Virtual reality

Virtual reality is still in its infancy. However, many contemporary applications already have proven virtual reality to be indispensable to everyday life. For instance, the technology of virtual product design and manufacturing makes the new products better and cheaper. The applications of VR in medicine allow doctors to diagnose a disease more accurately. Without a doubt, it has and will foster more innovative research and applications. High-resolution, low-lag and low-price systems will be the focus of future virtual reality research. The technology has been infiltrated into many application fields, involving training, medical, engineering; space exploring and communication.published_or_final_versio

Design area for assistance to maintenance based on augmented reality.

International audienceOne of the main weaknesses with traditional computing is the fact that the numerical world of the computer is decoupled from the user's real world. The application of Augmented Reality (AR) can provide interactive systems in which real objects and computer data are combined in a cohesive way. This new paradigm has many potential applications in various fields, in particular in the maintenance domain. It allows the user to see computer generated virtual objects superimposed to the real world through the see-through Head Mounted Display (HMD). The technician of maintenance, when using this system, can interact with the virtual world and have additional information, such as instruction for performing maintenance tasks in form of text messages, images, 3-D models of pieces or audio such as speech instruction. In this paper, we propose a design process of the maintenance system focused on the analysis of the interaction between the user, the system and the real world. This area is based on the UML notation. The use of UML represents our ergonomic and software design process basis for AR systems. This process also is based on ergonomic characteristics study within a UML system description and on the hybrid PAC-Amodeus model architecture adaptation for the AR systems

Asclepios: a Research Project-Team at INRIA for the Analysis and Simulation of Biomedical Images

International audienceAsclepios1 is the name of a research project-team o cially launched on November 1st, 2005 at INRIA Sophia-Antipolis, to study the Analysis and Simulation of Biological and Medical Images. This research project-team follows a previous one, called Epidaure, initially dedicated to Medical Imaging and Robotics research. These two project teams were strongly supported by Gilles Kahn, who used to have regular scienti c in- teractions with their members. More generally, Gilles Kahn had a unique vision of the growing importance of the interaction of the Information Technologies and Sciences with the Biological and Medical world. He was one of the originators of the creation of a speci c BIO theme among the main INRIA research directions, which now regroups 16 di fferent research teams including Asclepios, whose research objectives are described and illustrated in this article

Augmented reality interaction and vision-based tracking

Master'sMASTER OF ENGINEERIN

Le coeur numérique personnalisé

International audienceDuring the last past years, significant progress in Medical Image Analysis, in biomathematics and biophysics have led to development of the first personalized digital cardiac models. These digital models are personalized which means they can reproduce the anatomy and physiology of specific patients. They allow the quantitative analysis of the organ function and the simulation of some therapies to evaluate their expected benefit. In this article we describe some recent research work done on these topics in our project team Asclepios at Inria, in collaboration with other Inria teams (Macs, Reo, Sisyphe) and external academic, clinical and industrial partners. If a number of challenges in mathematics and informatics still have to be solved before such personalized digital cardiac models can be used in current clinical practice, these first results announce a new generation of tools in digital medicine which can contribute more widely to a more preventive and more predictive personalized medicine.Au cours de ces dernières années, des progrès importants dans l'analyse informatique des images médicales et dans la modélisation biomathématique et biophysique ont rendu possible la construction des premiers modèles numériques et personnalisés du cœur humain. Ces modèles informatiques sont personnalisés car ils reproduisent l'anatomie et la physiologie de patients spécifiques. Ils permettent d'analyser et de quantifier le fonctionnement de l'organe et de simuler certainesthérapies pour en évaluer le bénéfice espéré. Dans cet article nous décrivons des travaux de recherche récents réalisés sur ce thème au sein de l'équipe projet Asclepios à l'Inria, en collaboration avec d'autres équipes Inria (Macs, Reo, Sisyphe) et des partenaires extérieurs académiques, cliniques et industriels. Si de grands défis en modélisation informatique et mathématique doivent encore être relevés avant une utilisation clinique courante du cœur numérique personnalisé, ces premiers résultats annoncent une nouvelle génération d'outils de médecine numérique pouvant contribuer plus largement à une médecine personnalisée plus préventive et plus prédictive

