Advertisement

Regional-surface-based registration for image-guided neurosurgery: effects of scan modes on registration accuracy

  • Yuan Dong
  • Chenxi ZhangEmail author
  • Dafeng Ji
  • Manning Wang
  • Zhijian SongEmail author
Original Article

Abstract

Purpose

The conventional surface-based method only registers the facial zone with preoperative point cloud, resulting in low accuracy away from the facial area. Acquiring a point cloud of the entire head for registration can improve registration accuracy in all parts of the head. However, it takes a long time to collect a point cloud of the entire head. It may be more practical to selectively scan part of the head to ensure high registration accuracy in the surgical area of interest. In this study, we investigate the effects of different scan regions on registration errors in different target areas when using a surface-based registration method.

Methods

We first evaluated the correlation between the laser scan resolution and registration accuracy to determine an appropriate scan resolution. Then, with the appropriate resolution, we explored the effects of scan modes on registration error in computer simulation experiments, phantom experiments and two clinical cases. The scan modes were designed based on different combinations of five zones of the head surface, i.e., the sphenoid-frontal zone, parietal zone, left temporal zone, right temporal zone and occipital zone. In the phantom experiment, a handheld scanner was used to acquire a point cloud of the head. A head model containing several tumors was designed, enabling us to calculate the target registration errors deep in the brain to evaluate the effect of regional-surface-based registration.

Result

The optimal scan modes for tumors located in the sphenoid-frontal, parietal and temporal areas are mode 4 (i.e., simultaneously scanning the sphenoid-frontal zone and the temporal zone), mode 4 and mode 6 (i.e., simultaneously scanning the sphenoid-frontal zone, the temporal zone and the parietal zone), respectively. For the tumor located in the occipital area, no modes were able to achieve reliable accuracy.

Conclusion

The results show that selecting an appropriate scan resolution and scan mode can achieve reliable accuracy for use in sphenoid-frontal, parietal and temporal area surgeries while effectively reducing the operation time.

Keywords

Image-guided neurosurgery Regional-surface-based registration Point-based registration Target registration error 

Notes

Funding

This study was funded by the Shanghai Natural Science Foundation (Grant No. 17ZR1401500), and by the National Natural Science Foundation of China (Grant No. 81471758).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

References

  1. 1.
    Peters TM (2006) Image-guidance for surgical procedures. Phys Med Biol 51(14):R505–R540CrossRefGoogle Scholar
  2. 2.
    Maurer CR, Fitzpatrick JM, Wang MY, Galloway RL, Maciunas RJ, Allen GS (1997) Registration of head volume images using implantable fiducial markers. IEEE Trans Med Imaging 16:447–462CrossRefGoogle Scholar
  3. 3.
    Mascott CR, Sol JC, Bousquet P, Lagarrigue J, Lazorthes Y, Lauwers-Cances V (2006) Quantification of true in vivo application accuracy in cranial image-guided surgery: influence of mode of patient registration. Neurosurgery 59:146–156Google Scholar
  4. 4.
    Mascott CR (2006) In vivo accuracy of image guidance performed using optical tracking and optimized registration. J Neurosurg 105:561–567CrossRefGoogle Scholar
  5. 5.
    Manning W, Zhijian S (2010) Distribution templates of the fiducial points in image-guided neurosurgery. Neurosurgery 66:143–151Google Scholar
  6. 6.
    Schonemann PH (1966) A generalized solution of the orthogonal Procrustes problem. Psychometrika 31:1–10CrossRefGoogle Scholar
  7. 7.
    Arun K, Huang T, Blostein SD (1987) Least-squares fitting of two 3D point sets. IEEE Trans Pattern Anal Mach Intell 9:699–700Google Scholar
  8. 8.
    Cao A, Thompson RC, Dumpuri P (2008) Laser range scanning for image-guided neurosurgery: investigation of image-to-physical space registrations. Med Phys 35:593–1605Google Scholar
  9. 9.
    Ji S, Roberts DW, Hartov A, Paulsen KD (2012) Intraoperative patient registration using volumetric true 3D ultrasound without fiducials. Med Phys 39:7540–7552CrossRefGoogle Scholar
  10. 10.
    Fan Y, Lüth T, Ji S, Hartov A, Paulsen KD (2015) Intraoperative fiducial-less patient registration using volumetric 3D ultrasound: a prospective series of 32 neurosurgical cases. J Neurosurg 123(3):721–731CrossRefGoogle Scholar
  11. 11.
    Wang MN, Song ZJ (2011) Properties of the target registration error for surface matching in neuronavigation. Comput Aided Surg 16:161–169CrossRefGoogle Scholar
  12. 12.
    Fan Y, Jiang D, Wang M, Song Z (2014) A new markerless patient-to-image registration method using a portable 3D scanner. Med Phys 41:101910CrossRefGoogle Scholar
  13. 13.
    Liu Y, Song Z, Wang M (2017) A, new robust markerless method for automatic image-to-patient registration in image-guided neurosurgery system. Comput Assist Surg 22:319CrossRefGoogle Scholar
  14. 14.
    Miga MI, Sinha TK, Cash DM, Galloway RL, Weil RJ (2003) Cortical surface registration for image-guided neurosurgery using laser-range scanning. IEEE Trans Med Imaging 22:973–985CrossRefGoogle Scholar
  15. 15.
    Marmulla R, Muhling J, Wirtz CR, Hassfeld S (2004) High-resolution laser surface scanning for patient registration in cranial computer-assisted surgery. Minim Invasive Neurosurg 47:72–78CrossRefGoogle Scholar
  16. 16.
    Schicho K, Figl M, Seemann R, Donat M, Pretterklieber ML, Birkfellner W, Reichwein A, Wanschitz F, Kainberger F, Bergmann H (2007) Comparison of laser surface scanning and fiducial marker-based registration in frameless stereotaxy: technical note. J Neurosurg 106:704–709CrossRefGoogle Scholar
  17. 17.
    Woerdeman PA, Willems PW, Noordmans HJ, Tulleken CA, van der Sprenkel JWB (2007) Application accuracy in frameless image-guided neurosurgery: a comparison study of three patient-to-image registration methods. J Neurosurg 106:1012–1016CrossRefGoogle Scholar
  18. 18.
    Paraskevopoulos D, Unterberg A, Metzner R, Dreyhaupt J, Eggers G, Wirtz CR (2011) Comparative study of application accuracy of two frameless neuronavigation systems: experimental error assessment quantifying registration methods and clinically influencing factors. Neurosurg Rev 34:217–228CrossRefGoogle Scholar
  19. 19.
    Bucholz R, Macneil W, Fewings P, Ravindra A, Mcdurmont L, Baumann C (2000) Automated rejection of contaminated surface measurements for improved surface registration in image guided neurosurgery. Stud Health Technol Inf 70:39–45Google Scholar
  20. 20.
    Raabe A, Krishnan R, Wolff R, Hermann E, Zimmermann M (2002) Laser surface scanning for patient registration in intracranial image-guided surgery. Neurosurgery 50:802–803Google Scholar
  21. 21.
    Marmulla R, Lüth T, Mühlin J, Hassfeld S (2004) Automated laser registration in image-guided surgery: evaluation of the correlation between laser scan resolution and navigation accuracy. Int J Oral Maxillofac Surg 33:642–648CrossRefGoogle Scholar

Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.Digital Medical Research Center, School of Basic Medical SciencesFudan UniversityShanghaiChina
  2. 2.Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted InterventionShanghaiChina
  3. 3.Anatomy Department of Medical SchoolNantong UniversityNantongChina

Personalised recommendations