We are seeking a highly motivated candidate for a PhD position focused on bringing visual analytics into daily neurosurgical planning with diffusion MRI tractography. The project aims to address the critical need for intelligent software that enables interactive visualization of key parameters, thereby facilitating a thorough quality assessment of tractography at every stage. Diffusion MRI tractography has revolutionized our understanding of brain connectivity and is increasingly used in preoperative planning for neurosurgical interventions. However, the reliability and accuracy of tractography remain challenging due to the complexity and variability of the underlying brain architecture. This research endeavor will involve the development of novel techniques and algorithms that integrate advanced visual analytics methods with diffusion MRI data to enhance neurosurgical decision-making processes. The successful candidate will have the opportunity to collaborate with leading experts in both neurosurgery and visualization, pushing the boundaries of medical image analysis and contributing to improved patient outcomes.
The PhD candidate will play a pivotal role in advancing the field of neurosurgical planning by developing intelligent software tools for interactive visualization of key parameters in diffusion MRI tractography. The research will focus on addressing the limitations of existing visualization approaches and propose innovative solutions that enable comprehensive quality assessment of tractography results. The candidate will leverage state-of-the-art techniques in visual analytics, data visualization, and machine learning to design and implement a user-friendly software interface that empowers neurosurgeons to explore and evaluate the reliability and accuracy of tractography-based surgical plans. By integrating quantitative measures and intuitive visual representations about uncertainty, the developed software will assist in identifying potential pitfalls, optimizing surgical strategies, and ultimately improving patient outcomes. The successful candidate will have access to cutting-edge neuroimaging datasets, advanced computing resources, and a collaborative research environment, fostering interdisciplinary knowledge exchange and career development in the emerging field of neurosurgical visualization and analytics.
It is expected that the candidate will author high-quality scientific papers and showcase outputs of this work at international conferences.
The project will be developed within the visualization cluster under the supervision of Dr. Maxime Chamberland (firstname.lastname@example.org) and Prof. Anna Vilanova (email@example.com), and in strong collaboration with Prof. Luc Florack (mathematics) and Dr. Geert-Jan Rutten (ETZ Tilburg).
The visualization cluster (https://research.tue.nl/en/organisations/visualization
) at TU/e has a strong track record in visualization and visual analytics for ML models and high-dimensional data. It has generated several award winning contributions at major visualization conferences (IEEE VIS, IEEE InfoVis, IEEE VAST, EuroVis); several successful start-up companies (MagnaView, Process Gold and SynerScope); and a number of techniques that are used on a large scale world-wide.