Visualization and visual analysis play important roles in exploring, analyzing, and presenting scientific data. In many disciplines, data and model scenarios are becoming multifaceted: data are often spatiotemporal and multivariate; they stem from different data sources (multimodal data), from multiple simulation runs (multirun/ensemble data), or from multiphysics simulations of interacting phenomena (multimodel data resulting from coupled simulation models). Also, data can be of different dimensionality or structured on various types of grids that need to be related or fused in the visualization. This heterogeneity of data characteristics presents new opportunities as well as technical challenges for visualization research. Visualization and interaction techniques are thus often combined with computational analysis. In this survey, we study existing methods for visualization and interactive visual analysis of multifaceted scientific data. Based on a thorough literature review, a categorization of approaches is proposed. We cover a wide range of fields and discuss to which degree the different challenges are matched with existing solutions for visualization and visual analysis. This leads to conclusions with respect to promising research directions, for instance, to pursue new solutions for multirun and multimodel data as well as techniques that support a multitude of facets.

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Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey

This state of the art report focuses on glyph-based visualization, a common form of visual design where a data set is depicted by a collection of visual objects referred to as glyphs. Its major strength is that patterns of multivariate data involving more than two attribute dimensions can often be more readily perceived in the context of a spatial relationship, whereas many techniques for spatial data such as direct volume rendering find difficult to depict with multivariate or multi-field data, and many techniques for non-spatial data such as parallel coordinates are less able to convey spatial relationships encoded in the data. This report fills several major gaps in the literature, drawing the link between the fundamental concepts in semiotics and the broad spectrum of glyph-based visualization, reviewing existing design guidelines and implementation techniques, and surveying the use of glyph-based visualization in many applications.

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Glyph-based Visualization: Foundations, Design Guidelines, Techniques and Applications

Modern positioning and identification technologies enable tracking of almost any type of moving object. A remarkable amount of new trajectory data is thus available for the analysis of various phenomena. In cartography, a typical way to visualise and explore such data is to use a space-time cube, where trajectories are shown as 3D polylines through space and time. With increasingly large movement datasets becoming available, this type of display quickly becomes cluttered and unclear. In this article, we introduce the concept of 3D space-time density of trajectories to solve the problem of cluttering in the space-time cube. The space-time density is a generalisation of standard 2D kernel density around 2D point data into 3D density around 3D polyline data (i.e. trajectories). We present the algorithm for space-time density, test it on simulated data, show some basic visualisations of the resulting density volume and observe particular types of spatio-temporal patterns in the density that are specific to trajectory data. We also present an application to real-time movement data, that is, vessel movement trajectories acquired using the Automatic Identification System (AIS) equipment on ships in the Gulf of Finland. Finally, we consider the wider ramifications to spatial analysis of using this novel type of spatio-temporal visualisation.

Space-time density of trajectories: exploring spatio-temporal patterns in movement data

Purpose:
To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches.
Methods:
DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA’s perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration–time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases.
Results:
Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors.
Conclusions:
Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.

https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1118/1.4898202

Models and methods for analyzing DCE-MRI: A review

Visualization in Medicine is the first book on visualization and its application to problems in medical diagnosis, education, and treatment. The book describes the algorithms, the applications and their validation (how reliable are the results?), and the clinical evaluation of the applications (are the techniques useful?). It discusses visualization techniques from research literature as well as the compromises required to solve practical clinical problems. 

The book covers image acquisition, image analysis, and interaction techniques designed to explore and analyze the data. The final chapter shows how visualization is used for planning liver surgery, one of the most demanding surgical disciplines. The book is based on several years of the authors' teaching and research experience. Both authors have initiated and lead a variety of interdisciplinary projects involving computer scientists and medical doctors, primarily radiologists and surgeons.

