A literature review of magnetic resonance imaging sequence advancements in visualizing functional neurosurgery targets.

MRI functional neurosurgery magnetic resonance imaging targets visualization

Journal

Journal of neurosurgery
ISSN: 1933-0693
Titre abrégé: J Neurosurg
Pays: United States
ID NLM: 0253357

Informations de publication

Date de publication:
26 Mar 2021
Historique:
received: 13 04 2020
accepted: 13 08 2020
medline: 27 3 2021
pubmed: 27 3 2021
entrez: 26 3 2021
Statut: epublish

Résumé

Historically, preoperative planning for functional neurosurgery has depended on the indirect localization of target brain structures using visible anatomical landmarks. However, recent technological advances in neuroimaging have permitted marked improvements in MRI-based direct target visualization, allowing for refinement of "first-pass" targeting. The authors reviewed studies relating to direct MRI visualization of the most common functional neurosurgery targets (subthalamic nucleus, globus pallidus, and thalamus) and summarize sequence specifications for the various approaches described in this literature. The peer-reviewed literature on MRI visualization of the subthalamic nucleus, globus pallidus, and thalamus was obtained by searching MEDLINE. Publications examining direct MRI visualization of these deep brain stimulation targets were included for review. A variety of specialized sequences and postprocessing methods for enhanced MRI visualization are in current use. These include susceptibility-based techniques such as quantitative susceptibility mapping, which exploit the amount of tissue iron in target structures, and white matter attenuated inversion recovery, which suppresses the signal from white matter to improve the distinction between gray matter nuclei. However, evidence confirming the superiority of these sequences over indirect targeting with respect to clinical outcome is sparse. Future targeting may utilize information about functional and structural networks, necessitating the use of resting-state functional MRI and diffusion-weighted imaging. Specialized MRI sequences have enabled considerable improvement in the visualization of common deep brain stimulation targets. With further validation of their ability to improve clinical outcomes and advances in imaging techniques, direct visualization of targets may play an increasingly important role in preoperative planning.

Identifiants

pubmed: 33770759
doi: 10.3171/2020.8.JNS201125
pii: 2020.8.JNS201125
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1445-1458

Auteurs

Alexandre Boutet (A)

1University Health Network, Toronto.
2Joint Department of Medical Imaging, University of Toronto, Ontario, Canada.

Aaron Loh (A)

1University Health Network, Toronto.

Clement T Chow (CT)

1University Health Network, Toronto.

Alaa Taha (A)

1University Health Network, Toronto.

Gavin J B Elias (GJB)

1University Health Network, Toronto.

Clemens Neudorfer (C)

1University Health Network, Toronto.

Jurgen Germann (J)

1University Health Network, Toronto.

Michelle Paff (M)

1University Health Network, Toronto.

Ludvic Zrinzo (L)

3Functional Neurosurgery Unit, Department of Clinical and Movement Neurosciences, University College London, Queen Square Institute of Neurology, The National Hospital for Neurology and Neurosurgery, London, United Kingdom.

Alfonso Fasano (A)

4Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network, Division of Neurology, University of Toronto.
5Krembil Brain Institute, Toronto, Ontario.

Suneil K Kalia (SK)

1University Health Network, Toronto.

Christopher J Steele (CJ)

6Department of Psychology, Concordia University, Montreal, Quebec, Canada; and.
7Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.

David Mikulis (D)

1University Health Network, Toronto.
2Joint Department of Medical Imaging, University of Toronto, Ontario, Canada.

Walter Kucharczyk (W)

1University Health Network, Toronto.
2Joint Department of Medical Imaging, University of Toronto, Ontario, Canada.

Andres M Lozano (AM)

1University Health Network, Toronto.

Classifications MeSH