IBIS: an OR ready open-source platform for image-guided neurosurgery

  • Simon Drouin
  • Anna Kochanowska
  • Marta Kersten-Oertel
  • Ian J. Gerard
  • Rina Zelmann
  • Dante De Nigris
  • Silvain Bériault
  • Tal Arbel
  • Denis Sirhan
  • Abbas F. Sadikot
  • Jeffery A. Hall
  • David S. Sinclair
  • Kevin Petrecca
  • Rolando F. DelMaestro
  • D. Louis Collins
Original Article

Abstract

Purpose

Navigation systems commonly used in neurosurgery suffer from two main drawbacks: (1) their accuracy degrades over the course of the operation and (2) they require the surgeon to mentally map images from the monitor to the patient. In this paper, we introduce the Intraoperative Brain Imaging System (IBIS), an open-source image-guided neurosurgery research platform that implements a novel workflow where navigation accuracy is improved using tracked intraoperative ultrasound (iUS) and the visualization of navigation information is facilitated through the use of augmented reality (AR).

Methods

The IBIS platform allows a surgeon to capture tracked iUS images and use them to automatically update preoperative patient models and plans through fast GPU-based reconstruction and registration methods. Navigation, resection and iUS-based brain shift correction can all be performed using an AR view. IBIS has an intuitive graphical user interface for the calibration of a US probe, a surgical pointer as well as video devices used for AR (e.g., a surgical microscope).

Results

The components of IBIS have been validated in the laboratory and evaluated in the operating room. Image-to-patient registration accuracy is on the order of \(3.72\pm 1.27\,\hbox {mm}\) and can be improved with iUS to a median target registration error of 2.54 mm. The accuracy of the US probe calibration is between 0.49 and 0.82 mm. The average reprojection error of the AR system is \(0.37\pm 0.19\,\hbox {mm}\). The system has been used in the operating room for various types of surgery, including brain tumor resection, vascular neurosurgery, spine surgery and DBS electrode implantation.

Conclusions

The IBIS platform is a validated system that allows researchers to quickly bring the results of their work into the operating room for evaluation. It is the first open-source navigation system to provide a complete solution for AR visualization.

