Multimodal connectivity based eloquence score computation and visualisation for computer-aided neurosurgical path planning.

Non-invasive assessment of cognitive importance has been a major challenge for planning of neurosurgical procedures. In the past decade, in vivo brain imaging modalities have been considered for estimating the 'eloquence' of brain areas. In order to estimate the impact of damage caused by an access path towards a target region inside of the skull, multi-modal metrics are introduced in this paper. Accordingly, this estimated damage is obtained by combining multi-modal metrics. In other words, this damage is an aggregate of intervened grey matter volume and axonal fibre numbers, weighted by their importance within the assigned anatomical and functional networks. To validate these metrics, an exhaustive search algorithm is implemented for characterising the solution space and visually representing connectional cost associated with a path initiated from underlying points. In this presentation, brain networks are built from resting state functional magnetic resonance imaging (fMRI) and deterministic tractography. their results demonstrate that the proposed approach is capable of refining traditional heuristics, such as choosing the minimal distance from the lesion, by supplementing connectional importance of the resected tissue. This provides complementary information to help the surgeon in avoiding important functional hubs and their anatomical linkages; which are derived from neuroimaging modalities and incorporated to the related anatomical landmarks.

MultiXplore: Visual exploration platform for multimodal neuroimaging data.

BACKGROUND: Construction of brain functional and structural networks by neuroimaging methods facilitates inter-modal studies. These type of studies often demand exploration tools to carry out functional-structural discoveries and answer questions regarding the anatomical basis of brain networks. NEW METHOD: This paper describes the design and development of a software module for interactive visualization and exploration of dual-modal brain networks. Our objective was to equip the user with a research tool to investigate brain connectivity matrices while visualizing relevant anatomical landmarks within a 3D volumetric view. In order to create this view, MultiXplore was designed to load data from both structural and diffusion MRI and connectivity matrices. RESULTS: Once user starts to select desired cells through an interactive matrix unit, associated axonal fiber pathways and grey matter regions are generated and displayed. Integration and visualization of functional and structural networks in this 3D interactive framework was successfully implemented and tested. COMPARISON WITH EXISTING METHOD(S): MultiXplore contributes to the transition of connectivity visualization techniques from node-link format to an anatomically more realistic graphical form and assists scientists in relating connectivity matrices to their anatomical correlates. This module also benefits from additional novel functionalities to annotate and differentiate fibers in a large bundle. Unlike traditional graph displays, interactive functionality helps in the inspection and visualization of relevant structures without cluttering the scene with excessive items. CONCLUSION: This module was designed and developed as a plugin to 3D Slicer imaging platform and is accessible for neuroimaging researchers through NITRC (http://www.nitrc.org/projects/multixplore/).

Evaluation of a mobile augmented reality application for image guidance of neurosurgical interventions.

Image guidance can provide surgeons with valuable contextual information during a medical intervention. Often, image guidance systems require considerable infrastructure, setup-time, and operator experience to be utilized. Certain procedures performed at bedside are susceptible to navigational errors that can lead to complications. We present an application for mobile devices that can provide image guidance using augmented reality to assist in performing neurosurgical tasks. A methodology is outlined that evaluates this mode of visualization from the standpoint of perceptual localization, depth estimation, and pointing performance, in scenarios derived from a neurosurgical targeting task. By measuring user variability and speed we can report objective metrics of performance for our augmented reality guidance system.