Connecting Time-Oriented Data and Information to a Coherent Interactive Visualization

In modern intensive care units (ICUs), the medical staff has to monitor a huge amount of high-dimensional and timeoriented data, which needs to be visualized user- and taskspecifically to ease diagnosis and treatment planning. Available visual representations, like diagrams or charts neglect the implicit information as well as a-priory or associated knowledge about the data and its meaning (for example, 38.5°C (101.3°F) is moderate fever and 41°C (105.8°F) is critical fever). Another challenge is to provide appropriate interaction techniques to explore and navigate the data and its temporal dimensions. In this context one major challenge is to connect time-oriented data and information to a coherent interactive visualization. In this paper we present different interactive visualization techniques which enable the users to reveal the data at several levels of detail and abstraction, ranging from a broad overview to the fine structure. We will also introduce a time visualization and navigation technique that connects overview+detail, pan+zoom, and focus+context features to one powerful time-browser

Connecting Time-Oriented Data and Information to a Coherent Interactive Visualization

In modern intensive care units (ICUs), the medical staff has to monitor a huge amount of high-dimensional and timeoriented data, which needs to be visualized user- and taskspecifically to ease diagnosis and treatment planning. Available visual representations, like diagrams or charts neglect the implicit information as well as a-priory or associated knowledge about the data and its meaning (for example, 38.5C (101.3F) is moderate fever and 41C (105.8F) is critical fever). Another challenge is to provide appropriate interaction techniques to explore and navigate the data and its temporal dimensions. In this context one major challenge is to connect time-oriented data and information to a coherent interactive visualization. In this paper we present different interactive visualization techniques which enable the users to reveal the data at several levels of detail and abstraction, ranging from a broad overview to the fine structure. We will also introduce a time visualization and navigation technique that connects overview+detail, pan+zoom, and focus+context features to one powerful time-browser

Comparison of fundamental mesh smoothing algorithms for medical surface models

For diagnosis, intervention planning, and medical education it is common to extract surface models from clinical tomography data. Surface extraction suffers from various effects of medical imaging, segmentation, and surface extraction techniques which cause visual disturbing surface artifacts (e.g. stairs, plateaus). Basic mesh smoothing algorithms for such surface models are a common feature of visualization toolkits. However, there exists no study concerning efficiency, accuracy, and appropriateness of different smoothing strategies for surface models derived from medical volume data. To enable an elaborated and target oriented smoothing of medical surface models, we analyze and compare fundamental mesh smoothing algorithms applied to medical surface models. Based on the analysis of artifact sources and the classification of frequent object types that may make different demands on smoothing, we present smoothing recommendations for medical surface models.

Preim B. Comparison of fundamental mesh smoothing algorithms for medical surface models. SimVis’06

For diagnosis, intervention planning, and medical education it is common to extract surface models from clinical tomography data. Surface extraction suffers from various effects of medical imaging, segmentation, and surface extraction techniques which cause visual disturbing surface artifacts (e.g. stairs, plateaus). Basic mesh smoothing algorithms for such surface models are a common feature of visualization toolkits. However, there exists no study concerning efficiency, accuracy, and appropriateness of different smoothing strategies for surface models derived from medical volume data. To enable an elaborated and target oriented smoothing of medical surface models, we analyze and compare fundamental mesh smoothing algorithms applied to medical surface models. Based on the analysis of artifact sources and the classification of frequent object types that may make different demands on smoothing, we present smoothing recommendations for medical surface models.

Usability Comparison of Mouse-based

Due to the progress in computer graphics hardware high resolution 3d models may be explored at interactive frame rates, and facilities to explore them are a part of modern radiological workstations and therapy planning systems. Despite their advantages, 3d visualizations are only employed by a minority of potential users and even these employ 3d visualizations for a few selected tasks only. We hypothesize that this results from a lack of intuitive interaction techniques for 3d rotation. In this paper, we compare existing techniques with respect to design principles derived by clinical applications and present results of an empirical study. These results are relevant beyond clinical applications and strongly suggest that the presented design principles are crucial for comfortable and predictable interaction techniques for 3d rotation

Combining Training and Computer-assisted Planning of Oncologic Liver Surgery

We present a framework for liver surgery training with the focus on therapy decision making, tumor resection and ablation planning based on patient individual data. In this framework we combine state of the art planning facilities with components for training of oncologic liver surgery planning. Guide'n'Support , Intervention-StrategySets, and Comparison-to-Experts-Plan functionality are implemented to enable guided training for young surgeons as well as open but on demand supported training for senior visceral surgeons

Hardware-accelerated Stippling of Surfaces derived from Medical Volume Data

We present a fast hardware-accelerated stippling method which does not require any preprocessing for placing points on surfaces. The surfaces are automatically parameterized in order to apply stippling textures without major distortions. The mapping process is guided by a decomposition of the space in cubes. Seamless scaling with a constant density of points is realized by subdividing and summarizing cubes. Our mip-map technique enables arbitrarily scaling with one texture. Different shading tones and scales are facilitated by adhering to the constraints of tonal art maps. With our stippling technique, it is feasible to encode all scaling and brightness levels within one self-similar texture. Our method is applied to surfaces extracted from (segmented) medical volume data. The speed of the stippling process enables stippling for several complex objects simultaneously. We consider application scenarios in intervention planning (neck and liver surgery planning). In these scenarios, object recognition (shape perception) is supported by adding stippling to semi-transparently shaded objects which are displayed as context information. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism-Color, shading, shadowing, and texture 1

Using AI and Gd-EOB-DTPA-enhanced MR imaging to assess liver function, comparing the MELIF score with the ALBI score

Abstract Monitoring disease progression is particularly important for determining the optimal treatment strategy in patients with liver disease. Especially for patients with diseases that have a reversible course, there is a lack of suitable tools for monitoring liver function. The development and establishment of such tools is very important, especially in view of the expected increase in such diseases in the future. Image-based liver function parameters, such as the T1 relaxometry-based MELIF score, are ideally suited for this purpose. The determination of this new liver function score is fully automated by software developed with AI technology. In this study, the MELIF score is compared with the widely used ALBI score. The ALBI score was used as a benchmark, as it has been shown to better capture the progression of less severe liver disease than the MELD and Child‒Pugh scores. In this study, we retrospectively determined the ALBI and MELIF scores for 150 patients, compared these scores with the corresponding MELD and Child‒Pugh scores (Pearson correlation), and examined the ability of these scores to discriminate between good and impaired liver function (AUC: MELIF 0.8; ALBI 0.77) and to distinguish between patients with and without cirrhosis (AUC: MELIF 0.83, ALBI 0.79). The MELIF score performed more favourably than the ALBI score and may also be suitable for monitoring mild disease progression. Thus, the MELIF score is promising for closing the gap in the available early-stage liver disease monitoring tools (i.e., identification of liver disease at a potentially reversible stage before chronic liver disease develops)