Manabu Tamura1,2, Ikuma Sato3, Takashi Maruyama4,5, Kazuma Ohshima3, Jean-François Mangin6, Masayuki Nitta4,5, Taiichi Saito5, Hiroyuki Yamada4, Shinji Minami4, Ken Masamune4, Takakazu Kawamata5, Hiroshi Iseki4, Yoshihiro Muragaki4,5. 1. Faculty of Advanced Techno-Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, 8-1 (TWIns) Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan. tamura.manabu@twmu.ac.jp. 2. Department of Neurosurgery, Neurological Institute, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan. tamura.manabu@twmu.ac.jp. 3. Faculty of System Information Science Engineering, Future University Hakodate, 116-2 Kamedanakano-cho, Hakodate City, Hokkaido, 041-8655, Japan. 4. Faculty of Advanced Techno-Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, 8-1 (TWIns) Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan. 5. Department of Neurosurgery, Neurological Institute, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan. 6. The Computer Assisted Neuroimaging Laboratory, Neurospin, Biomedical Imaging Institute, CEA, Centre d'études de Saclay, 91191, Gif-Sur-Yvette, France.
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.
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.
Authors: J-F Mangin; D Rivière; A Cachia; E Duchesnay; Y Cointepas; D Papadopoulos-Orfanos; P Scifo; T Ochiai; F Brunelle; J Régis Journal: Neuroimage Date: 2004 Impact factor: 6.556