Ultrasound in augmented reality: a mixed-methods evaluation of head-mounted displays in image-guided interventions

Purpose: Augmented reality (AR) and head-mounted displays (HMD) in medical practice are current research topics. A commonly proposed use case of AR-HMDs is to display data in image-guided interventions. Although technical feasibility has been thoroughly shown, effects of AR-HMDs on interventions are not yet well researched, hampering clinical applicability. Therefore, the goal of this study is to better understand the benefits and limitations of this technology in ultrasound-guided interventions. Methods: We used an AR-HMD system (based on the first-generation Microsoft Hololens) which overlays live ultrasound images spatially correctly at the location of the ultrasound transducer. We chose ultrasound-guided needle placements as a representative task for image-guided interventions. To examine the effects of the AR-HMD, we used mixed methods and conducted two studies in a lab setting: (1) In a randomized crossover study, we asked participants to place needles into a training model and evaluated task duration and accuracy with the AR-HMD as compared to the standard procedure without visual overlay and (2) in a qualitative study, we analyzed the user experience with AR-HMD using think-aloud protocols during ultrasound examinations and semi-structured interviews after the task. Results: Participants (n = 20) placed needles more accurately (mean error of 7.4 mm vs. 4.9 mm, p = 0.022) but not significantly faster (mean task duration of 74.4 s vs. 66.4 s, p = 0.211) with the AR-HMD. All participants in the qualitative study (n = 6) reported limitations of and unfamiliarity with the AR-HMD, yet all but one also clearly noted benefits and/or that they would like to test the technology in practice. Conclusion: We present additional, though still preliminary, evidence that AR-HMDs provide benefits in image-guided procedures. Our data also contribute insights into potential causes underlying the benefits, such as improved spatial perception. Still, more comprehensive studies are needed to ascertain benefits for clinical applications and to clarify mechanisms underlying these benefits

Automatic extraction of bronchus and centerline determination from CT images for three dimensional virtual bronchoscopy.

Law Tsui Ying.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 64-70).Abstracts in English and Chinese.Acknowledgments --- p.iiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Structure of Bronchus --- p.3Chapter 1.2 --- Existing Systems --- p.4Chapter 1.2.1 --- Virtual Endoscope System (VES) --- p.4Chapter 1.2.2 --- Virtual Reality Surgical Simulator --- p.4Chapter 1.2.3 --- Automated Virtual Colonoscopy (AVC) --- p.5Chapter 1.2.4 --- QUICKSEE --- p.5Chapter 1.3 --- Organization of Thesis --- p.6Chapter 2 --- Three Dimensional Visualization in Medicine --- p.7Chapter 2.1 --- Acquisition --- p.8Chapter 2.1.1 --- Computed Tomography --- p.8Chapter 2.2 --- Resampling --- p.9Chapter 2.3 --- Segmentation and Classification --- p.9Chapter 2.3.1 --- Segmentation by Thresholding --- p.10Chapter 2.3.2 --- Segmentation by Texture Analysis --- p.10Chapter 2.3.3 --- Segmentation by Region Growing --- p.10Chapter 2.3.4 --- Segmentation by Edge Detection --- p.11Chapter 2.4 --- Rendering --- p.12Chapter 2.5 --- Display --- p.13Chapter 2.6 --- Hazards of Visualization --- p.13Chapter 2.6.1 --- Adding Visual Richness and Obscuring Important Detail --- p.14Chapter 2.6.2 --- Enhancing Details Incorrectly --- p.14Chapter 2.6.3 --- The Picture is not the Patient --- p.14Chapter 2.6.4 --- Pictures-'R'-Us --- p.14Chapter 3 --- Overview of Advanced Segmentation Methodologies --- p.15Chapter 3.1 --- Mathematical Morphology --- p.15Chapter 3.2 --- Recursive Region Search --- p.16Chapter 3.3 --- Active Region Models --- p.17Chapter 4 --- Overview of Centerline Methodologies --- p.18Chapter 4.1 --- Thinning Approach --- p.18Chapter 4.2 --- Volume Growing Approach --- p.21Chapter 4.3 --- Combination of Mathematical Morphology and Region Growing Schemes --- p.22Chapter 4.4 --- Simultaneous Borders Identification Approach --- p.23Chapter 4.5 --- Tracking Approach --- p.24Chapter 4.6 --- Distance Transform Approach --- p.25Chapter 5 --- Automated Extraction of Bronchus Area --- p.27Chapter 5.1 --- Basic Idea --- p.27Chapter 5.2 --- Outline of the Automated Extraction Algorithm --- p.28Chapter 5.2.1 --- Selection of a Start Point --- p.28Chapter 5.2.2 --- Three Dimensional Region Growing Method --- p.29Chapter 5.2.3 --- Optimization of the Threshold Value --- p.29Chapter 5.3 --- Retrieval of Start Point Algorithm Using Genetic Algorithm --- p.29Chapter 5.3.1 --- Introduction to Genetic Algorithm --- p.30Chapter 5.3.2 --- Problem Modeling --- p.31Chapter 5.3.3 --- Algorithm for Determining a Start Point --- p.33Chapter 5.3.4 --- Genetic Operators --- p.33Chapter 5.4 --- Three Dimensional Painting Algorithm --- p.34Chapter 5.4.1 --- Outline of the Three Dimensional Painting Algorithm --- p.34Chapter 5.5 --- Optimization of the Threshold Value --- p.36Chapter 6 --- Automatic Centerline Determination Algorithm --- p.38Chapter 6.1 --- Distance Transformations --- p.38Chapter 6.2 --- End Points Retrieval --- p.41Chapter 6.3 --- Graph Based Centerline Algorithm --- p.44Chapter 7 --- Experiments and Discussion --- p.48Chapter 7.1 --- Experiment of Automated Determination of Bronchus Algorithm --- p.48Chapter 7.2 --- Experiment of Automatic Centerline Determination Algorithm --- p.54Chapter 8 --- Conclusion --- p.62Bibliography --- p.6