* A core field of visualization and graphics missing a dedicated book until now
* Written by pioneers in the field and illustrated in full color
* Covers theory as well as practice

Visualization in Medicine: Theory, Algorithms, and Applications

http://liu.diva-portal.org/smash/get/diva2:623390/FULLTEXT01

Visual Computing in Biology and Medicine: Survey of glyph-based visualization techniques for spatial multivariate medical data

We present a method for visualizing vasculature based on clinical computed tomography or magnetic resonance data. The vessel skeleton as well as the diameter information per voxel serve as input. Our method adheres to these data, while producing smooth transitions at branchings and closed, rounded ends by means of convolution surfaces. We examine the filter design with respect to irritating bulges, unwanted blending and the correct visualization of the vessel diameter. The method has been applied to a large variety of anatomic trees. We discuss the validation of the method by means of a comparison to other visualization methods. Surface distance measures are carried out to perform a quantitative validation. Furthermore, we present the evaluation of the method which has been accomplished on the basis of a survey by 11 radiologists and surgeons.

Visualization of vasculature with convolution surfaces: method, validation and evaluation

Current magnetic resonance imaging (MRI) technology allows the determination of patient-individual coronary tree structure, detection of infarctions, and assessment of myocardial perfusion. Joint inspection of these three aspects yields valuable information for therapy planning, e.g., through classification of myocardium into healthy tissue, regions showing a reversible hypoperfusion, and infarction with additional information on the corresponding supplying artery. Standard imaging protocols normally provide image data with different orientations, resolutions and coverages for each of the three aspects, which makes a direct comparison of analysis results difficult. The purpose of this work is to develop methods for the alignment and combined analysis of these images. The proposed approach is applied to 21 datasets of healthy and diseased patients from the clinical routine. The evaluation shows that, despite limitations due to typical MRI artifacts, combined inspection is feasible and can yield clinically useful information.

A Comprehensive Approach to the Analysis of Contrast Enhanced Cardiac MR Images

Perfusion data are dynamic medical image data which characterize the regional blood flow in human tissue. These data bear a great potential in medical diagnosis, since diseases can be better distinguished and detected at an earlier stage compared to static image data. The wide-spread use of perfusion data is hampered by the lack of efficient evaluation methods. For each voxel, a time-intensity curve characterizes the enhancement of a contrast agent. Parameters derived from these curves characterize the perfusion and have to be integrated for diagnosis. The diagnostic evaluation of this multi-field data is challenging and time-consuming due to its complexity. For the visual analysis of such datasets, feature-based approaches allow to reduce the amount of data and direct the user to suspicious areas. We present an interactive visual analysis approach for the evaluation of perfusion data. For this purpose, we integrate statistical methods and interactive feature specification. Correlation analysis and Principal Component Analysis (PCA) are applied for dimension reduction and to achieve a better understanding of the inter-parameter relations. Multiple, linked views facilitate the definition of features by brushing multiple dimensions. The specification result is linked to all views establishing a focus+context style of visualization in 3D. We discuss our approach with respect to clinical datasets from the three major application areas: ischemic stroke diagnosis, breast tumor diagnosis, as well as the diagnosis of the coronary heart disease (CHD). It turns out that the significance of perfusion parameters strongly depends on the individual patient, scanning parameters, and data pre-processing.

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Interactive Visual Analysis of Perfusion Data

A large variety of techniques has been developed to visualize vascular structures These techniques differ in the necessary preprocessing effort, in the computational effort to create the visualizations, in the accuracy with respect to the underlying image data and in the visual quality of the result In this overview, we compare 3D visualization methods and discuss their applicability for diagnosis, therapy planning and educational purposes We consider direct volume rendering as well as surface rendering

Steffen Oeltze

Papers

Visual Computing in Biology and Medicine: Survey of glyph-based visualization techniques for spatial multivariate medical data

Visualization of vasculature with convolution surfaces: method, validation and evaluation

A Comprehensive Approach to the Analysis of Contrast Enhanced Cardiac MR Images

Interactive Visual Analysis of Perfusion Data

3D Visualization of Vasculature: An Overview