Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
International Journal of Computer Assisted Radiology and Surgery
Published in: International Journal of Computer Assisted Radiology and Surgery 1/2017

Open Access 01-01-2017 | Original Article

Automated multiple trajectory planning algorithm for the placement of stereo-electroencephalography (SEEG) electrodes in epilepsy treatment

Authors: Rachel Sparks, Gergely Zombori, Roman Rodionov, Mark Nowell, Sjoerd B. Vos, Maria A. Zuluaga, Beate Diehl, Tim Wehner, Anna Miserocchi, Andrew W. McEvoy, John S. Duncan, Sebastien Ourselin

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 1/2017

Login to get access

Abstract

Purpose

About one-third of individuals with focal epilepsy continue to have seizures despite optimal medical management. These patients are potentially curable with neurosurgery if the epileptogenic zone (EZ) can be identified and resected. Stereo-electroencephalography (SEEG) to record epileptic activity with intracranial depth electrodes may be required to identify the EZ. Each SEEG electrode trajectory, the path between the entry on the skull and the cerebral target, must be planned carefully to avoid trauma to blood vessels and conflicts between electrodes. In current clinical practice trajectories are determined manually, typically taking 2–3 h per patient (15 min per electrode). Manual planning (MP) aims to achieve an implantation plan with good coverage of the putative EZ, an optimal spatial resolution, and 3D distribution of electrodes. Computer-assisted planning tools can reduce planning time by quantifying trajectory suitability.

Methods

We present an automated multiple trajectory planning (MTP) algorithm to compute implantation plans. MTP uses dynamic programming to determine a set of plans. From this set a depth-first search algorithm finds a suitable plan. We compared our MTP algorithm to (a) MP and (b) an automated single trajectory planning (STP) algorithm on 18 patient plans containing 165 electrodes.

Results

MTP changed all 165 trajectories compared to MP. Changes resulted in lower risk (122), increased grey matter sampling (99), shorter length (92), and surgically preferred entry angles (113). MTP changed 42 % (69/165) trajectories compared to STP. Every plan had between 1 to 8 (median 3.5) trajectories changed to resolve electrode conflicts, resulting in surgically preferred plans.

