Real-time illustrative visualization of cardiovascular hemodynamics

Healthcare institutions generate vast amounts of clinical imaging data. Because of the advances in acquisition techniques, contemporary imaging data can be multimodal, multi-dimensional and multi-valued by nature. In particular, modern magnetic resonance imaging (MRI) techniques enable acquisition of multiple image series that supply anatomical and functional information. In this thesis, we concentrate on visual analysis of MRI-acquired blood-flow information in the heart and the thoracic arteries. In addition to anatomical information, MRI enables non-invasive acquisition of time-resolved blood-flow velocity data that capture the intricate cardiovascular hemodynamics. These quantitative velocity data describe the blood flow by means of volumetric velocity fields during a heart beat. This is often referred to as four-dimensional blood-flow data, based on the three spatial dimensions plus the time. For this relatively new MRI acquisition technique, physicians are rather unsure what to expect from the data. Nevertheless, there are clear indications that the data contain valuable information. Quantitative and qualitative analyses of these data should provide insight into the blood-flow dynamics, improving the understanding of the cardiovascular system and its pathologies. This improved understanding conceivably leads to better diagnosis and prognosis of cardiovascular diseases, and may facilitate risk assessment, as well as evaluation of treatment and follow-up studies. With qualitative analyses, physicians aim for newfound insight into the intricate blood-flow dynamics, and therefore there are no a priori questions to be answered, or tasks to be performed. The visual analysis should enable exploration of the complex high-dimensional data. However, exploration through the typical series-by-series and slice-by-slice inspection requires a full mental reconstruction of the unsteady bloodflow velocity data, as well as the cardiovascular morphology. This is a tedious and highly challenging task, even for skilled physicians. Therefore, we aim to alleviate this task by means of comprehensive exploratory and interactive visualization techniques. These techniques incorporate domain knowledge, and provide a more abstract representation of the data that can be steered interactively by the physicians. Prior to visual analysis, sensible abstraction of the high-dimensional data is generally required. We have investigated various approaches to simplify the abundance of information contained in the acquired blood-flow data. On the one hand, we present a segmentation of the luminal geometry, using both direction and speed of the bloodflow velocities. We show that the inclusion of directional information leads to more accurate segmentation results. On the other hand, we abstract the time-resolved blood-flow data using spatiotemporal hierarchical clustering. The resulting cluster tree allows for intuitive level-of-detail selection, using a single user-defined parameter. For sparser detail levels, we use the cluster results in various visualization techniques, providing an abstract overview of the blood-flow data. To facilitate interactive exploration of the four-dimensional blood-flow data, we introduce different probing tools, enabling local analysis of the hemodynamics. The probes enclose a region-of-interest, and serve a basis for various visualizations. The first probing approach focusses on the thoracic arteries, using an automated technique to select vessel cross-sections, perpendicular to the centerline of the vessel. With these cross-sections as a basis, we introduce novel geometry-based blood-flow visualization approaches, such as exploded planar reformats, and flow-rate arrow-trails. In addition, we present improvements on established flow visualization techniques, such as dynamic pathline seeding, and animated pathline highlights. All blood-flow visualizations are combined with an illustrative context to communicate the anatomy. The second probing technique enables exploration throughout the cardiovascular system. To this end, we introduce a virtual probe that resides in the blood-flow field. The virtual probe can be translocated by means of elementary two-dimensional interactions, enabling exploration. Based on the location of the virtual probe, we introduce novel visualization techniques, such as comic-inspired particles, illustrative pathlines, and nested pathtubes. Furthermore, we have investigated approaches to communicate the anatomical context, using volume projections and volume clipping. The results of both probing approaches were evaluated with domain experts, measuring the value of the visualizations, the interaction approaches, and the involved user parameters. The evaluation questionnaires were carried out with several physicians, who are actively involved with advancements in MRI blood flow acquisition, and have in-depth knowledge of diagnosis and treatment of cardiovascular diseases. The feedback obtained from these evaluation studies have yielded valuable insights concerning the presented visualization and interaction techniques. Furthermore, we have extended the use of the virtual probe, visualizing the fourdimensional MRI blood-flow in a similar way as used with color Doppler ultrasound imaging. Ultrasound is an established technique for blood-flow measurements, and the typical red-blue visualizations are familiar to the physicians. We introduce a compound view with different visualizations, inspired by ultrasound imaging, while exploiting the merits of the volumetric MRI blood-flow velocity data. All presented visualization techniques perform in real-time, enabling interactive exploration of the four-dimensional blood-flow data. The renditions update instantaneously when moving the probe, or when the user parameterizes the visualization. Furthermore, real-time interaction with the virtual camera facilitates the visual inspection, providing different viewpoints and enhancing perception of depth in the animated volumetric representations of the blood-flow. To achieve this performance, we have employed modern consumer graphics hardware for our visualizations, enabling parallel processing of the graphics and associated algorithms. Based on the evaluation studies with the involved physicians, we believe that realtime exploration of time-resolved volumetric blood-flow data, by means of illustrative visualizations, facilitates qualitative analysis of the hemodynamic behavior. We were able to present exemplary pathological cases. Time will reveal what new insights can be obtained by means of exploratory qualitative analyses

