Precise Brain-shift Prediction by New Combination of W-Net Deep Learning for Neurosurgical Navigation

Abstract

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.

Keywords

• brain shift
• image-guided neurosurgery
• magnetic resonance image
• convolutional neural network
• brain tumor