation for the effect of the catheter position on image intensities in intravascular optical coherence tomography

Cardiovascular Aspects of Radiolog

The popular music heritage of the Dutch pirates: illegal radio and cultural identity

This article explores how cultural identities are negotiated in relation to the heritage of illegal radio in the Netherlands. The term ‘pirate radio’ commonly refers to the offshore radio stations that were broadcasting during the 1960s. These stations introduced commercial radio and popular music genres like beat music, which were not played by public broadcasters at the time. In their wake, land-based pirates began broadcasting for local audiences. This study examines the identities that are constituted by the narrative of pirate radio. Drawing on in-depth interviews with archivists, fans and broadcasters, this article explores the connection between pirate radio, popular music heritage and cultural identity. Moreover, it considers how new technologies such as internet radio provide platforms to engage with this heritage and thus to maintain these local identities. To examine how the memories of pirate radio live on in the present a narrative approach to identity will be used