Keywords

Image-guided surgery Ultrasound Augmented reality Brain shift correction 

References

  1. 1.
    Kersten-Oertel M, Jannin P, Collins DL (2013) The state of the art of visualization in mixed reality image guided surgery. Comput Med Imaging Graph 37:98–112. doi:10.1016/j.compmedimag.2013.01.009 CrossRefPubMedGoogle Scholar
  2. 2.
    Ince DC, Hatton L, Graham-Cumming J (2012) The case for open computer programs. Nature 482:485–488. doi:10.1038/nature10836 CrossRefPubMedGoogle Scholar
  3. 3.
    Owens B (2016) Montreal institute going ‘open’ to accelerate science. Science 351:329–329. doi:10.1126/science.351.6271.329 CrossRefPubMedGoogle Scholar
  4. 4.
    Owens B (2016) Data sharing: access all areas. Nature 533:S71–S72CrossRefPubMedGoogle Scholar
  5. 5.
    Enquobahrie A, Cheng P, Gary K, Ibanez L, Gobbi D, Lindseth F, Yaniv Z, Aylward S, Jomier J, Cleary K (2007) The image-guided surgery toolkit IGSTK: an open source C++ software toolkit. J Digit Imaging 20(Suppl 1):21–33. doi:10.1007/s10278-007-9054-3 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Tokuda J, Gregory S, Yaniv Z, Cheng P, Blevins J, Golby AJ, Kapur T, Pieper S, Burdette EC, Fischer GS, Papademetris X, Ibanez L, Liu H, Arata J, Fichtinger G, Tempany CM, Hata N (2009) OpenIGTLink: an open network protocol for image-guided therapy environment. Int J Med Robot Comput Assist Surg 5:423–434. doi:10.1002/rcs.274 CrossRefGoogle Scholar
  7. 7.
    Lasso A, Heffter T, Rankin A, Pinter C, Ungi T, Fichtinger G (2014) PLUS: open-source toolkit for ultrasound-guided intervention systems. IEEE Trans Biomed Eng 61:2527–2537. doi:10.1109/TBME.2014.2322864 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Nolden M, Zelzer S, Seitel A, Wald D, Müller M, Franz AM, Maleike D, Fangerau M, Baumhauer M, Maier-Hein L, Maier-Hein KH, Meinzer H-P, Wolf I (2013) The Medical Imaging Interaction Toolkit: challenges and advances?: 10 years of open-source development. Int J Comput Assist Radiol Surg 8:607–620. doi:10.1007/s11548-013-0840-8 CrossRefPubMedGoogle Scholar
  9. 9.
    Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R (2012) 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30:1323–1341. doi:10.1016/j.mri.2012.05.001 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Franz AM, Seitel A, Servatius M, Zöllner C, Gergel I, Wegner I, Neuhaus J, Zelzer S, Nolden M, Gaa J, Mercea P, Yung K, Sommer CM, Radeleff BA, Schlemmer H-P, Kauczor H-U, Meinzer H-P, Maier-Hein L (2012) Simplified development of image-guided therapy software with MITK-IGT. In: Holmes DR III, Wong KH (eds) SPIE Med. International Society for Optics and Photonics, Imaging, p 83162JGoogle Scholar
  11. 11.
    Clarkson MJ, Zombori G, Thompson S, Totz J, Song Y, Espak M, Johnsen S, Hawkes D, Ourselin S (2015) The NifTK software platform for image-guided interventions: platform overview and NiftyLink messaging. Int J Comput Assist Radiol Surg 10:301–316. doi:10.1007/s11548-014-1124-7 CrossRefPubMedGoogle Scholar
  12. 12.
    Ungi T, Gauvin G, Lasso A, Yeo CT, Pezeshki P, Vaughan T, Carter K, Rudan J, Engel CJ, Fichtinger G (2016) Navigated breast tumor excision using electromagnetically tracked ultrasound and surgical instruments. IEEE Trans Biomed Eng 63:600–606. doi:10.1109/TBME.2015.2466591 CrossRefPubMedGoogle Scholar
  13. 13.
    Askeland C, Solberg OV, Bakeng JBL, Reinertsen I, Tangen GA, Hofstad EF, Iversen DH, Våpenstad C, Selbekk T, Langø T, Hernes TAN, Olav Leira H, Unsgård G, Lindseth F (2015) CustusX: an open-source research platform for image-guided therapy. Int J Comput Assist Radiol Surg. doi:10.1007/s11548-015-1292-0 PubMedPubMedCentralGoogle Scholar
  14. 14.
