Top

International Journal of Computer Assisted Radiology and Surgery

Published in:

01-11-2014 | Original Article

Multi-trajectories automatic planner for StereoElectroEncephaloGraphy (SEEG)

Authors: E. De Momi, C. Caborni, F. Cardinale, G. Casaceli, L. Castana, M. Cossu, R. Mai, F. Gozzo, S. Francione, L. Tassi, G. Lo Russo, L. Antiga, G. Ferrigno

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 6/2014

Abstract

Purpose

StereoElectroEncephaloGraphy (SEEG) is done to identify the epileptogenic zone of the brain using several multi-lead electrodes whose positions in the brain are pre-operatively defined. Intracranial hemorrhages due to disruption of blood vessels can cause major complications of this procedure (\(<\)1 %). In order to increase the intervention safety, we developed and tested planning tools to assist neurosurgeons in choosing the best trajectory configuration.

Methods

An automated planning method was developed that maximizes the distance of the electrode from the vessels and avoids the sulci as entry points. The angle of the guiding screws is optimized to reduce positioning error. The planner was quantitatively and qualitatively compared with manually computed trajectories on 26 electrodes planned for three patients undergoing SEEG by four neurosurgeons. Quantitative comparison was performed computing for each trajectory using (a) the Euclidean distance from the closest vessel and (b) the incidence angle.

Results

Quantitative evaluation shows that automatic planned trajectories are safer in terms of distance from the closest vessel with respect to manually planned trajectories. Qualitative evaluation performed by four neurosurgeons showed that the automatically computed trajectories would have been preferred to manually computed ones in 30 % of the cases and were judged good or acceptable in about 86 % of the cases. A significant reduction in time required for planning was observed with the automated system (approximately 1/10).

Conclusion

The automatic SEEG electrode planner satisfied the essential clinical requirements, by providing safe trajectories in an efficient timeframe.

Altrogge I, Kröger T, Preusser T, Büskens C, Pereira PL, Schmidt D, Weihusen A, Peitgen HO (2006) Towards optimization of probe placement for radio-frequency ablation. In: Larsen R, Nielsen M, Sporring J (eds) International conference on medical image computing and computer assisted intervention (MICCAI), LNCS 4190. Springer, Berlin, pp 486–493

Baegert C, Villard C, Schreck P, Soler L (2007) Precise determination of regions of interest for hepatic RFA planning. Stud Health Technol Inform 125:31–36PubMed

Baegert C, Villard C, Schreck P, Soler L, Gangi A (2007) Trajectory optimization for the planning of percutaneous radiofrequency ablation of hepatic tumors. Comput Aided Surg 12(2):82–90PubMedCrossRef

Bériault S, Subaie FA, Collins DL, Sadikot AF, Pike GB (2012) A multi-modal approach to computer-assisted deep brain stimulation trajectory planning. Int J Comput Assist Radiol Surg 7(5):687–704PubMedCrossRef

Caborni C, De Momi E, Antiga L, Hammoud A, Ferrigno G, Cardinale F (2012) Automatic trajectory planning in stereo-electroencephalography image guided neurosurgery. Int J Comput Assist Radiol Surg 7(1):S126–S128

Cardinale F, Cossu M, Castana L, Casaceli G, Schiariti M, Miserocchi A, Fuschillo D, Moscato A, Caborni C, Arnulfo G, Lo Russo G (2013) StereoElectroEncephaloGraphy: surgical methodology, safety and stereotactic application accuracy in five hundred procedures. Neurosurgery 72(3):353–366PubMedCrossRef

Cardinale F, Cossu M, Castana L, Schiariti M, Miserocchi A, Casaceli G, Caborni C, Moscato A, Lo Russo G (2012) Five hundreds stereoelectroencephalography (SEEG) procedures for epilepsy surgery: a retrospective analysis of clinical safety and in vivo application accuracy. Int J Comput Assist Radiol Surg 7(1):S124–S125

Cardinale F, Miserocchi A, Moscato A, Cossu M, Castana L, Schiariti MP, Gozzo F, Pero G, Quilici L, Citterio A, Minella M, Torresin A, Lo Russo G (2012) Talairach methodology in the multimodal imaging and robotic era. In: Scarabin J (ed) Stereotaxy and epilepsy neurosurgery. John Libbey Eurotext, London, pp 245–272

Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis, I: segmentation and surface reconstruction. Neuroimage 9(2):179–194PubMedCrossRef

10.

