The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
A ressonância magnética funcional (fMRI) permite exibir atividades funcionais de certas áreas do cérebro. Em combinação com um conjunto de dados anatômicos tridimensionais, adquiridos com um scanner de ressonância magnética (RM) padrão, pode ser usado para identificar áreas cerebrais eloquentes, resultando na chamada neuronavegação funcional, apoiando o neurocirurgião durante o planejamento e execução da operação. Mas durante a operação, a mudança cerebral leva a uma imprecisão crescente do sistema de navegação. A imagem intraoperatória de RM é usada para atualizar o sistema de neuronavegação com um novo conjunto de dados anatômicos. Para preservar as vantagens da neuronavegação funcional, é necessário salvar as informações funcionais. Como a fMRI não pode ser repetida facilmente no intraoperatório com o paciente inconsciente, tentamos resolver esse problema por meio de processamento de imagem e algoritmos de reconhecimento de padrões. Neste artigo apresentamos uma abordagem automática para transferir marcadores pré-operatórios para um conjunto de dados 3-D intraoperatório. Na primeira etapa os cérebros são segmentados em ambos os conjuntos de imagens que são então registrados e alinhados. A seguir, são determinados os pontos correspondentes. Esses pontos são então usados para determinar a posição dos marcadores, estimando a influência local da mudança cerebral.
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Matthias WOLF, Timo VOGEL, Peter WEIERICH, Heinrich NIEMANN, Christopher NIMSKY, "Automatic Transfer of Preoperative fMRI Markers into Intraoperative MR-Images for Updating Functional Neuronavigation" in IEICE TRANSACTIONS on Information,
vol. E84-D, no. 12, pp. 1698-1704, December 2001, doi: .
Abstract: Functional magnetic resonance imaging (fMRI) allows to display functional activities of certain brain areas. In combination with a three dimensional anatomical dataset, acquired with a standard magnetic resonance (MR) scanner, it can be used to identify eloquent brain areas, resulting in so-called functional neuronavigation, supporting the neurosurgeon while planning and performing the operation. But during the operation brain shift leads to an increasing inaccuracy of the navigation system. Intraoperative MR imaging is used to update the neuronavigation system with a new anatomical dataset. To preserve the advantages of functional neuronavigation, it is necessary to save the functional information. Since fMRI cannot be repeated intraoperatively with the unconscious patient easily we tried to solve this problem by means of image processing and pattern recognition algorithms. In this paper we present an automatic approach for transfering preoperative markers into an intraoperative 3-D dataset. In the first step the brains are segmented in both image sets which are then registered and aligned. Next, corresponding points are determined. These points are then used to determine the position of the markers by estimating the local influence of brain shift.
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_12_1698/_p
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@ARTICLE{e84-d_12_1698,
author={Matthias WOLF, Timo VOGEL, Peter WEIERICH, Heinrich NIEMANN, Christopher NIMSKY, },
journal={IEICE TRANSACTIONS on Information},
title={Automatic Transfer of Preoperative fMRI Markers into Intraoperative MR-Images for Updating Functional Neuronavigation},
year={2001},
volume={E84-D},
number={12},
pages={1698-1704},
abstract={Functional magnetic resonance imaging (fMRI) allows to display functional activities of certain brain areas. In combination with a three dimensional anatomical dataset, acquired with a standard magnetic resonance (MR) scanner, it can be used to identify eloquent brain areas, resulting in so-called functional neuronavigation, supporting the neurosurgeon while planning and performing the operation. But during the operation brain shift leads to an increasing inaccuracy of the navigation system. Intraoperative MR imaging is used to update the neuronavigation system with a new anatomical dataset. To preserve the advantages of functional neuronavigation, it is necessary to save the functional information. Since fMRI cannot be repeated intraoperatively with the unconscious patient easily we tried to solve this problem by means of image processing and pattern recognition algorithms. In this paper we present an automatic approach for transfering preoperative markers into an intraoperative 3-D dataset. In the first step the brains are segmented in both image sets which are then registered and aligned. Next, corresponding points are determined. These points are then used to determine the position of the markers by estimating the local influence of brain shift.},
keywords={},
doi={},
ISSN={},
month={December},}
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TY - JOUR
TI - Automatic Transfer of Preoperative fMRI Markers into Intraoperative MR-Images for Updating Functional Neuronavigation
T2 - IEICE TRANSACTIONS on Information
SP - 1698
EP - 1704
AU - Matthias WOLF
AU - Timo VOGEL
AU - Peter WEIERICH
AU - Heinrich NIEMANN
AU - Christopher NIMSKY
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2001
AB - Functional magnetic resonance imaging (fMRI) allows to display functional activities of certain brain areas. In combination with a three dimensional anatomical dataset, acquired with a standard magnetic resonance (MR) scanner, it can be used to identify eloquent brain areas, resulting in so-called functional neuronavigation, supporting the neurosurgeon while planning and performing the operation. But during the operation brain shift leads to an increasing inaccuracy of the navigation system. Intraoperative MR imaging is used to update the neuronavigation system with a new anatomical dataset. To preserve the advantages of functional neuronavigation, it is necessary to save the functional information. Since fMRI cannot be repeated intraoperatively with the unconscious patient easily we tried to solve this problem by means of image processing and pattern recognition algorithms. In this paper we present an automatic approach for transfering preoperative markers into an intraoperative 3-D dataset. In the first step the brains are segmented in both image sets which are then registered and aligned. Next, corresponding points are determined. These points are then used to determine the position of the markers by estimating the local influence of brain shift.
ER -