PURPOSE: Depth electrodes are inserted in the brain to locate the epileptogenic zone without craniotomy, but there is risk of surgical hemorrhage. Preoperative planning is required to mitigate this risk. A preoperative imaging, segmentation and three dimensional (3D) visualization procedure was developed to provide neurosurgeons with cortical and vascular anatomy information for surgical planning and neuronavigation. METHODS: Cerebral vascular imaging was performed with phase-contrast magnetic resonance angiography (PC-MRA). Fuzzy c-means was performed to extract brain parenchyma from the PC-MRA images. A multi-scale vessel enhancement filter and thresholding process were combined to segment the vasculature and suppress background noise in the PC-MRA images. Finally, 3D visualization of the vasculature and cortical structures was implemented using volume rendering. RESULTS: Quantitative and qualitative validation of the vascular segmentation method were done. Using manual vascular segmentation as the gold standard, our method produced a satisfactory result: sensitivity was as high as 90 % at a specificity level of 95 %. Moreover, comparing the 3D visualizations of the vasculature and cortical structure for 4 patients with their respective intraoperative craniotomy photographs showed high levels of similarity. CONCLUSION: A new automated segmentation and visualization procedure provides sufficient and accurate cortical and vascular anatomy information compared to intraoperative photographs. This method has potential to assist neurosurgeons with planning and neuronavigation for depth electrode insertion with avoidance of cerebral hemorrhage.
PURPOSE: Depth electrodes are inserted in the brain to locate the epileptogenic zone without craniotomy, but there is risk of surgical hemorrhage. Preoperative planning is required to mitigate this risk. A preoperative imaging, segmentation and three dimensional (3D) visualization procedure was developed to provide neurosurgeons with cortical and vascular anatomy information for surgical planning and neuronavigation. METHODS: Cerebral vascular imaging was performed with phase-contrast magnetic resonance angiography (PC-MRA). Fuzzy c-means was performed to extract brain parenchyma from the PC-MRA images. A multi-scale vessel enhancement filter and thresholding process were combined to segment the vasculature and suppress background noise in the PC-MRA images. Finally, 3D visualization of the vasculature and cortical structures was implemented using volume rendering. RESULTS: Quantitative and qualitative validation of the vascular segmentation method were done. Using manual vascular segmentation as the gold standard, our method produced a satisfactory result: sensitivity was as high as 90 % at a specificity level of 95 %. Moreover, comparing the 3D visualizations of the vasculature and cortical structure for 4 patients with their respective intraoperative craniotomy photographs showed high levels of similarity. CONCLUSION: A new automated segmentation and visualization procedure provides sufficient and accurate cortical and vascular anatomy information compared to intraoperative photographs. This method has potential to assist neurosurgeons with planning and neuronavigation for depth electrode insertion with avoidance of cerebral hemorrhage.
Authors: Andre Machado; Ali R Rezai; Brian H Kopell; Robert E Gross; Ashwini D Sharan; Alim-Louis Benabid Journal: Mov Disord Date: 2006-06 Impact factor: 10.338
Authors: Ellen J L Brunenberg; Anna Vilanova; Veerle Visser-Vandewalle; Yasin Temel; Linda Ackermans; Bram Platel; Bart M ter Haar Romeny Journal: Med Image Comput Comput Assist Interv Date: 2007
Authors: M Mallar Chakravarty; Abbas F Sadikot; Sanjay Mongia; Gilles Bertrand; D Louis Collins Journal: Med Image Comput Comput Assist Interv Date: 2006
Authors: Kathryn L Holloway; Steven E Gaede; Philip A Starr; Joshua M Rosenow; Viswanathan Ramakrishnan; Jaimie M Henderson Journal: J Neurosurg Date: 2005-09 Impact factor: 5.115
Authors: Maria A Zuluaga; Roman Rodionov; Mark Nowell; Sufyan Achhala; Gergely Zombori; Alex F Mendelson; M Jorge Cardoso; Anna Miserocchi; Andrew W McEvoy; John S Duncan; Sébastien Ourselin Journal: Int J Comput Assist Radiol Surg Date: 2015-04-07 Impact factor: 2.924