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

Healthcare Technology Letters

article

Saeed M. Bakhshmand¹  Roy Eagleson¹  Sandrine de Ribaupierre¹ 

[1] Biomedical Engineering Graduate Program, University of Western Ontario;Department of Electrical and Computer Engineering, University of Western Ontario;Department of Clinical Neurological Sciences, University of Western Ontario

关键词: neurophysiology;    brain;    surgery;    cognition;    bone;    biomedical MRI;    medical image processing;    biological tissues;    multimodal connectivity based eloquence score computation;    multimodal connectivity based eloquence score visualisation;    computer-aided neurosurgical path planning;    noninvasive assessment;    cognitive importance;    neurosurgical procedures;    in vivo brain imaging modalities;    impact damage;    skull;    multimodal metrics;    intervened grey matter volume;    axonal fibre numbers;    anatomical networks;    functional networks;    solution space;    visually representing connectional cost;    brain networks;    resting state functional magnetic resonance imaging;    fMRI;    deterministic tractography;    rehning traditional heuristics;    resected tissue;    neuroimaging modalities;    related anatomical landmarks;   

DOI  :  10.1049/htl.2017.0073

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

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.

【 授权许可】

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