ムラガキ ヨシヒロ   MURAGAKI Yoshihiro
  村垣 善浩
   所属   研究施設 研究施設
   職種   教授
言語種別 英語
発表タイトル Integration of intra-operative brain functionalpositions into the standard brain using SPM
会議名 CARS 2019 – 33rd International Congress and Exhibition
学会区分 国際学会及び海外の学会
発表形式 ポスター掲示
講演区分 一般
発表者・共同発表者OHSHIMA Kazuma, SATO Ikuma, NAMBU Y, FUJINO Yuichi, HORISE Yuki, KUSUDA Kaori, TAMURA Manabu, MURAGAKI Yoshihiro, MASAMUNE Ken
発表年月日 2019/06/20
開催地
(都市, 国名)
Rennes, France
学会抄録 CARS 2019 21(4) 2019
CARS 2019 – 33rd International Congress and Exhibition
概要 Purpose
‘‘Future-predicting glioma surgery’’ has been suggested for glioma
resection surgery [1]. Future-predicting glioma surgery can facilitate
clinical decision-making by predicting the survival rate and postoperative neurological complications with respect to the set resection
area. However, storage of evidenced-based information for realizing
future-predicting glioma surgery has not yet been sufficiently done.
To enable future-predicting glioma surgery, we need to create
datasets for predicting the survival rate and post-operative neurological complications extracted from post-surgery information. In a
previous study, digitized brain functional positions and methods for
creation of datasets for statistical analysis and a method of integrating
intra-operative electrical stimulation positions into the standard brain
were suggested [1, 2]. As individual differences exist in intra-operative information among patients, position information is unified in
the standard brain coordinate system, and individual differences can
be eliminated by standardization. Using the intra-operative information obtained by these studies to integrate them into the standard brain
allows the analysis of evidenced-based information. However, the
available datasets for statistical analysis are not adequate.
In this study, for creating analysis datasets to realize future prediction surgery, brain functional positions with individual differences
of each patient were integrated into the standard brain by statistical
parametric mapping (SPM) and were visualized. In addition, w