    Chamberland M, Whittingstall K, Fortin D, Mathieu D, Descoteaux M (2014) Real-time multi-peak tractography for instantaneous connectivity display. Front Neuroinform 8:59. doi:10.3389/fninf.2014.00059 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Chamberland M, Bernier M, Fortin D, Whittingstall K, Descoteaux M (2015) 3D interactive tractography-informed resting-state fMRI connectivity. Front Neurosci 9:1–15. doi:10.3389/fnins.2015.00275 CrossRefGoogle Scholar
  16. 16.
    Wolfgang P (1994) Design patterns for object-oriented software development. Addison-Wesley, BostonGoogle Scholar
  17. 17.
    Horn BKP (1987) Closed-form solution of absolute orientation using unit quaternions. J Opt Soc Am A 4:629. doi:10.1364/JOSAA.4.000629 CrossRefGoogle Scholar
  18. 18.
    Gerard IJ, Hall J, Mok K, Collins DL (2015) New protocol for skin landmark registration in image-guided neurosurgery. Neurosurgery. doi:10.1227/NEU.0000000000000868 PubMedGoogle Scholar
  19. 19.
    Stieglitz LH, Fichtner J, Andres R, Schucht P, Krähenbühl A-K, Raabe A, Beck J (2013) The silent loss of neuronavigation accuracy: a systematic retrospective analysis of factors influencing the mismatch of frameless stereotactic systems in cranial neurosurgery. Neurosurgery 72:796–807. doi:10.1227/NEU.0b013e318287072d CrossRefPubMedGoogle Scholar
  20. 20.
    Zhang Z, Member S (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22:1330–1334. doi:10.1109/34.888718 CrossRefGoogle Scholar
  21. 21.
    Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7:308–313. doi:10.1093/comjnl/7.4.308 CrossRefGoogle Scholar
  22. 22.
    Drouin S, Kersten-Oertel M, Collins DL (2015) Interaction-based registration correction for improved augmented reality overlay in neurosurgery. Lecture notes on comput science. Augment Environ Comput Interv 9365:21–29. doi:10.1007/978-3-319-24601-7_3 CrossRefGoogle Scholar
  23. 23.
    Brown RA (1979) A stereotactic head frame for use with CT body scanners. Invest Radiol 14:300–304CrossRefPubMedGoogle Scholar
  24. 24.
    Comeau RM, Fenster A, Peters TM (1998) Integrated MR and ultrasound imaging for improved image guidance in neurosurgery. In: Hanson KM (ed) Med. Imaging ’98. International Society for Optics and Photonics, pp 747–754Google Scholar
  25. 25.
    Mercier L, Del Maestro RF, Petrecca K, Kochanowska A, Drouin S, Yan CXB, Janke AL, Chen SJ-S, Collins DL (2011) New prototype neuronavigation system based on preoperative imaging and intraoperative freehand ultrasound: system description and validation. Int J Comput Assist Radiol Surg 6:507–522CrossRefPubMedGoogle Scholar
  26. 26.
    De Nigris D, Collins DL, Arbel T (2012) Multi-modal image registration based on gradient orientations of minimal uncertainty. IEEE Trans Med Imaging 31:2343–2354. doi:10.1109/TMI.2012.2218116 CrossRefPubMedGoogle Scholar
  27. 27.
    De Nigris D, Collins DL, Arbel T (2013) Fast rigid registration of pre-operative magnetic resonance images to intra-operative ultrasound for neurosurgery based on high confidence gradient orientations. Int J Comput Assist Radiol Surg 8:649–661. doi:10.1007/s11548-013-0826-6 CrossRefPubMedGoogle Scholar
  28. 28.
    Mercier L, Del Maestro RF, Petrecca K, Araujo D, Haegelen C, Collins DL (2012) Online database of clinical MR and ultrasound images of brain tumors. Med Phys 39:3253–3261. doi:10.1118/1.4709600 CrossRefPubMedGoogle Scholar
  29. 29.
    Heinrich MP, Jenkinson M, Papiez BW, Brady M, Schnabel JA (2013) Towards realtime multimodal fusion for image-guided interventions using self-similarities. In: International conference on medical image computing and computer-assisted intervention, pp 187–194Google Scholar
  30. 30.
    Wein W, Ladikos A, Fuerst B, Shah A, Sharma K, Navab N (2013) Global registration of ultrasound to MRI using the LC2 metric for enabling neurosurgical guidance. In: Medical image computing and computer intervention. Lecture notes on computer science, pp 34–41Google Scholar
