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Faster and improved 3-D head digitization in MEG using Kinect

Accuracy in localizing the brain areas that generate neuromagnetic activity in magnetoencephalography (MEG) is dependent on properly co-registering MEG data to the participant's structural magnetic resonance image (MRI). Effective MEG-MRI co-registration is, in turn, dependent on how accurately...

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Autores principales: Vema Krishna Murthy, Santosh, MacLellan, Matthew, Beyea, Steven, Bardouille, Timothy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4211394/
https://www.ncbi.nlm.nih.gov/pubmed/25389382
http://dx.doi.org/10.3389/fnins.2014.00326
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author Vema Krishna Murthy, Santosh
MacLellan, Matthew
Beyea, Steven
Bardouille, Timothy
author_facet Vema Krishna Murthy, Santosh
MacLellan, Matthew
Beyea, Steven
Bardouille, Timothy
author_sort Vema Krishna Murthy, Santosh
collection PubMed
description Accuracy in localizing the brain areas that generate neuromagnetic activity in magnetoencephalography (MEG) is dependent on properly co-registering MEG data to the participant's structural magnetic resonance image (MRI). Effective MEG-MRI co-registration is, in turn, dependent on how accurately we can digitize anatomical landmarks on the surface of the head. In this study, we compared the performance of three devices—Polhemus electromagnetic system, NextEngine laser scanner and Microsoft Kinect for Windows—for source localization accuracy and MEG-MRI co-registration. A calibrated phantom was used for verifying the source localization accuracy. The Kinect improved source localization accuracy over the Polhemus and the laser scanner by 2.23 mm (137%) and 0.81 mm (50%), respectively. MEG-MRI co-registration accuracy was verified on data from five healthy human participants, who received the digitization process using all three devices. The Kinect device captured approximately 2000 times more surface points than the Polhemus in one third of the time (1 min compared to 3 min) and thrice as many points as the NextEngine laser scanner. Following automated surface matching, the calculated mean MEG-MRI co-registration error for the Kinect was improved by 2.85 mm with respect to the Polhemus device, and equivalent to the laser scanner. Importantly, the Kinect device automatically aligns 20–30 images per second in real-time, reducing the limitations on participant head movement during digitization that are implicit in the NextEngine laser scan (~1 min). We conclude that the Kinect scanner is an effective device for head digitization in MEG, providing the necessary accuracy in source localization and MEG-MRI co-registration, while reducing digitization time.
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spelling pubmed-42113942014-11-11 Faster and improved 3-D head digitization in MEG using Kinect Vema Krishna Murthy, Santosh MacLellan, Matthew Beyea, Steven Bardouille, Timothy Front Neurosci Neuroscience Accuracy in localizing the brain areas that generate neuromagnetic activity in magnetoencephalography (MEG) is dependent on properly co-registering MEG data to the participant's structural magnetic resonance image (MRI). Effective MEG-MRI co-registration is, in turn, dependent on how accurately we can digitize anatomical landmarks on the surface of the head. In this study, we compared the performance of three devices—Polhemus electromagnetic system, NextEngine laser scanner and Microsoft Kinect for Windows—for source localization accuracy and MEG-MRI co-registration. A calibrated phantom was used for verifying the source localization accuracy. The Kinect improved source localization accuracy over the Polhemus and the laser scanner by 2.23 mm (137%) and 0.81 mm (50%), respectively. MEG-MRI co-registration accuracy was verified on data from five healthy human participants, who received the digitization process using all three devices. The Kinect device captured approximately 2000 times more surface points than the Polhemus in one third of the time (1 min compared to 3 min) and thrice as many points as the NextEngine laser scanner. Following automated surface matching, the calculated mean MEG-MRI co-registration error for the Kinect was improved by 2.85 mm with respect to the Polhemus device, and equivalent to the laser scanner. Importantly, the Kinect device automatically aligns 20–30 images per second in real-time, reducing the limitations on participant head movement during digitization that are implicit in the NextEngine laser scan (~1 min). We conclude that the Kinect scanner is an effective device for head digitization in MEG, providing the necessary accuracy in source localization and MEG-MRI co-registration, while reducing digitization time. Frontiers Media S.A. 2014-10-28 /pmc/articles/PMC4211394/ /pubmed/25389382 http://dx.doi.org/10.3389/fnins.2014.00326 Text en Copyright © 2014 Vema Krishna Murthy, MacLellan, Beyea and Bardouille. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Vema Krishna Murthy, Santosh
MacLellan, Matthew
Beyea, Steven
Bardouille, Timothy
Faster and improved 3-D head digitization in MEG using Kinect
title Faster and improved 3-D head digitization in MEG using Kinect
title_full Faster and improved 3-D head digitization in MEG using Kinect
title_fullStr Faster and improved 3-D head digitization in MEG using Kinect
title_full_unstemmed Faster and improved 3-D head digitization in MEG using Kinect
title_short Faster and improved 3-D head digitization in MEG using Kinect
title_sort faster and improved 3-d head digitization in meg using kinect
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4211394/
https://www.ncbi.nlm.nih.gov/pubmed/25389382
http://dx.doi.org/10.3389/fnins.2014.00326
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