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Integrated datasets of normalized brain with functional localization using intra-operative electrical stimulation

  • Manabu TamuraEmail author
  • Ikuma Sato
  • Takashi Maruyama
  • Kazuma Ohshima
  • Jean-François Mangin
  • Masayuki Nitta
  • Taiichi Saito
  • Hiroyuki Yamada
  • Shinji Minami
  • Ken Masamune
  • Takakazu Kawamata
  • Hiroshi Iseki
  • Yoshihiro Muragaki
Original Article
  • 38 Downloads

Abstract

Purpose

The purpose of this study was to transform brain mapping data into a digitized intra-operative MRI and integrated brain function dataset for predictive glioma surgery considering tumor resection volume, as well as the intra-operative and postoperative complication rates.

Methods

Brain function data were transformed into digitized localizations on a normalized brain using a modified electric stimulus probe after brain mapping. This normalized brain image with functional information was then projected onto individual patient’s brain images including predictive brain function data.

Results

Log data were successfully acquired using a medical device integrated into intra-operative MR images, and digitized brain function was converted to a normalized brain data format in 13 cases. For the electrical stimulation positions in which patients showed speech arrest (SA), speech impairment (SI), motor and sensory responses during cortical mapping processes in awake craniotomy, the data were tagged, and the testing task and electric current for the stimulus were recorded. There were 13 SA, 7 SI, 8 motor and 4 sensory responses (32 responses) in total. After evaluation of transformation accuracy in 3 subjects, the first transformation from intra- to pre-operative MRI using non-rigid registration was calculated as 2.6 ± 1.5 and 2.1 ± 0.9 mm, examining neighboring sulci on the electro-stimulator position and the cortex surface near each tumor, respectively; the second transformation from pre-operative to normalized brain was 1.7 ± 0.8 and 1.4 ± 0.5 mm, respectively, representing acceptable accuracy.

Conclusion

This image integration and transformation method for brain normalization should facilitate practical intra-operative brain mapping. In the future, this method may be helpful for pre-operatively or intra-operatively predicting brain function.

Keywords

Brain mapping Digitization Transformation Normalization Predictive glioma surgery 

Notes

Acknowledgements

This study was supported by a Grant-in-Aid for Scientific Research (B-JP22300093, C-JP12007086), Core Research for Evolutional Science and Technology (CREST), JSPS Grant-in-Aid for Scientific Research on Innovative Areas (Multidisciplinary Computational Anatomy, JSPS KAKENHI Grant-JP15H01128, JP17H05306) and MIC Grant-JP162101001 Strategic Information and Communications R&D Promotion Programme (SCOPE). The authors would like to thank Dr. Etsuko Kobayashi for advisory assistance with precision biomedical engineering and Prof. Akimasa Hirata for advisory assistance with the physics of brain mapping.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

All procedures performed in studies involving patients were in accordance with the ethical standards of the ethics committee of Tokyo Women’s Medical University and with the 1964 Declaration of Helsinki, as revised in 2013. Each patient provided informed consent before the surgical procedure.

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Copyright information

© CARS 2019

Authors and Affiliations

  • Manabu Tamura
    • 1
    • 2
    Email author
  • Ikuma Sato
    • 3
  • Takashi Maruyama
    • 1
    • 2
  • Kazuma Ohshima
    • 3
  • Jean-François Mangin
    • 4
  • Masayuki Nitta
    • 1
    • 2
  • Taiichi Saito
    • 2
  • Hiroyuki Yamada
    • 1
  • Shinji Minami
    • 1
  • Ken Masamune
    • 1
  • Takakazu Kawamata
    • 2
  • Hiroshi Iseki
    • 1
  • Yoshihiro Muragaki
    • 1
    • 2
  1. 1.Faculty of Advanced Techno-SurgeryInstitute of Advanced Biomedical Engineering and Science, Tokyo Women’s Medical UniversityTokyoJapan
  2. 2.Department of NeurosurgeryNeurological Institute, Tokyo Women’s Medical UniversityTokyoJapan
  3. 3.Faculty of System Information Science EngineeringFuture University HakodateHokkaidoJapan
  4. 4.The Computer Assisted Neuroimaging Laboratory, NeurospinBiomedical Imaging Institute, CEA, Centre d’études de SaclayGif-Sur-YvetteFrance

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