  31. 31.
    Ferrante E, Paragios N (2013) Non-rigid 2D-3D medical image registration using markov random fields. In: Medical image computing and computer-assisted intervention, pp 163–70Google Scholar
  32. 32.
    Fuerst B, Wein W, Muller M, Navab N (2014) Automatic ultrasound-MRI registration for neurosurgery using the 2D and 3D LC2 metric. Med Image Anal 18:1312–1319. doi:10.1016/j.media.2014.04.008 CrossRefPubMedGoogle Scholar
  33. 33.
    Farnia P, Ahmadian A, Shabanian T, Serej ND, Alirezaie J (2015) Brain-shift compensation by non-rigid registration of intra-operative ultrasound images with preoperative MR images based on residual complexity. Int J Comput Assist Radiol Surg 10:555–562. doi:10.1007/s11548-014-1098-5 CrossRefPubMedGoogle Scholar
  34. 34.
    Ferrante E, Fecamp V, Paragios N (2015) Slice-to-volume deformable registration: efficient one-shot consensus between plane selection and in-plane deformation. Int J Comput Assist Radiol Surg 10:791–800. doi:10.1007/s11548-015-1205-2 CrossRefPubMedGoogle Scholar
  35. 35.
    Rivaz H, Karimaghaloo Z, Collins DL (2014) Self-similarity weighted mutual information: a new nonrigid image registration metric. Med Image Anal 18:343–358. doi:10.1016/j.media.2013.12.003 CrossRefPubMedGoogle Scholar
  36. 36.
    Rivaz H, Karimaghaloo Z, Fonov VS, Collins DL (2014) Nonrigid registration of ultrasound and MRI using contextual conditioned mutual information. IEEE Trans Med Imaging 33:708–725. doi:10.1109/TMI.2013.2294630 CrossRefPubMedGoogle Scholar
  37. 37.
    Mercier L, Araujo D, Haegelen C, Del Maestro RF, Petrecca K, Collins DL (2013) Registering pre- and postresection 3-dimensional ultrasound for improved visualization of residual brain tumor. Ultrasound Med Biol 39:16–29. doi:10.1016/j.ultrasmedbio.2012.08.004 CrossRefPubMedGoogle Scholar
  38. 38.
    Rivaz H, Collins DL (2015) Near real-time robust non-rigid registration of volumetric ultrasound images for neurosurgery. Ultrasound Med Biol 41:574–587. doi:10.1016/j.ultrasmedbio.2014.08.013 CrossRefPubMedGoogle Scholar
  39. 39.
    Mercier L, Fonov V, Haegelen C, Del Maestro RF, Petrecca K, Collins DL (2012) Comparing two approaches to rigid registration of three-dimensional ultrasound and magnetic resonance images for neurosurgery. Int J Comput Assist Radiol Surg 7:125–136. doi:10.1007/s11548-011-0620-2 CrossRefPubMedGoogle Scholar
  40. 40.
    Kersten-Oertel M, Chen SJ, Collins DL (2014) An evaluation of depth enhancing perceptual cues for vascular volume visualization in neurosurgery. IEEE Trans Vis Comput Graph 20:391–403. doi:10.1109/TVCG.2013.240 CrossRefPubMedGoogle Scholar
  41. 41.
    Drouin S, Kersten-oertel M, Chen SJ, Collins DL (2012) A realistic test and development environment for mixed reality in neurosurgery. In: Augmented environments for computer-assisted interventions, pp 13–23Google Scholar
  42. 42.
    Kersten-Oertel M, Chen SSJ, Drouin S, Sinclair DS, Collins DL (2012) Augmented reality visualization for guidance in neurovascular surgery. Stud Health Technol Inform 173:225–229. doi:10.3233/978-1-61499-022-2-225 PubMedGoogle Scholar
  43. 43.
    Kersten-Oertel M, Gerard I, Drouin S, Mok K, Sirhan D, Sinclair DS, Collins DL (2015) Augmented reality in neurovascular surgery: feasibility and first uses in the operating room. Int J Comput Assist Radiol Surg 10:1823–1836. doi:10.1007/s11548-015-1163-8 CrossRefPubMedGoogle Scholar
  44. 44.
    Kersten-Oertel M, Gerard I, Drouin S, Mok K, Sirhan D, Sinclair D, Collins DL (2014) Augmented Reality in Neurovascular Surgery: First Experiences. In: Augmented environments for computer-assisted interventions. Lecture notes on computer science, vol 8678, pp 80–89Google Scholar
