Browsing by Author "Markl, Michael"

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    Visual Analysis of Regional Anomalies in Myocardial Motion
    (The Eurographics Association, 2018) Sheharyar, Ali; Ruh, Alexander; Aristova, Maria; Scott, Michael; Jarvis, Kelly; Elbaz, Mohammed; Dolan, Ryan; Schnell, Susanne; Lin, Kal; Carr, James; Markl, Michael; Bouhali, Othmane; Linsen, Lars; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
    Regional anomalies in the myocardial motion of the left ventricle (LV) are important biomarkers for several cardiac diseases. Myocardial motion can be captured using a velocity-encoded magnetic resonance imaging method called tissue phase mapping (TPM). The acquired data are pre-processed and represented as regional velocities in cylindrical coordinates at three short-axis slices of the left ventricle over one cardiac cycle. We use a spatio-temporal visualization based on a radial layout where the myocardial regions are laid out in an angular pattern similar to the American Heart Association (AHA) model and the temporal dimension increases with increasing radius. To detect anomalies, we compare patient data against the myocardial motion of a cohort of healthy volunteers. For the healthy volunteer cohort, we compute nested envelopes of central regions for the time series of each region and each of the three velocity directions based on the concept of functional boxplots. A quantitative depiction of deviations from the spatio-temporal pattern of healthy heart motion allows for quick detection of regions of interests, which can then be analyzed in more detail by looking at the actual time series. We evaluated our approach in a qualitative user study with imaging and medical experts. The participants appreciated the proposed encoding and considered it a substantial improvement over the current methods.
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    A Visual Analytics Tool for Cohorts in Motion Data
    (The Eurographics Association, 2019) Sheharyar, Ali; Ruh, Alexander; Valkov, Dimitar; Markl, Michael; Bouhali, Othmane; Linsen, Lars; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
    Motion data are curves over time in a 1D, 2D, or 3D space. To analyze sets of curves, machine learning methods can be applied to cluster them and detect outliers. However, often metadata or prior knowledge of the analyst drives the analysis by defining cohorts. Our goal is to provide a flexible system for comparative visual analytics of cohorts in motion data. The analyst interactively defines cohorts by filtering on metadata properties. We, then, apply machine learning and statistical methods to extract the main features of each cohort. Summarizations of these features are visually encoded using, in particular, boxplots and their extensions to functional and curve boxplots, depending on the number of selected dimensions of the space. These summarizations allow for an intuitive comparative visual analysis of cohorts in a juxtaposed or superimposed representation. Our system provides full flexibility in defining cohorts, selecting time intervals and spatial dimensions, and adjusting the aggregation level of summarizations. Comparison of an individual sample against a cohort is also supported. We demonstrate the functionality, effectiveness, and flexibility of our system by applying it to a range of diverse motion data sets.