Conclusion

MTP is computationally efficient, determining implantation plans containing 7–12 electrodes within 1 min, compared to 2–3 h for MP.
Literature
1.
Bèriault S, Subaie F, Mok K, Sadikot AF, Pike GB (2011) Automatic trajectory planning of DBS neurosurgery from multi-modal MRI datasets. MICCAI 2011, vol 6891, Lecture Notes in Computer ScienceSpringer, Berlin Heidelberg, pp 259–266
2.
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):687–704CrossRefPubMed
3.
David O, Blauwblomme T, Job AS, Chabardès S, Hoffmann D, Minotti L, Kahane P (2011) Imaging the seizure onset zone with stereo-electroencephalography. Brain 134(10):2898–2911CrossRefPubMed
4.
de Almeida AN, Olivier A, Quesney F, Dubeau F, Savard G, Andermann F (2006) Efficacy of and morbidity associated with stereoelectroencephalography using computerized tomography or magnetic resonance imaging-guided electrode implantation. J Neurosurg 104(4):483–487CrossRefPubMed
5.
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 Bio-Med Eng 60(4):986–993CrossRef
6.
De Momi E, Caborni C, Cardinale F, Casaceli G, Castana L, Cossu M, Mai R, Gozzo F, Francione S, Tassi L, Lo Russo G, Antiga L, Ferrigno G (2014) Multi-trajectories automatic planner for StereoElectroEncephaloGraphy (SEEG). Int J Comput Assist Radiol Surg 9(6):1087–1097
7.
Duncan JS (2010) Imaging in the surgical treatment of epilepsy. Na Rev Neurol 6(10):537–550CrossRef
8.
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):517–532CrossRefPubMed
9.
Fischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci USA 97(20):11,050–11,055CrossRef
10.
Fujii T, Emoto H, Sugou N, Mito T, Shibata I (2003) Neuropath planner-automatic path searching for neurosurgery. Int Congr Ser 1256:587–596CrossRef
11.
Herghelegiu PC, Manta V, Perin R, Bruckner S, Gröller E (2012) Biopsy planner-visual analysis for needle pathway planning in deep seated brain tumor biopsy. Comput Gr Forum 31(3pt2):1085–1094
12.
Karras T (2012) Maximizing parallelism in the construction of BVHs, octrees, and k-d trees. In: Proceedings of the 4th ACM SIGGRAPH/Eurographics conference on high-performance graphics, Eurographics Association, EGGH-HPG 2012:33–37
13.
Kwan P, Brodie MJ (2004) Drug treatment of epilepsy: When does it fail and how to optimize its use? CNS Spectr 9(2):110–119CrossRefPubMed
14.
Liu Y, Konrad PE, Neimat JS, Tatter SB, Yu H, Datteri RD, Landman BA, Noble JH, Pallavaram S, Dawant BM, D’Haese PF (2014) Multisurgeon, multisite validation of a trajectory planning algorithm for deep brain stimulation procedures. IEEE Trans Biomed Eng 61(9):2479–2487CrossRefPubMedPubMedCentral
15.
Morton GM (1966) A computer oriented geodetic data base and a new technique in file sequencing. International Business Machines Company, New York
16.
Navkar NV, Tsekos NV, Stafford JR, Weinberg JS, Deng Z (2010) Visualization and planning of neurosurgical interventions with straight access. IPCAI 2010, vol 6135, Lecture Notes in Computer ScienceSpringer, Berlin Heidelberg, pp 1–11
17.
Nowell M, Rodionov R, Zombori G, Sparks R, Baio G, Gianluca T, Tisdall M, Diehl B, Wehner T, Miserocchi A, McEvoy AW, Ourselin S, Duncan (2016) Comparison of computer-assisted planning and manual planning for depth electrode implantations in epilepsy. J Neurosurg 10(10):1–9, doi:10.​3171/​2015.​6.​JNS15487
18.
Nowell M, Rodionov R, Diehl B, Wehner T, Zombori G, Kinghorn J, Ourselin S, Duncan J, Miserocchi A, McEvoy A (2014) A novel method for implementation of frameless StereoEEG in epilepsy surgery. Neurosurgery 10(4):525–534CrossRefPubMedPubMedCentral
19.
Nowell M, Rodionov R, Zombori G, Sparks R, Winston G, Kinghorn J, Diehl B, Wehner T, Miserocchi A, McEvoy AW, Ourselin S, Duncan J (2015) Utility of 3D multimodality imaging in the implantation of intracranial electrodes in epilepsy. Epilepsia 56(3):403–413CrossRefPubMedPubMedCentral
20.
Rincon-Nigro M, Navkar NV, Tsekos NV, Deng Z (2014) GPU-accelerated interactive visualization and planning of neurosurgical interventions. IEEE Comput Gr 34(1):22–31CrossRef
21.
Rodionov R, Vollmar C, Nowell M, Miserocchi A, Wehner T, Micallef C, Zombori G, Ourselin S, Diehl B, McEvoy AW, Duncan JS (2013) Feasibility of multimodal 3D neuroimaging to guide implantation of intracranial (EEG) electrodes. Epilepsy Res 107(1–2):91–100CrossRefPubMedPubMedCentral
22.
Shamir RR, Tamir I, Dabool E, Joskowicz L, Shoshan Y (2010) A method for planning safe trajectories in image-guided keyhole neurosurgery. MICCAI 2010, vol 6363, Lecture Notes in Computer ScienceSpringer, Berlin Heidelberg, pp 457–464
23.
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):2885–2895CrossRefPubMed
24.
Trope M, Shamir RR, Joskowicz L, Medress Z, Rosenthal G, Mayer A, Levin N, Bick A, Shoshan Y (2015) The role of automatic computer-aided surgical trajectory planning in improving the expected safety of stereotactic neurosurgery. Int J Comput Assist Radiol Surg 10(7):1127–1140CrossRefPubMed
25.
Vaillant M, Davatzikos C, Taylor RH, Bryan RN (1997) A path-planning algorithm for image-guided neurosurgery. CVRMed-MRCAS 1997, vol 1205, Lecture Notes in Computer ScienceSpringer, Berlin Heidelberg, pp 467–476
26.
Zelmann R, Bèriault S, Mok K, Haegelen C, Hall J, Pike GB, Olivier A, Collins DL (2014) Automatic optimization of depth electrode trajectory planning. In: Clinical Image-Based Procedures. Translational Research in Medical Imaging, Lecture Notes in Computer Science, Springer International Publishing, pp 99–107
27.
Zelmann R, Beriault S, Marinho MM, Mok K, Hall JA, Guizard N, Haegelen C, Olivier A, Pike GB, Collins DL (2015) Improving recorded volume in mesial temporal lobe by optimizing stereotactic intracranial electrode implantation planning. Int J Comput Assist Radiol Surg (IJCARS) 10(10):1599–1615CrossRef
28.
Zhang Z (1994) Iterative point matching for registration of free-form curves and surfaces. Int J Comput Vision 13(2):119–152CrossRef
29.
Zombori G, Rodionov R, Nowell M, Zuluaga MA, Clarkson MJ, Micallef C, Diehl B, Wehner T, Miserochi A, McEvoy AW, Duncan JS, Ourselin S (2014) A computer assisted planning system for the placement of sEEG electrodes in the treatment of epilepsy. IPCAI 8498: 118–127
30.
Zuluaga MA, Rodionov R, Nowell M, Achhala S, Zombori G, Cardoso MJ, Miserocchi A, McEvoy AW, Duncan J, Ourselin S (2014) SEEG trajectory planning: Combining stability, structure and scale in vessel extraction. In: MICCAI 2014, Lecture Notes in Computer Science, vol 8674, Springer International Publishing, pp 651–658
Metadata
Title
Automated multiple trajectory planning algorithm for the placement of stereo-electroencephalography (SEEG) electrodes in epilepsy treatment
Authors
Rachel Sparks
Gergely Zombori
Roman Rodionov
Mark Nowell
Sjoerd B. Vos
Maria A. Zuluaga
Beate Diehl
Tim Wehner
Anna Miserocchi
Andrew W. McEvoy
John S. Duncan
Sebastien Ourselin
Publication date
01-01-2017
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 1/2017
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-016-1452-x

Other articles of this Issue 1/2017

International Journal of Computer Assisted Radiology and Surgery 1/2017 Go to the issue

Original Article

Planning of mandibular reconstructions based on statistical shape models

Original Article

On-patient see-through augmented reality based on visual SLAM

Original Article

GPU-based RFA simulation for minimally invasive cancer treatment of liver tumours

Original Article

The production of digital and printed resources from multiple modalities using visualization and three-dimensional printing techniques

Original Article

Electromagnetic navigation versus fluoroscopy in aortic endovascular procedures: a phantom study

Original Article

Shared control of a medical robot with haptic guidance