  45. 45.
    Kersten-oertel M, Gerard IJ, Drouin S, Mok K, Sirhan D, Sinclair D, Collins DL (2015) Augmented reality for specific neurovascular tasks. In: Augmented environments for computer-assisted interventions. Lecture notes on computer science, vol 9365, pp 92–103Google Scholar
  46. 46.
    Gerard IJ, Kersten-Oertel M, Drouin S, Hall J a., Petrecca K, De Nigris D, Arbel T, Collins DL (2015) Improving patient specific neurosurgical models with intraoperative ultrasound and augmented reality visualizations in a neuronavigation environment. In: Workshop on clinical image-based procedures: translational research in medical imaging. Lecture notes on computer science, pp 28–35Google Scholar
  47. 47.
    Langston TH, Kevin TF, Holly LT, Foley KT (2007) Image guidance in spine surgery. Orthop Clin North Am 38:451–461. doi:10.1016/j.ocl.2007.04.001 CrossRefGoogle Scholar
  48. 48.
    Yan CXB, Goulet B, Pelletier J, Chen SJ-S, Tampieri D, Collins DL (2011) Towards accurate, robust and practical ultrasound-CT registration of vertebrae for image-guided spine surgery. Int J Comput Assist Radiol Surg 6:523–537. doi:10.1007/s11548-010-0536-2 CrossRefPubMedGoogle Scholar
  49. 49.
    Yan CXB, Goulet B, Chen SJ-S, Tampieri D, Collins DL (2012) Validation of automated ultrasound-CT registration of vertebrae. Int J Comput Assist Radiol Surg 7:601–610. doi:10.1007/s11548-011-0666-1 CrossRefPubMedGoogle Scholar
  50. 50.
    Yan CXB, Goulet B, Tampieri D, Collins DL (2012) Ultrasound-CT registration of vertebrae without reconstruction. Int J Comput Assist Radiol Surg 7:901–909. doi:10.1007/s11548-012-0771-9 CrossRefPubMedGoogle Scholar
  51. 51.
    Bériault S, Al Subaie F, 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 7:687–704. doi:10.1007/s11548-012-0768-4 CrossRefPubMedGoogle Scholar
  52. 52.
    Bériault S, Sadikot AF, Alsubaie F, Drouin S, Collins DL, Pike GB (2014) Neuronavigation using susceptibility-weighted venography: application to deep brain stimulation and comparison with gadolinium contrast. J Neurosurg 0:1–11. doi:10.3171/2014.3.JNS131860 Google Scholar
  53. 53.
    Bériault S, Xiao Y, Collins D, Pike G (2015) Automatic SWI venography segmentation using conditional random fields. IEEE Trans Med Imaging 34:2478–2491. doi:10.1109/TMI.2015.2442236 CrossRefPubMedGoogle Scholar
  54. 54.
    Bériault S, Xiao Y, Bailey L, Collins DL, Sadikot AF, Pike GB (2012) Towards computer-assisted deep brain stimulation targeting with multiple active contacts. Med Image Comput Comput Assist Interv 15:487–494PubMedGoogle Scholar
  55. 55.
    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 10:1599–1615. doi:10.1007/s11548-015-1165-6 CrossRefPubMedGoogle Scholar
  56. 56.
    Bériault S, Drouin S, Sadikot AF, Xiao Y, Collins DL, Pike GB (2013) A prospective evaluation of computer-assisted deep brain stimulation trajectory planning. In: Clinical image-based procedures from planning to intervention. Lecture notes on computer science, vol 7761, pp 42–49Google Scholar
  57. 57.
    Association WM (2013) World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310:2191–2194. doi:10.1001/jama.2013.281053 CrossRefGoogle Scholar

Copyright information

© CARS 2016

Authors and Affiliations

  • Simon Drouin
    • 1
  • Anna Kochanowska
    • 1
  • Marta Kersten-Oertel
    • 1
  • Ian J. Gerard
    • 1
  • Rina Zelmann
    • 1
  • Dante De Nigris
    • 1
  • Silvain Bériault
    • 1
  • Tal Arbel
    • 1
  • Denis Sirhan
    • 1
  • Abbas F. Sadikot
    • 1
  • Jeffery A. Hall
    • 1
  • David S. Sinclair
    • 1
  • Kevin Petrecca
    • 1
  • Rolando F. DelMaestro
    • 1
  • D. Louis Collins
    • 1
  1. 1.McConnell Brain Imaging CenterMontreal Neurological Institute and HospitalMontrealCanada

Personalised recommendations