Gesture-based registration correction using a mobile augmented reality image-guided neurosurgery system

Étienne Léger; Jonatan Reyes; Simon Drouin; D. Louis Collins; Tiberiu Popa; Marta Kersten-Oertel

Gesture-based registration correction using a mobile augmented reality image-guided neurosurgery system

View Fulltext

Author(s): Étienne Léger¹ ; Jonatan Reyes¹ ; Simon Drouin² ; D. Louis Collins² ; Tiberiu Popa^{1, 3} ; Marta Kersten-Oertel^{1, 3}
- Affiliations: 1: Department of Computer Science and Software Engineering, Concordia University , Montréal , Canada ;
  2: Department of Biomedical Engineering, McGill University , Montréal , Canada ;
  3: PERFORM Centre, Concordia University , Montréal , Canada
Source: Volume 5, Issue 5, October 2018, p. 137 – 142
DOI: 10.1049/htl.2018.5063 , Online ISSN 2053-3713

This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)

Received 10/08/2018, Accepted 20/08/2018, Published 27/09/2018

In image-guided neurosurgery, a registration between the patient and their pre-operative images and the tracking of surgical tools enables GPS-like guidance to the surgeon. However, factors such as brainshift, image distortion, and registration error cause the patient-to-image alignment accuracy to degrade throughout the surgical procedure no longer providing accurate guidance. The authors present a gesture-based method for manual registration correction to extend the usage of augmented reality (AR) neuronavigation systems. The authors’ method, which makes use of the touchscreen capabilities of a tablet on which the AR navigation view is presented, enables surgeons to compensate for the effects of brainshift, misregistration, or tracking errors. They tested their system in a laboratory user study with ten subjects and found that they were able to achieve a median registration RMS error of 3.51 mm on landmarks around the craniotomy of interest. This is comparable to the level of accuracy attainable with previously proposed methods and currently available commercial systems while being simpler and quicker to use. The method could enable surgeons to quickly and easily compensate for most of the observed shift. Further advantages of their method include its ease of use, its small impact on the surgical workflow and its small-time requirement.

