Real-Time Motor Cortex Mapping for the Safe Resection of Glioma: An Intraoperative Resting-State fMRI Study.

BACKGROUND AND PURPOSE: Resting-state functional MR imaging has been used for motor mapping in presurgical planning but never used intraoperatively. This study aimed to investigate the feasibility of applying intraoperative resting-state functional MR imaging for the safe resection of gliomas using real-time motor cortex mapping during an operation. MATERIALS AND METHODS: Using interventional MR imaging, we conducted preoperative and intraoperative resting-state intrinsic functional connectivity analyses of the motor cortex in 30 patients with brain tumors. Factors that may influence intraoperative imaging quality, including anesthesia type (general or awake anesthesia) and tumor cavity (filled with normal saline or not), were studied to investigate image quality. Additionally, direct cortical stimulation was used to validate the accuracy of intraoperative resting-state fMRI in mapping the motor cortex. RESULTS: Preoperative and intraoperative resting-state fMRI scans were acquired for all patients. Fourteen patients who successfully completed both sufficient intraoperative resting-state fMRI and direct cortical stimulation were used for further analysis of sensitivity and specificity. Compared with those subjected to direct cortical stimulation, the sensitivity and specificity of intraoperative resting-state fMRI in localizing the motor area were 61.7% and 93.7%, respectively. The image quality of intraoperative resting-state fMRI was better when the tumor cavity was filled with normal saline (P = .049). However, no significant difference between the anesthesia types was observed (P = .102). CONCLUSIONS: This study demonstrates the feasibility of using intraoperative resting-state fMRI for real-time localization of functional areas during a neurologic operation. The findings suggest that using intraoperative resting-state fMRI can avoid the risk of intraoperative seizures due to direct cortical stimulation and may provide neurosurgeons with valuable information to facilitate the safe resection of gliomas.

A sparse intraoperative data-driven biomechanical model to compensate for brain shift during neuronavigation.

BACKGROUND AND PURPOSE: Intraoperative brain deformation is an important factor compromising the accuracy of image-guided neurosurgery. The purpose of this study was to elucidate the role of a model-updated image in the compensation of intraoperative brain shift. MATERIALS AND METHODS: An FE linear elastic model was built and evaluated in 11 patients with craniotomies. To build this model, we provided a novel model-guided segmentation algorithm. After craniotomy, the sparse intraoperative data (the deformed cortical surface) were tracked by a 3D LRS. The surface deformation, calculated by an extended RPM algorithm, was applied on the FE model as a boundary condition to estimate the entire brain shift. The compensation accuracy of this model was validated by the real-time image data of brain deformation acquired by intraoperative MR imaging. RESULTS: The prediction error of this model ranged from 1.29 to 1.91 mm (mean, 1.62 ± 0.22 mm), and the compensation accuracy ranged from 62.8% to 81.4% (mean, 69.2 ± 5.3%). The compensation accuracy on the displacement of subcortical structures was higher than that of deep structures (71.3 ± 6.1%:66.8 ± 5.0%, P < .01). In addition, the compensation accuracy in the group with a horizontal bone window was higher than that in the group with a nonhorizontal bone window (72.0 ± 5.3%:65.7 ± 2.9%, P < .05). CONCLUSIONS: Combined with our novel model-guided segmentation and extended RPM algorithms, this sparse data-driven biomechanical model is expected to be a reliable, efficient, and convenient approach for compensation of intraoperative brain shift in image-guided surgery.