Fusion imaging for intra-operative ultrasound-based navigation in neurosurgery View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-09

AUTHORS

Francesco Prada, Massimiliano Del Bene, Luca Mattei, Cecilia Casali, Assunta Filippini, Federico Legnani, Antonella Mangraviti, Andrea Saladino, Alessandro Perin, Carla Richetta, Ignazio Vetrano, Alessandro Moiraghi, Marco Saini, Francesco DiMeco

ABSTRACT

The major shortcoming of image-guided navigation systems is the use of presurgically acquired image data, which does not account for intra-operative changes such as brain shift, tissue deformation and tissue removal occurring during the surgical procedure. Intra-operative ultrasound (iUS) is becoming widely used in neurosurgery but they lack orientation and panoramic view. In this article, we describe our procedure for US-based real-time neuro-navigation during surgery. We used fusion imaging between preoperative magnetic resonance imaging (MRI) and iUS for brain lesion removal in 67 patients so far. Surgical planning is based on preoperative MRI only. iUS images obtained during surgery are fused with the preoperative MRI. Surgery is performed under intra-operative US control. Relying on US imaging, it is possible to recalibrate navigated MRI imaging, adjusting distortion due to brain shift and tissue resection, continuously updating the two modalities. Ultrasound imaging provides excellent visualization of targets, their margins and surrounding structures. The use of navigated MRI is helpful in better understanding cerebral ultrasound images, providing orientation and panoramic view. Intraoperative US-guided neuro-navigation adjustments are very accurate and helpful in the event of brain shift. The use of this integrated system allows for a true real-time feedback during surgery. More... »

PAGES

243-251

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s40477-014-0111-8

DOI

http://dx.doi.org/10.1007/s40477-014-0111-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1037954255

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25177400


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