Normalized Brain Datasets with Functional Information Predict the Glioma Surgery

DOI
  • TAMURA Manabu
    Faculty of Advanced Techno-Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women’s Medical University Department of Neurosurgery, Neurological Institute, Tokyo Women’s Medical University
  • SATO Ikuma
    Faculty of System Information Science Engineering, Future University Hakodate
  • MANGIN Jean-Francois
    The Computer Assisted Neuroimaging Labratory, Neurospin, Biomedical Imaging Institute, CEA
  • FUJINO Yuichi
    Faculty of System Information Science Engineering, Future University Hakodate
  • MASAMUNE Ken
    Faculty of Advanced Techno-Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women’s Medical University
  • KAWAMATA Takakazu
    Department of Neurosurgery, Neurological Institute, Tokyo Women’s Medical University
  • MURAGAKI Yoshihiro
    Faculty of Advanced Techno-Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women’s Medical University Department of Neurosurgery, Neurological Institute, Tokyo Women’s Medical University

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Other Title
  • 標準脳機能アトラスの投影による未来予測手術の具現化

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Abstract

<p>The goal of this study is to transform to the digitized intra-operative imaging and the compiled brain-function database for the predicting glioma surgery that is based on patientʼs future perspective depending on the tumor resection rate as well as the post-operative complication rate. In awake craniotomy, we estimated language-related location in response to the surgeonʼs electrical stimulation and the examinerʼs task from the precise process analysis of the medical device “IEMAS: Intra-operative examination monitoring in awake surgery”. Secondarily, successful acquisition of log data with the location of medical device integrated into intra-operative MR image was performed and digitized brain function was converted to a normalized brain data format. Digitized log data of the electrostimulation probe during awake craniotomy was acquired successfully in 20 cases, that were totally 22 speech arrest (SA), 10 speech impairment (SI), 12 motor, and 7 sensory responses (51 responses). Finally, intraoperative SA response converted fully to normalized brain with acceptable accuracy. We simulated the projection of the normalized brain data to the individual pre- and intra-operative MR image. These image integration and transformation methods using brain normalization should facilitate practical intra-operative brain mapping. These methods may be helpful for pre-operatively and/or intra-operatively predicting brain function.</p>

Journal

  • Medical Imaging Technology

    Medical Imaging Technology 38 (5), 222-227, 2020-11-25

    The Japanese Society of Medical Imaging Technology

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