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Deformable registration of preoperative MR, pre-resection ultrasound, and post-resection ultrasound images of neurosurgery

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Sites that use ultrasound (US) in image-guided neurosurgery (IGNS) of brain tumors generally have three sets of imaging data: preoperative magnetic resonance (MR) image, pre-resection US, and post-resection US. The MR image is usually acquired days before the surgery, the pre-resection US is obtained after the craniotomy but before the resection, and finally, the post-resection US scan is performed after the resection of the tumor. The craniotomy and tumor resection both cause brain deformation, which significantly reduces the accuracy of the MR–US alignment.

Method

Three unknown transformations exist between the three sets of imaging data: MR to pre-resection US, pre- to post-resection US, and MR to post-resection US. We use two algorithms that we have recently developed to perform the first two registrations (i.e., MR to pre-resection US and pre- to post-resection US). Regarding the third registration (MR to post-resection US), we evaluate three strategies. The first method performs a registration between the MR and pre-resection US, and another registration between the pre- and post-resection US. It then composes the two transformations to register MR and post-resection US; we call this method compositional registration. The second method ignores the pre-resection US and directly registers the MR and post-resection US; we refer to this method as direct registration. The third method is a combination of the first and second: it uses the solution of the compositional registration as an initial solution for the direct registration method. We call this method group-wise registration.

Results

We use data from 13 patients provided in the MNI BITE database for all of our analysis. Registration of MR and pre-resection US reduces the average of the mean target registration error (mTRE) from 4.1 to 2.4 mm. Registration of pre- and post-resection US reduces the average mTRE from 3.7 to 1.5 mm. Regarding the registration of MR and post-resection US, all three strategies reduce the mTRE. The initial average mTRE is 5.9 mm, which reduces to 3.3 mm with the compositional method, 2.9 mm with the direct technique, and 2.8 mm with the group-wise method.

Conclusion

Deformable registration of MR and pre- and post-resection US images significantly improves their alignment. Among the three methods proposed for registering the MR to post-resection US, the group-wise method gives the lowest TRE values. Since the running time of all registration algorithms is less than 2 min on one core of a CPU, they can be integrated into IGNS systems for interactive use during surgery.

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References

  1. Hill D, Maurer C, Maciunas R, Barwise J, Fitzpatrick M, Wang M (1998) Measurement of intraoperative brain surface deformation under a craniotomy. Neurosurgery 43(3):514–526

    Article  CAS  PubMed  Google Scholar 

  2. Roberts D, Hartov A, Kennedy F, Miga M, Paulsen K (1998) Intraoperative brain shift and deformation: a quantitative analysis of cortical displacement in 28 cases. Neurosurgery 43(4):749–758

    Article  CAS  PubMed  Google Scholar 

  3. Ji S, Fan X, Hartov A, Roberts DW, Paulsen KD (2012) Estimation of intraoperative brain deformation. In: Soft tissue biomechanical modeling for computer assisted surgery. Springer, New York, pp 97–133

  4. Kuhnt D, Bauer MH, Nimsky C (2012) Brain shift compensation and neurosurgical image fusion using intraoperative MRI: current status and future challenges. Crit Rev Biomed Eng 40(3):175–185

  5. Nabavi A, Black P et al (2001) Serial intraoperative magnetic resonance imaging of brain shift. Neurosurgery 48(4):787–798

    CAS  PubMed  Google Scholar 

  6. Nimsky C, Fujita A, Ganslandt O, Von Keller B, Fahlbusch R (2004) Volumetric assessment of glioma removal by intraoperative high-field magnetic resonance imaging. Neurosurgery 55(4):358–370

    Article  PubMed  Google Scholar 

  7. Truwit C, Martin AJ, Hall WA (2012) MRI guidance of minimally invasive cranial applications. In: Interventional magnetic resonance imaging. Springer, New York, pp 97–112