Medical Image Analysis: Progress over two decades and the challenges ahead

International audienceThe analysis of medical images has been woven into the fabric of the pattern analysis and machine intelligence (PAMI) community since the earliest days of these Transactions. Initially, the efforts in this area were seen as applying pattern analysis and computer vision techniques to another interesting dataset. However, over the last two to three decades, the unique nature of the problems presented within this area of study have led to the development of a new discipline in its own right. Examples of these include: the types of image information that are acquired, the fully three-dimensional image data, the nonrigid nature of object motion and deformation, and the statistical variation of both the underlying normal and abnormal ground truth. In this paper, we look at progress in the field over the last 20 years and suggest some of the challenges that remain for the years to come

Methods for interventional magnetic resonance imaging

This thesis has as its central aim to demonstrate, develop, discuss and promote new methods and technology for improving interventional low field magnetic resonance imaging. The work addresses problems related to accurate localization of minimally invasive surgical tools by describing novel devices and improvements to prior art techniques, such as optical tracking. In addition to instrument guidance, ablative treatment of liver tumours is discussed in connection with low field temperature measurement and the work describes suitable sequences for qualitative temperature imaging. For instrument localization, a method utilising ex vivo Overhauser enhancement of a catheter like structure was demonstrated. An enhancement factor of 10 was achieved, proving that a substantial signal gain is possible through the use of ex vivo-enhanced liquid. Similarly, a method for biopsy needle tip tracking was developed; where the position of the tip was tracked with a signal from a miniaturized electron spin resonance sample and gradient pulses. At an update rate of 10 samples per second, the accuracy was measured to be better than ±2 mm within a homogeneous sphere of 300 mm. Optical tracking methods concentrated on new indications of use for the developed optical tracking system and associated software: The system was applied to guide the needle 35 times into first sacral root foramina, with a success rate of 97%. It was also used in five bone biopsies, all of which were performed successfully, the samples allowed for a pathologic diagnosis, and the percutaneous procedures could be performed in less than 40 minutes. A new patient tracker device was developed for staged neurosurgical procedures and demonstrated with two patient cases. In the temperature measurement study, spin echo, gradient echo and completely balanced steady-state free precession sequences were optimized for maximal temperature sensitivity and the optimized sequences compared. The steady-state sequence seemed the most promising for the prediction of ablated volume in liver.reviewe