Danielsson PE (1980) Euclidean Distance Mapping. Comput Graph Image Process 14:227–248CrossRef

11.

De Momi E, Caborni C, Cardinale F, Castana L, Casaceli G, Cossu M, Antiga L, Ferrigno G (2013) Automatic trajectory planner for StereoElectroEncephaloGraphy procedures: a retrospective study. IEEE Trans Biomed Eng 60(4):968–995

12.

Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3):968–980PubMedCrossRef

13.

Diehl B, Lüders HO (2000) Temporal lobe epilepsy: when are invasive recordings needed? Epilepsia 41(3):S61–S74PubMedCrossRef

14.

Essert C, Haegelen C, Lalys F, Abadie A, Jannin P (2012) Automatic computation of electrode trajectories for Deep Brain Stimulation: a hybrid symbolic and numerical approach. Int J Comput Assist Radiol Surg 7(4):517–532PubMedCrossRef

15.

Fleiss JL (1971) Measuring nominal scale agreement among many raters. Psychol Bull 76(5):378–382CrossRef

16.

Gering DT, Nabavi A, Kikinis R, Grimson WE, Hata N, Everett P, Jolesz FA, Wells WM (1999) An integrated visualization system for surgical planning and guidance using image fusion and interventional imaging. In: Taylor C, Colchester A (eds) International conference on medical image computing and computer assisted intervention (MICCAI), LNCS 1679. Springer, Berlin, pp 809–819

17.

Hoffmann D, Lo Russo G (2008) Stereoelectroencephalography. In: Lüders HO (ed) Textbook of epilepsy surgery. Informa Healthcare, London, pp 945–959CrossRef

18.

Jenkinson M, Bannister PR, Brady JM, Smith SM (2002) Improved optimisation for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17(2):825–841PubMedCrossRef

19.

Kahane P, Landrè E, Minotti L, Francione S, Ryvlin P (2006) The Bancaud and Talairach view on the epileptogenic zone: a working hypothesis. Epileptic Disord 8(2):S16–S26PubMed

20.

Kilpatrick C, Cook M, Kaye A, Murphy M, Matkovic Z (1997) Non-invasive investigations successfully select patients for temporal lobe surgery. J Neurol Neurosurg Psychiatry 63(3):327–333PubMedCrossRefPubMedCentral

21.

Kimmel R, Sethian JA (1998) Computing geodesic paths on manifolds. Proc Natl Acad Sci 95:8341–8435CrossRef

22.

Liu Y, Dawant BM, Pallavaram S, Neimat J S, Konrad PE, D’Haese PF, Datteri R, Landman BA, Noble JH (2012) A surgeon specific automatic path planning algorithm for deep brain stimulation. In: Holmes DRIII, Wong KH (eds) Medical imaging 2012: image-guided procedures, robotic interventions, and modeling, SPIE 83161D

23.

Munari C (1987) Depth electrode implantation at Hôpital Sainte Anne, Paris. In: Engel J Jr (ed) Surgical treatment of the epilepsies. Raven Press Ltd, New York, pp 583–588

24.

Munari C, Hoffmann D, Francione S, Kathane P, Tassi L, Lo Russo G, Benabid AL (1994) Stereo-electroencephalography methodology: advantages and limits. Acta Neurol Scand Suppl 152:56–67PubMedCrossRef

25.

