Visualization, visual analytics and virtual reality in medicine : state-of-the-art techniques and applications /

Visualization, Visual Analytics and Virtual Reality in Medicine: State-of-the-art Techniques and Applications describes important techniques and applications that show an understanding of actual user needs as well as technological possibilities. --

Bibliographic Details
Main Authors:	Preim, Bernhard (Author), Raidou, Renata (Author), Smit, Noeska (Author), Lawonn, Kai (Author)
Corporate Author:	ScienceDirect (Online service)
Format:	eBook
Language:	English
Published:	London : Academic Press, [2023]
Series:	Elsevier and MICCAI Society book series.
Subjects:	Visualization > Therapeutic use. Visual analytics > Therapeutic use. Virtual reality in medicine. Medical illustration. Virtual Reality Medical Illustration Visualisation > Emploi en thérapeutique. Analyse visuelle > Emploi en thérapeutique. Réalité virtuelle en médecine. Illustration médicale. Virtual reality in medicine Visualization > Therapeutic use Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Front Cover
Visualization, Visual Analytics and Virtual Reality in Medicine
Copyright
Contents
Preface
1 Introduction
Acknowledgment
1 Medical visualization techniques
2 Illustrative medical visualization
2.1 Introduction
2.2 Definition
2.3 Requirements
2.4 Preliminaries
2.5 Illustrative visualization techniques
2.5.1 Silhouettes and contours
2.5.2 Feature lines
2.5.3 View-independent feature lines
2.5.4 View-dependent feature lines
2.5.5 Hatching
2.6 Concluding remarks
3 Advanced vessel visualization
3.1 Introduction
3.2 Perception-based vessel visualization
3.2.1 Depth perception
3.2.2 Shape perception
3.3 Integrated visualization of vascular surfaces and embedded flow
3.4 Focus-and-context vessel visualization
3.5 Vessel visualization for diagnosis and treatment planning
3.5.1 Visualization of neurovascular diseases
3.5.2 Visualization of cardiovascular diseases
3.5.2.1 Diagnosis of the coronary heart disease
CTA data
Designing local transfer functions
3.5.2.2 Visualization of aortic dissection
Morphological features
Diameter plot
Branching plot
Intervention plot
3.6 Concluding remarks
4 Multimodal medical visualization
4.1 Introduction
4.2 Medical imaging modalities
4.2.1 Computed tomography (CT)
4.2.2 Magnetic resonance imaging (MRI)
4.2.3 Ultrasound
4.2.4 Nuclear medicine modalities
4.2.5 Hybrid scanners
4.3 Workflow and requirements
4.3.1 Clinical workflow
4.3.2 Requirement analysis
4.4 Visualization techniques
4.4.1 Pre-processing
4.4.2 Smart visibility
4.4.3 Summary
4.5 Rendering and interaction techniques
4.5.1 Fusion
4.5.2 Rendering techniques
4.5.3 Interaction techniques
4.6 Selected applications
4.7 Concluding remarks
5 Medical flow visualization.
5.1 Introduction
5.2 Medical background of flow data generation
5.2.1 Cerebral hemodynamics
5.2.2 Cardiac hemodynamics
5.2.3 Nasal aerodynamics
5.3 Generation of medical flow data
5.3.1 Medical image acquisition
5.3.2 Correction of imaging artifacts
5.3.3 Image segmentation
5.3.4 Surface reconstruction and enhancement
5.3.5 Feature extraction
5.3.6 Generation of volume mesh
5.3.7 CFD simulation
5.3.8 Parameter extraction
5.4 Task-based visual analysis of medical flow data
5.4.1 Gaining a spatial overview
5.4.2 Probe
5.4.3 Filter
5.4.4 Features
5.4.5 Observe
5.4.6 Compare
5.4.7 Validation
5.4.8 Uncertainty
5.5 Medical flow analysis systems
5.6 Concluding remarks
6 Medical animations
6.1 Introduction
6.2 Fundamentals
6.2.1 Fundamentals from perception and cognition
6.2.2 Fundamentals from education
6.2.3 Fundamentals from animation design
6.3 Medical animations of static data
6.3.1 Viewpoint selection
6.3.2 Camera path planning
6.3.3 Annotating animated visualizations
6.3.4 Scripting languages
6.3.5 Hybrid animations
6.4 Animated volume rendering
6.4.1 Camera paths for volume data
6.4.2 Animation for focus+context visualization
6.4.3 Animated display of uncertainty for diagnosis
6.5 Medical animations of dynamic data
6.5.1 Preprocessing measured dynamic medical image data
6.5.2 Medical animations of measured dynamic image data
6.5.3 Animations of dynamic map-based medical data
6.5.4 Animating medical simulations
6.6 Applications of animations based on static data
6.6.1 Medical education
6.6.2 Patient education
6.6.3 Virtual endoscopy
6.6.4 Animation for the diagnosis of medical blood flow data
6.6.5 Forensics
6.6.6 Summary
6.7 Interactive animations
6.8 The process of animation generation.
6.8.1 Tools for animation design
6.8.2 Re-use of medical animations
6.9 Concluding remarks
2 Selected applications
7 3D visualization for anatomy education
7.1 Introduction
7.2 Educational background
7.2.1 Learning theories
7.2.2 Aspects of anatomy education
7.3 Datasets
7.3.1 Anatomical specimens
7.3.2 Clinical imaging
