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

PurposeThe 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.MethodsBrain 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.ResultsLog 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.ConclusionThis 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.

[1]  Henry Brem,et al.  Establishing percent resection and residual volume thresholds affecting survival and recurrence for patients with newly diagnosed intracranial glioblastoma. , 2014, Neuro-oncology.

[2]  Emmanuel Mandonnet,et al.  Proposal of an optimized strategy for intraoperative testing of speech and language during awake mapping , 2016, Neurosurgical Review.

[3]  Hugues Duffau,et al.  Low Rate of Intraoperative Seizures During Awake Craniotomy in a Prospective Cohort with 374 Supratentorial Brain Lesions: Electrocorticography Is Not Mandatory. , 2015, World neurosurgery.

[4]  Mitchel S Berger,et al.  Functional outcome after language mapping for glioma resection. , 2008, The New England journal of medicine.

[5]  Olivier Clatz,et al.  An ITK implementation of a physics-based non-rigid registration method for brain deformation in image-guided neurosurgery , 2014, Front. Neuroinform..

[6]  N. Tandon,et al.  Intraoperative subcortical language tract mapping guides surgical removal of gliomas involving speech areas , 2008 .

[7]  T. Kayama,et al.  The guidelines for awake craniotomy guidelines committee of the Japan awake surgery conference. , 2012, Neurologia medico-chirurgica.

[8]  H. Iseki,et al.  Strategy of Surgical Resection for Glioma Based on Intraoperative Functional Mapping and Monitoring , 2015, Neurologia medico-chirurgica.

[9]  Lingzhong Meng,et al.  Awake craniotomy to maximize glioma resection: methods and technical nuances over a 27-year period. , 2015, Journal of neurosurgery.

[10]  Hiroshi Iseki,et al.  Proposed therapeutic strategy for adult low-grade glioma based on aggressive tumor resection. , 2015, Neurosurgical focus.

[11]  Karl J. Friston,et al.  Unified segmentation , 2005, NeuroImage.

[12]  Yoshihiro Muragaki,et al.  Differential reorganization of three syntax-related networks induced by a left frontal glioma. , 2014, Brain : a journal of neurology.

[13]  C. Papagno,et al.  INTRAOPERATIVE SUBCORTICAL LANGUAGETRACT MAPPING GUIDES SURGICAL REMOVALOF GLIOMAS INVOLVING SPEECH AREAS , 2007, Neurosurgery.

[14]  Hao Chen,et al.  VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images , 2017, NeuroImage.

[15]  Hiroshi Iseki,et al.  Information-guided surgical management of gliomas using low-field-strength intraoperative MRI. , 2011, Acta neurochirurgica. Supplement.

[16]  Amy S Nowacki,et al.  Residual tumor volume versus extent of resection: predictors of survival after surgery for glioblastoma. , 2014, Journal of neurosurgery.

[17]  Norbert Ritter,et al.  State of the Art and Future Directions , 2019, Real-Time & Stream Data Management.

[18]  Kuniyoshi L Sakai,et al.  Language Acquisition and Brain Development , 2005, Science.

[19]  Christine DeLorenzo,et al.  From medical image computing to computer‐aided intervention: development of a research interface for image‐guided navigation , 2009, The international journal of medical robotics + computer assisted surgery : MRCAS.

[20]  Emmanuel Mandonnet,et al.  Direct electrical stimulation as an input gate into brain functional networks: principles, advantages and limitations , 2010, Acta Neurochirurgica.

[21]  Carl-Fredrik Westin,et al.  Efficient and robust nonlocal means denoising of MR data based on salient features matching , 2012, Comput. Methods Programs Biomed..

[22]  Yoshihiro Muragaki,et al.  Intraoperative Functional Mapping and Monitoring during Glioma Surgery. , 2015, Neurologia medico-chirurgica.

[23]  小野 道夫,et al.  Atlas of the Cerebral Sulci , 1990 .

[24]  B. Meyer,et al.  Functional preoperative and intraoperative mapping and monitoring: increasing safety and efficacy in glioma surgery. , 2015, Neurosurgical focus.

[25]  E. Mandonnet,et al.  Dissociating motor–speech from lexico-semantic systems in the left frontal lobe: insight from a series of 17 awake intraoperative mappings in glioma patients , 2019, Brain Structure and Function.

[26]  Gabor Fichtinger,et al.  OpenIGTLink: an open network protocol for image‐guided therapy environment , 2009, The international journal of medical robotics + computer assisted surgery : MRCAS.

[27]  Jun Okamoto,et al.  Wireless modification of the intraoperative examination monitor for awake surgery. , 2011, Neurologia medico-chirurgica.

[28]  Hugues Duffau,et al.  Selection of intraoperative tasks for awake mapping based on relationships between tumor location and functional networks. , 2013, Journal of neurosurgery.

[29]  Daniel L. Rubin,et al.  Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions , 2017, Journal of Digital Imaging.

[30]  E. Duchesnay,et al.  A framework to study the cortical folding patterns , 2004, NeuroImage.