Roche A, Malandain G, Pennec X, Ayache N (1998) The correlation ratio as a new similarity measure for multimodal image registration. In: Wells W, Colchester A, Delp S (eds) International conference on medical image computing and computer assisted intervention (MICCAI), LNCS1496. Springer, Berlin, pp 1115–1124

26.

Seitel A, Engel M, Sommer CM, Radeleff BA, Essert-Villard C, Baegert C, Fangerau M, Fritzsche KH, Yung K, Meinzer HP, Maier-Hein L (2011) Computer-assisted trajectory planning for percutaneous needle insertions. Med Phys 38(6):3246–3259PubMedCrossRef

27.

Shamir RR, Joskowicz L, Tamir I, Dabool E, Pertman L, Ben-Ami A, Shoshan Y (2012) Reduced risk trajectory planning in image-guided keyhole neurosurgery. Med Phys 39(5):2885–2895PubMedCrossRef

28.

Shamir RR, Tamir I, Dabool E, Joskowicz L, Shoshan Y (2010) A method for planning safe trajectories in image-guided keyhole neurosurgery. In: Jiang T, Navab N, Pluim JPW, Viergever MA (eds) International conference on medical image computing and computer assisted intervention (MICCAI), LNCS 6363. Springer, Berlin, pp 457–464

29.

Shi Y, Sun B, Lai R, Dinov I, Toga AW (2010) Automated sulci identification via intrinsic modeling of cortical anatomy. In: Jiang T, Navab N, Pluim JPW, Viergever MA (eds) International conference on medical image computing and computer assisted intervention (MICCAI), LNCS 6363. Springer, Berlin, pp 49–56

30.

Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, Zhang Y, De Stefano N, Brady JM, Matthews PM (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(1):S208–S219

31.

Spencer SS, Sperling MR, Shewmon AD (1997) Intracranial electrodes. In: Engel J Jr, Pedley TA (eds) Epilepsy: a comprehensive textbook. Lippincott- Raven, Philadelphia, pp 1719–1747

32.

Thadani VM, Williamson PD, Berger R, Spencer SS, Spencer DD, Novelly RA, Sass KJ, Kim JH, Mattson RH (1995) Successful epilepsy surgery without intracranial EEG recording: criteria for patient selection. Epilepsia 36(1):7–15PubMedCrossRef

33.

Viola P, Wells WM (1997) Alignment by maximization of mutual information. Int J Comp Vis 24(2):137–154CrossRef

34.

Vitter JS (1984) Faster methods for random sampling. Commun ACM 27(7):703–718CrossRef

35.

Yuan J, Chen Y, Hirsch E (2012) Intracranial electrodes in the presurgical evaluation of epilepsy. Neurol Sci 33:723–729PubMedCrossRef

36.

Zumsteg D, Wiese HG (2000) Presurgical evaluation: current role of invasive EEG. Epilepsia 41(3):S55–S60PubMedCrossRef

Title: Multi-trajectories automatic planner for StereoElectroEncephaloGraphy (SEEG)
Authors: E. De Momi
C. Caborni
F. Cardinale
G. Casaceli
L. Castana
M. Cossu
R. Mai
F. Gozzo
S. Francione
L. Tassi
G. Lo Russo
L. Antiga
G. Ferrigno
Publication date: 01-11-2014
Publisher: Springer Berlin Heidelberg
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 6/2014
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-014-1004-1

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Multi-trajectories automatic planner for StereoElectroEncephaloGraphy (SEEG)

Abstract

Purpose

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 6/2014

Vision-based online recognition of surgical activities

AneuSearch: a software prototype for intracranial aneurysm searching and clinical decision support

Needle deflection estimation: prostate brachytherapy phantom experiments

Neurosurgical simulation by interactive computer graphics on iPad

Intraoperative detection and localization of cylindrical implants in cone-beam CT image data

Variable dose interplay effects across radiosurgical apparatus in treating multiple brain metastases