7.3.3 Segmentation
7.4 Visualization techniques
7.4.1 Surface visualization
7.4.2 Volume visualization
7.4.3 Illustrative visualization
7.4.4 Viewpoint selection
7.4.5 Animation in anatomy education
7.4.6 Modeling and visualization for functional anatomy
7.5 Knowledge representation and labeling
7.5.1 Knowledge representation
7.5.2 Labeling anatomical models
7.5.3 External label placement
7.5.4 Internal label placement
7.5.5 Labeling interactive illustrations
7.5.6 Other annotations
7.6 Interaction techniques
7.6.1 Basic interaction techniques
7.6.2 Advanced interaction techniques
7.7 Virtual anatomy systems
7.8 3D web-based anatomy education
7.8.1 Open web-based standards
7.8.2 Selected examples
7.9 Evaluation of virtual anatomy systems
7.9.1 Evaluation strategies
7.9.2 Selected examples
7.9.3 Discussion
7.10 Concluding remarks
8 Visual computing for radiation treatment planning
8.1 Introduction
8.2 Background on cancer
8.3 Radiation therapy (RT)
8.3.1 Basic RT workflow
8.3.2 Data involved in the RT workflow
8.3.3 Users involved in RT
8.4 Definition of target volumes and organs at risk
8.4.1 Data registration
8.4.2 Data fusion
8.4.3 Data segmentation
8.5 Treatment plan design and dose calculation
8.6 Dose plan review and treatment evaluation
8.7 Image-guided adaptive RT
8.8 Concluding remarks
3 Visual analytics in healthcare
9 An introduction to visual analytics.
9.1 Introduction
9.2 The data-users-tasks design triangle
9.3 Information visualization
9.3.1 Visualizing distributions
9.3.2 Scatterplot-based representations
9.3.3 Mosaic plots for visualizing categorical data
9.3.4 Parallel coordinates
9.3.5 Glyph-based visualization
9.3.6 Visualizations of relational data
9.3.7 Geospatial visualizations
9.3.8 Visualization of time-varying data
9.3.9 Multiple coordinated views
9.4 Statistical methods employed in visual analytics
9.5 Dimension reduction
9.5.1 Linear dimension reduction
9.5.2 Non-linear dimension reduction
9.6 Clustering
9.7 Subspace clustering
9.8 Association rule mining
9.8.1 Searching for association rules
9.8.2 Visualization of association rules
9.9 Correlation-based visual analytics
9.9.1 Types of correlations
9.9.2 Rank-by-feature framework
9.9.3 Correlation and causality
9.10 Interaction
9.11 Challenges in visual analytics for clinical applications
9.12 Concluding remarks
10 Visual analytics in public health
10.1 Introduction
10.2 Public health
10.2.1 Epidemiology
10.2.2 Study types
10.2.3 Task analysis and requirements
10.3 Data for public health
10.3.1 Population-based cohort study data
10.3.2 Clinical data
10.3.3 Other data for public health
10.3.4 Data preparation and data management
10.4 Commonly used visual analytics techniques
10.4.1 Dashboards and multiple coordinated views
10.4.2 Interactive subpopulation definition
10.4.3 Analytical methods for subpopulation definition
10.4.4 Spatial epidemiology
10.4.4.1 Data
10.4.4.2 Small area epidemiology
10.4.4.3 Visualization techniques
Choropleth maps
Heatmaps
Isopleth maps
Dotplots
Multivariate maps
Focus-and-context visualization
10.4.4.4 Uncertainty quantification and visualization.
Uncertainty quantification
Uncertainty visualization
Evaluation
10.4.5 Temporal visualizations
10.5 Analysis and control of epidemics
10.5.1 Interactive visualization
10.5.2 Simulation of spreading
10.5.3 Predictive analytics for the simulation of outbreaks
10.5.4 Modeling COVID-19
10.5.5 Training of outbreak response
10.5.6 Zoonotic diseases
10.6 Visual analytics for epidemiological research
10.6.1 Pharmacoepidemiology
10.6.2 Surveillance of air quality
10.6.3 Cancer epidemiology
10.6.4 Investigation of frequent chronic diseases
10.7 Visual analytics of population-based cohort study data
10.7.1 Visual analytics and radiomics
10.7.2 Identification of strong correlations with disorders
10.7.3 Data quality
10.8 Evaluation
10.9 Concluding remarks
11 Visual analytics in clinical medicine
11.1 Introduction
11.2 Data in clinical medicine
11.3 Visual analytics of event-type data
11.3.1 Filtering and simplifying event-type data
11.4 Visualization of single patient data
11.5 Visualization of patient cohort data
11.6 Visual analytics for prediction
11.7 Clinical decision support
11.8 Selected applications
11.8.1 Digital pathology
11.8.2 Gait analysis
11.8.3 Sleep monitoring
11.9 Concluding remarks
4 Virtual Reality in medicine
12 Introduction to Virtual Reality
12.1 Introduction
12.2 Immersion and presence
12.3 VR sickness
12.4 VR hardware
12.4.1 Stereo rendering
12.4.2 Principles of VR headsets
12.4.3 VR headsets
12.4.4 Hardware for semi-immersive VR
12.5 Avatar design
12.5.1 Virtual body illusion
12.5.2 Uncanny valley effect
12.5.3 Customization
12.5.4 Evaluation
12.6 Basic interaction techniques
12.6.1 The role of metaphors
12.6.2 Selection of objects
12.6.3 Manipulation
12.6.4 System control.