  8. Giordano M, Gerganov VM, Metwali H, Fahlbusch R, Samii A, Samii M, Bertalanffy H (2014) Feasibility of cervical intramedullary diffuse glioma resection using intraoperative magnetic resonance imaging. Neurosurg Rev 37(1):139–146

    Article  Google Scholar 

  9. Czyż M, Tabakow P, Weiser A, Lechowicz-Głogowska B, Zub L, Jarmundowicz W (2014) The safety and effectiveness of low field intraoperative mri guidance in frameless stereotactic biopsies of brain tumoursdesign and interim analysis of a prospective randomized trial. Neurosurg Rev 37(1):127–137

    Article  PubMed Central  PubMed  Google Scholar 

  10. Sommer B, Grummich P, Coras R, Kasper BS, Blumcke I, Hamer HM, Stefan H, Buchfelder M, Roessler K (2013) Integration of functional neuronavigation and intraoperative mri in surgery for drug-resistant extratemporal epilepsy close to eloquent brain areas. Neurosurg Focus 34(4):E4

    Article  PubMed  Google Scholar 

  11. Black P, Jolesz FA, Medani K (2011) From vision to reality: the origins of intraoperative mr imaging. In: Intraoperative imaging. Springer, New York, pp 3–7

  12. Keles G, Lamborn K, Berger S (2003) Coregistration accuracy and detection of brain shift using intraoperative sononavigation during resection of hemispheric tumors. Neurosurgery 53:556–562

    Article  PubMed  Google Scholar 

  13. Unsgaard A, Selbekk T, Gronningsaeter S, Ommedal S, Nagelhus H (2005) Ability of navigated 3D ultra-sound to delineate gliomas and metastases: comparison of image interpretations with histopathology. Acta Neurochir 147(4):1259–1269

    Article  CAS  PubMed  Google Scholar 

  14. Letteboer M, Willems P, Viergever M, Niessen W (2005) Brain shift estimation in image-guided neurosurgery using 3-d ultrasound. IEEE Trans Med Imag 52:267–276

    Google Scholar 

  15. Rygh O, Selbekk T, Torp S, Lydersen S, Hernes T, Unsgaard G (2005) Comparison of navigated 3D ultrasound findings with histopathology in subsequent phases of glioblastoma resection. Acta Neurochirur 150(10):1033–1042

    Article  Google Scholar 

  16. Miller D, Benes L, Sure U (2011) Stand-alone 3d-ultrasound navigation after failure of conventional image guidance for deep-seated lesions. Neurosurg Rev 34(3):381–388

    Article  PubMed  Google Scholar 

  17. Ji S, Fan X, Roberts DW, Hartov A, Paulsen KD (2011) Optimizing nonrigid registration performance between volumetric true 3d ultrasound images in image-guided neurosurgery. In: SPIE medical imaging. International Society for Optics and Photonics, pp 79640V–79640V

  18. Ji S, Roberts DW, Hartov A, Paulsen KD et al (2012) Intraoperative patient registration using volumetric true 3d ultrasound without fiducials. Med Phys 39(12):7540

    Article  PubMed Central  PubMed  Google Scholar 

  19. Mercier L, Fonov V, Haegelen C, Maesstro R, Petrecca K, Collins DL (2012) Comparing two approaches to rigid registration of three-dimensional ultrasound and magnetic resonance images for neurosurgery. Compt Aided Surg 7(1):125–136

    Google Scholar 

  20. Selbekk T, Jakola AS, Solheim O, Johansen TF, Lindseth F, Reinertsen I, Unsgård G (2013) Ultrasound imaging in neurosurgery: approaches to minimize surgically induced image artefacts for improved resection control. Acta Neurochirur 155(6):973–980

    Article  Google Scholar 

  21. Reinertsen I, Jakola A, Friderichsen P, Lindseth F, Solheim O, Selbekk T, Unsgård G (2012) A new system for 3d ultrasound-guided placement of cerebral ventricle catheters. Int J Comput Assist Radiol Surg 7(1):151–157