References

1. 1)
  - 10. Rivaz, H., Louis Collins, D.: Deformable registration of preoperative MR, pre-resection ultrasound, and post-resection ultrasound images of neurosurgery. Int. J. Comput. Assist. Radiol. Surg., 2015, 10, (7), pp. 1017–1028 (doi: 10.1007/s11548-014-1099-4).
2. 2)
  - 1. Miner, R.C.: ‘Image-guided neurosurgery’, J. Med. Imaging. Radiat. Sci., 2017, 48, (4), pp. 328–335 (doi: 10.1016/j.jmir.2017.06.005).
3. 3)
  - 24. Tokuda, J., Fischer, G.S., Papademetris, X., et al: ‘Openigtlink: an open network protocol for image-guided therapy environment’, Int. J. Med. Robot. Comput. Assist. Surg., 2009, 5, (4), pp. 423–434 (doi: 10.1002/rcs.274).
4. 4)
  - 11. Nimsky, C., Ganslandt, O., Hastreiter, P., et al: ‘Intraoperative compensation for brain shift’, Surg. Neurol., 2001, 56, (6), pp. 357–364 (doi: 10.1016/S0090-3019(01)00628-0).
5. 5)
  - 12. Pereira, V.M., Smit-Ockeloen, I., Brina, O., et al: ‘Volumetric measurements of brain shift using intraoperative cone-beam computed tomography: preliminary study’, Operative Neurosurg., 12, (1), pp. 4–13 (doi: 10.1227/NEU.0000000000000999).
6. 6)
  - 7. Stieglitz, L.H., Fichtner, J., Andres, R., et al: ‘The silent loss of neuronavigation accuracy: a systematic retrospective analysis of factors influencing the mismatch of frameless stereotactic systems in cranial neurosurgery’, Neurosurgery, 2013, 72, (5), pp. 796–807 (doi: 10.1227/NEU.0b013e318287072d).
7. 7)
  - 16. Drouin, S., Kersten-Oertel, M., Louis Collins, D.: ‘Interaction-based registration correction for improved augmented reality overlay in neurosurgery’. Workshop on Augmented Environments for Computer-Assisted Interventions, 2015, pp. 21–29.
8. 8)
  - 17. Kantelhardt, S.R., Gutenberg, A., Neulen, A., et al: ‘Video-assisted navigation for adjustment of image guidance accuracy to slight brain shift’, Operative Neurosurg., 2015, 11, (4), pp. 504–511 (doi: 10.1227/NEU.0000000000000921).
9. 9)
  - 6. Zhang, Z.: ‘A flexible new technique for camera calibration’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (11), pp. 1330–1334 (doi: 10.1109/34.888718).
10. 10)
  - 15. Morin, F., Courtecuisse, H., Reinertsen, I., et al: ‘Brain-shift compensation using intraoperative ultrasound and constraint-based biomechanical simulation’, Med. Image Anal., 2017, 40, pp. 133–153 (doi: 10.1016/j.media.2017.06.003).
11. 11)
  - 4. Kersten-Oertel, M., Gerard, I.J., Drouin, S., et al: ‘Augmented reality in neurovascular surgery: feasibility and first uses in the operating room’, Int. J. Comput. Assist. Radiol. Surg., 2015, 10, (11), pp. 1823–1836 (doi: 10.1007/s11548-015-1163-8).
12. 12)
  - 2. Léger, É., Drouin, S., Louis Collins, D., et al: ‘Quantifying attention shifts in augmented reality image-guided neurosurgery’, IET Healthc. Technol. Lett., 2017, 4, (5), pp. 188–192 (doi: 10.1049/htl.2017.0062).
13. 13)
  - 8. Gerard, I.J., Kersten-Oertel, M., Petrecca, K., et al: ‘Brain shift in neuronavigation of brain tumors: a review’, Med. Image Anal., 2017, 35, pp. 403–420 (doi: 10.1016/j.media.2016.08.007).
14. 14)
  - 14. Miga, M.I., Sun, K., Chen, I., et al: ‘Clinical evaluation of a model-updated image-guidance approach to brain shift compensation: experience in 16 cases’, Int. J. Comput. Assist. Radiol. Surg., 2016, 11, (8), pp. 1467–1474 (doi: 10.1007/s11548-015-1295-x).
15. 15)
  - 22. Brooke, J.: ‘Sus-a quick and dirty usability scale’, Usability Eval. Ind., 1996, 189, (194), pp. 4–7.
16. 16)
  - 13. Ruijters, D., ter Haar Romeny, B.M., Suetens, P.: ‘GPU-accelerated elastic 3D image registration for intra-surgical applications’, Comput. Methods Programs Biomed., 2011, 103, (2), pp. 104–112 (doi: 10.1016/j.cmpb.2010.08.014).
17. 17)
  - 9. Nabavi, A., Black, P.M., Gering, D.T., et al: ‘Serial intraoperative MR imaging of brain shift’, Neurosurgery, 2001, 48, (4), pp. 787–798.
18. 18)
  - 10. Watanabe, E., Satoh, M., Konno, T., et al: ‘The trans-visible navigator: a see-through neuronavigation system using augmented reality’, World Neurosurg., 2016, 87, pp. 399–405 (doi: 10.1016/j.wneu.2015.11.084).
19. 19)
  - 21. Tabrizi, L.B., Mahvash, M.: ‘Augmented reality–guided neurosurgery: accuracy and intraoperative application of an image projection technique’, J. Neurosurg., 2015, 123, (1), pp. 206–211 (doi: 10.3171/2014.9.JNS141001).
20. 20)
  - 18. Roethe, A. L., Rösler, J., Vajkoczy, P., et al: ‘Current experiences and future perspectives for augmented reality visualization in navigated neurosurgical interventions’, 22nd Annual Conference of the International Society for Computer Aided Surgery, Berlin, Germany, June 2018.
21. 21)
  - 5. Cabrilo, I., Bijlenga, P., Schaller, K.: ‘Augmented reality in the surgery of cerebral aneurysms: a technical report’, Neurosurgery, 2014, 10, (Suppl 2), pp. 252–260; discussion 260–1 (doi: 10.1227/NEU.0000000000000328).
22. 22)
  - 8. Drouin, S., Kochanowska, A., Kersten-Oertel, M., et al: ‘IBIS: an OR ready open-source platform for image-guided neurosurgery’, Int. J. Comput. Assist. Radiol. Surg., 2017, 12, (3), pp. 363–378 (doi: 10.1007/s11548-016-1478-0).

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Gesture-based registration correction using a mobile augmented reality image-guided neurosurgery system

References

Related content