ムラガキ　ヨシヒロ   MURAGAKI Yoshihiro   村垣　善浩    所属   医学部 医学科（東京女子医科大学病院）    職種   客員教授
論文種別	原著
言語種別	英語
査読の有無	査読あり
表題	Precise Brain-shift Prediction by New Combination of W-Net Deep Learning for Neurosurgical Navigation.
掲載誌名	正式名：Neurologia medico-chirurgica 略　 称：Neurol Med Chir (Tokyo) ISSNコード：13498029/04708105
掲載区分	国外
巻・号・頁	63(7),pp.295-303
著者・共著者	SHIMAMOTO Takafumi†, SANO Yuko, YOSHIMITSU Kitaro, MASAMUNE Ken, MURAGAKI Yoshihiro
発行年月	2023/07/15
概要	Brain tissue deformation during surgery significantly reduces the accuracy of image-guided neurosurgeries. We generated updated magnetic resonance images (uMR) in this study to compensate for brain shifts after dural opening using a convolutional neural network (CNN). This study included 248 consecutive patients who underwent craniotomy for initial intra-axial brain tumor removal and correspondingly underwent preoperative MR (pMR) and intraoperative MR (iMR) imaging. Deep learning using CNN to compensate for brain shift was performed using the pMR as input data, and iMR obtained after dural opening as the ground truth. For the tumor center (TC) and the maximum shift position (MSP), statistical analysis using the Wilcoxon signed-rank test was performed between the target registration error (TRE) for the pMR and iMR (i.e., the actual amount of brain shift) and the TRE for the uMR and iMR (i.e., residual error after compensation). The TRE at the TC decreased from 4.14 ± 2.31 mm to 2.31 ± 1.15 mm, and the TRE at the MSP decreased from 9.61 ± 3.16 mm to 3.71 ± 1.98 mm. The Wilcoxon signed-rank test of the pMR TRE and uMR TRE yielded a p-value less than 0.0001 for both the TC and MSP. Using a CNN model, we designed and implemented a new system that compensated for brain shifts after dural opening. Learning pMR and iMR with a CNN demonstrated the possibility of correcting the brain shift after dural opening.
DOI	10.2176/jns-nmc.2022-0350
PMID	37164701