Understanding blood-flow dynamics: new challenges for visualization

Modern simulation and imaging techniques are providing intricate blood-flow velocity data, the analysis of which can lead to new insights into how blood flow relates to the development of cardiovascular disease. Rapidly interpreting this complex data requires novel comprehensive visual representations

Illustrative volume visualization using GPU-based particle systems

Illustrative techniques are generally applied to produce stylized renderings. Various illustrative styles have been applied to volumetric data sets, producing clearer images and effectively conveying visual information. We adopt particle systems to produce user-configurable stylized renderings from the volume data, imitating traditional pen-and-ink drawings. In the following, we present an interactive GPU-based illustrative volume rendering framework, called VolFliesGPU. In this framework, iso-surfaces are sampled by evenly distributed particle sets, delineating surface shape by illustrative styles. The appearance of these styles is based on locallymeasuredsurface properties. For instance, hatches convey surface shape by orientation and shape characteristics are enhanced by color, mapped using a curvature-based transfer function. Hidden-surfaces are generally removed to avoid visual clutter, after which acombination of styles is applied per iso-surface. Multiple surfaces and styles can be explored interactively, exploiting parallelism in bothgraphics hardware and particle systems. We achieve real-time interaction and prompt parametrization of the illustrative styles, using an intuitive GPGPU paradigm that delivers the computational power to drive our particle system and visualization algorithms

GPU-based particle systems for illustrative volume rendering

Illustrative techniques are generally applied to produce stylized renderings. Various illustrative styles have been applied to volumetric data sets, producing clearer images and effectively conveying visual information. We adopt user-configurable particle systems to produce stylized renderings from the volume data, imitating traditional pen-and-ink drawings. In the following, we present an interactive GPU-based illustrative framework, called VolFliesGPU, for rendering volume data, exploiting parallelism in both graphics hardware and particle systems. We achieve real-time interaction and prompt parametrization of the illustrative styles, using an intuitive GPGPU paradigm that delivers the computational power to drive our particle system and visualization algorithms

Characterization of blood-flow patterns from phase-contrast MRI velocity fields

Hemodynamic information has proven valuable for analysis of cardiovascular diseases. Aberrant blood-flow patterns, for instance, often relate to disease progression. Magnetic resonance imaging enables blood-flow measurements that provide three-dimensional velocity fields during one heartbeat. However, visual analysis of these data is challenging, because of the abundance and complexity of information. Explicit feature extraction can facilitate the pattern characterization, and hence support visualization techniques to effectively convey anomalous flow areas. In this work, we improve on existing pattern matching methods that characterize blood-flow patterns in volumetric imaging data. To this end, we propose a set of helical and vortical patterns that can be parameterized by a single variable. The characterization performance is validated on both synthetic and imaging blood-flow data. Moreover, we present a comprehensive visualization based on the pattern matching results, enabling semi-quantitative assessment of the patterns in relation to the cardiovascular anatomy

