Mobile and Low-cost Hardware Integration in Neurosurgical Image-Guidance

It is estimated that 13.8 million patients per year require neurosurgical interventions worldwide, be it for a cerebrovascular disease, stroke, tumour resection, or epilepsy treatment, among others. These procedures involve navigating through and around complex anatomy in an organ where damage to eloquent healthy tissue must be minimized. Neurosurgery thus has very specific constraints compared to most other domains of surgical care. These constraints have made neurosurgery particularly suitable for integrating new technologies. Any new method that has the potential to improve surgical outcomes is worth pursuing, as it has the potential to not only save and prolong lives of patients, but also increase the quality of life post-treatment. In this thesis, novel neurosurgical image-guidance methods are developed, making use of currently available, low-cost off-the-shelf components. In particular, a mobile device (e.g. smartphone or tablet) is integrated into a neuronavigation framework to explore new augmented reality visualization paradigms and novel intuitive interaction methods. The developed tools aim at improving image-guidance using augmented reality to improve intuitiveness and ease of use. Further, we use gestures on the mobile device to increase interactivity with the neuronavigation system in order to provide solutions to the problem of accuracy loss or brain shift that occurs during surgery. Lastly, we explore the effectiveness and accuracy of low-cost hardware components (i.e. tracking systems and ultrasound) that could be used to replace the current high cost hardware that are integrated into commercial image-guided neurosurgery systems. The results of our work show the feasibility of using mobile devices to improve neurosurgical processes. Augmented reality enables surgeons to focus on the surgical field while getting intuitive guidance information. Mobile devices also allow for easy interaction with the neuronavigation system thus enabling surgeons to directly interact with systems in the operating room to improve accuracy and streamline procedures. Lastly, our results show that low-cost components can be integrated into a neurosurgical guidance system at a fraction of the cost, while having a negligible impact on accuracy. The developed methods have the potential to improve surgical workflows, as well as democratize access to higher quality care worldwide

Human factors in instructional augmented reality for intravehicular spaceflight activities and How gravity influences the setup of interfaces operated by direct object selection

In human spaceflight, advanced user interfaces are becoming an interesting mean to facilitate human-machine interaction, enhancing and guaranteeing the sequences of intravehicular space operations. The efforts made to ease such operations have shown strong interests in novel human-computer interaction like Augmented Reality (AR). The work presented in this thesis is directed towards a user-driven design for AR-assisted space operations, iteratively solving issues arisen from the problem space, which also includes the consideration of the effect of altered gravity on handling such interfaces.Auch in der bemannten Raumfahrt steigt das Interesse an neuartigen Benutzerschnittstellen, um nicht nur die Mensch-Maschine-Interaktion effektiver zu gestalten, sondern auch um einen korrekten Arbeitsablauf sicherzustellen. In der Vergangenheit wurden wiederholt Anstrengungen unternommen, Innenbordarbeiten mit Hilfe von Augmented Reality (AR) zu erleichtern. Diese Arbeit konzentriert sich auf einen nutzerorientierten AR-Ansatz, welcher zum Ziel hat, die Probleme schrittweise in einem iterativen Designprozess zu lösen. Dies erfordert auch die Berücksichtigung veränderter Schwerkraftbedingungen

Multi-Modal Similarity Learning for 3D Deformable Registration of Medical Images

Alors que la perspective de la fusion d images médicales capturées par des systèmes d imageries de type différent est largement contemplée, la mise en pratique est toujours victime d un obstacle théorique : la définition d une mesure de similarité entre les images. Des efforts dans le domaine ont rencontrés un certain succès pour certains types d images, cependant la définition d un critère de similarité entre les images quelle que soit leur origine et un des plus gros défis en recalage d images déformables. Dans cette thèse, nous avons décidé de développer une approche générique pour la comparaison de deux types de modalités donnés. Les récentes avancées en apprentissage statistique (Machine Learning) nous ont permis de développer des solutions innovantes pour la résolution de ce problème complexe. Pour appréhender le problème de la comparaison de données incommensurables, nous avons choisi de le regarder comme un problème de plongement de données : chacun des jeux de données est plongé dans un espace commun dans lequel les comparaisons sont possibles. A ces fins, nous avons exploré la projection d un espace de données image sur l espace de données lié à la seconde image et aussi la projection des deux espaces de données dans un troisième espace commun dans lequel les calculs sont conduits. Ceci a été entrepris grâce à l étude des correspondances entre les images dans une base de données images pré-alignées. Dans la poursuite de ces buts, de nouvelles méthodes ont été développées que ce soit pour la régression d images ou pour l apprentissage de métrique multimodale. Les similarités apprises résultantes sont alors incorporées dans une méthode plus globale de recalage basée sur l optimisation discrète qui diminue le besoin d un critère différentiable pour la recherche de solution. Enfin nous explorons une méthode qui permet d éviter le besoin d une base de données pré-alignées en demandant seulement des données annotées (segmentations) par un spécialiste. De nombreuses expériences sont conduites sur deux bases de données complexes (Images d IRM pré-alignées et Images TEP/Scanner) dans le but de justifier les directions prises par nos approches.Even though the prospect of fusing images issued by different medical imagery systems is highly contemplated, the practical instantiation of it is subject to a theoretical hurdle: the definition of a similarity between images. Efforts in this field have proved successful for select pairs of images; however defining a suitable similarity between images regardless of their origin is one of the biggest challenges in deformable registration. In this thesis, we chose to develop generic approaches that allow the comparison of any two given modality. The recent advances in Machine Learning permitted us to provide innovative solutions to this very challenging problem. To tackle the problem of comparing incommensurable data we chose to view it as a data embedding problem where one embeds all the data in a common space in which comparison is possible. To this end, we explored the projection of one image space onto the image space of the other as well as the projection of both image spaces onto a common image space in which the comparison calculations are conducted. This was done by the study of the correspondences between image features in a pre-aligned dataset. In the pursuit of these goals, new methods for image regression as well as multi-modal metric learning methods were developed. The resulting learned similarities are then incorporated into a discrete optimization framework that mitigates the need for a differentiable criterion. Lastly we investigate on a new method that discards the constraint of a database of images that are pre-aligned, only requiring data annotated (segmented) by a physician. Experiments are conducted on two challenging medical images data-sets (Pre-Aligned MRI images and PET/CT images) to justify the benefits of our approach.CHATENAY MALABRY-Ecole centrale (920192301) / SudocSudocFranceF

Proceedings of the 2nd European conference on disability, virtual reality and associated technologies (ECDVRAT 1998)

The proceedings of the conferenc

The student-produced electronic portfolio in craft education

The authors studied primary school students’ experiences of using an electronic portfolio in their craft education over four years. A stimulated recall interview was applied to collect user experiences and qualitative content analysis to analyse the collected data. The results indicate that the electronic portfolio was experienced as a multipurpose tool to support learning. It makes the learning process visible and in that way helps focus on and improves the quality of learning. © ISLS.Peer reviewe