GPU-based particle systems for illustrative volume rendering

Illustrative techniques are generally applied to produce stylized renderings. Various illustrative styles have been applied to volumetric data sets, producing clearer images and effectively conveying visual information. We adopt user-configurable particle systems to produce stylized renderings from the volume data, imitating traditional pen-and-ink drawings. In the following, we present an interactive GPU-based illustrative framework, called VolFlies-GPU, for rendering volume data, exploiting parallelism in both graphics hardware and particle systems. We achieve real-time interaction and prompt parametrization of the illustrative styles, using an intuitive GPGPU paradigm that delivers the computational power to drive our particle system and visualization algorithms

Point and pattern detection in 4D PC-MRI blood flow

Cardiovascular diseases (CVDs) are the number one cause of death worldwide[1]. It is important to investigate these diseases in order to find better diagnostic techniques. Blood-flow data provides important information for the analysis of cardiovascular diseases, since the bloodstream influences vessel walls, and vice versa. Anomalies in the blood flow can therefore be a result or cause of CVDs. Blood-flow measurements can reveal patterns and anomalies, and can be used to gain a better understanding of the cardiovascular physiology and pathology. Visual inspection of blood-flow data potentially provides newfound insight in the flow characteristics. However, visual analysis is challenging because of the abundance and complexity of information. New visualization methods showing clearly the anomalous flow areas are needed. This often involves simplification of the velocity fields by means of feature extraction. The goal of this research is to compare and cross-validate points and patterns in 4D (3D cine) PC-MRI blood-flow data. PC-MRI blood-flow data consists of 3D vector fields for several time points within one heart cycle. The data of each time point is combined data from measurements during several heart cycles. We investigate three methods to obtain patterns in the blood-flow field. The winding number method was adapted to make it applicable to vector fields[2,3], and to extract critical points within the blood flow. Furthermore, we have inspected the ¿2 criterion by Jeong and Hussein[4] for the detection of vortices. For flow-pattern recognition, a method proposed by Heiberg et al.[5] is examined. Although these methods provide intrinsically different results, they can be compared because they all indicate the presence of patterns in the blood-flow field. The robustness of the winding number algorithm and the ¿2 criterion are investigated on several artificial data sets under increasing noise levels. The winding number is able to extract saddle points in images with a signal to noise ratio (SNR) of 4 or larger. The ¿2 criterion is able to extract vortices in images with a SNR of 8 or larger. This difference is largely due to the fact that the winding number uses more information from the local environment. In future work, robustness experiments regarding the pattern matching method will be performed. We expect the pattern matching method to be more robust than the winding number and ¿2 criterion approaches, because no derivatives are used, and more information from the local environment is incorporated. Furthermore, an advantage of the pattern matching method is that it can detect multiple patterns. Initial experiments on artificial data sets show promising results. This will be further investigated using measured blood-flow velocity data

4D MRI flow coupled to physics-based fluid simulation for blood-flow visualization

Modern MRI measurements deliver volumetric and time-varying blood-flow data of unprecedented quality. Visual analysis of these data potentially leads to a better diagnosis and risk assessment of various cardiovascular diseases. Recent advances have improved the speed and quality of the imaging data considerably. Nevertheless, the data remains compromised by noise and a lack of spatiotemporal resolution. Besides imaging data, also numerical simulations are employed. These are based on mathematical models of specific features of physical reality. However, these models require realistic parameters and boundary conditions based on measurements. We propose to use data assimilation to bring measured data and physically-based simulation together, and to harness the mutual benefits. The accuracy and noise robustness of the coupled approach is validated using an analytic flow field. Furthermore, we present a comparative visualization that conveys the differences between using conventional interpolation and our coupled approach

4D MRI flow coupled to physics-based fluid simulation for blood-flow visualization

Modern MRI measurements deliver volumetric and time-varying blood-flow data of unprecedented quality. Visual analysis of these data potentially leads to a better diagnosis and risk assessment of various cardiovascular diseases. Recent advances have improved the speed and quality of the imaging data considerably. Nevertheless, the data remains compromised by noise and a lack of spatiotemporal resolution. Besides imaging data, also numerical simulations are employed. These are based on mathematical models of specific features of physical reality. However, these models require realistic parameters and boundary conditions based on measurements. We propose to use data assimilation to bring measured data and physically-based simulation together, and to harness the mutual benefits. The accuracy and noise robustness of the coupled approach is validated using an analytic flow field. Furthermore, we present a comparative visualization that conveys the differences between using conventional interpolation and our coupled approach