    Article  CAS  PubMed  Google Scholar 

  22. 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

    Article  PubMed  Google Scholar 

  23. Fan X, Ji S, Fontaine K, Hartov A, Roberts D, Paulsen K (2011) Simulation of brain tumor resection in image-guided neurosurgery. In: SPIE medical imaging. International society for optics and photonics, pp 79640U–79640U

  24. Fan X, Ji S, Hartov A, Roberts D, Paulsen K (2013) Retractor-induced brain shift compensation in image-guided neurosurgery. In: SPIE medical imaging. International Society for Optics and Photonics, pp 86710K–86710K

  25. Rivaz H, Boctor EM, Choti MA, Hager GD (2014) Ultrasound elastography using multiple images. Med Image Anal 18(2):314–329

    Article  PubMed  Google Scholar 

  26. Rivaz H, Collins DL (2014) Near real-time robust nonrigid registration of volumetric ultrasound images for neurosurgery. Ultrasound Med Biol. doi:10.1016/j.ultrasmedbio.2014.08.013

  27. Rivaz H, Chen S, Collins DL (2014) Automatic deformable mr-ultrasound registration for image-guided neurosurgery. IEEE Trans Med Imaging. doi:10.1109/TMI.2014.2354352

  28. Roche A, Pennec X, Malandain G, Ayache N (2001) Rigid registration of 3-d ultrasound with mr images: a new approach combining intensity and gradient information. IEEE Trans Med Imag 20(72):291–237

    Google Scholar 

  29. Arbel T, Morandi X, Comeau R, Collins DL (2004) Automatic non-linear mri-ultrasound registration for the correction of intra-operative brain deformations. Comput Aided Surg 9(4):123–136

    Article  PubMed  Google Scholar 

  30. Collins DL, Neelin P, Peters TM, Evans AC (1994) Automatic 3d intersubject registration of mr volumetric data in standardized talairach space. J Comput Assist Tomogr 18(2):192–205

    Article  CAS  PubMed  Google Scholar 

  31. Kuklisova-Murgasova M, Cifor A, Napolitano R, Papageorghiou A, Quaghebeur G, Rutherford MA, Hajnal JV, Alison Noble J, Schnabel JA (2013) Registration of 3d fetal neurosonography and mri. Med Image Anal 17(8):1137–1150

  32. Penney G, Blackall J, Hamady M, Sabharwal T, Adam A, Hawks D (2004) Registration of freehand 3d ultrasound and magnetic resonance liver images. Med Imag Anal 8(1):81–91

    Article  CAS  Google Scholar 

  33. Ji S, Wu Z, Hartov A, Roberts D, Paulsen K (2008) Mutual-information-based image to patient re-registration using intraoperative ultrasound in image-guided neurosurgery. Med Phys 35(10):4612–4624

    Article  PubMed Central  PubMed  Google Scholar 

  34. Hartov A, Roberts DW, Paulsen KD (2008) A comparative analysis of coregistered ultrasound and magnetic resonance imaging in neurosurgery. Neurosurgery 62(3):91–101

    Article  PubMed  Google Scholar 

  35. Brooks R, Collins DL, Morandi X, Arbel T (2008) Deformable ultrasound registration without reconstruction. In: Medical image computing and computer-assisted intervention-MICCAI 2008. Springer, New York, pp 1023–1031

  36. Zhang W, Brady M, Becher H, Noble A (2011) Spatio-temporal (2d+t) non-rigid registration of real-time 3d echocardiography and cardiovascular mr image sequences. Phys Med Biol 56:1341–1360

    Article  PubMed  Google Scholar 

  37. Kybic J, Unser M (2003) Fast parametric elastic image registration. IEEE Trans Med Imaging 12:1427–1442

    Google Scholar 

  38. Rivaz H, Boctor EM, Choti MA, Hager GD (2011) Real-time regularized ultrasound elastography. IEEE Trans Med Imaging 30(4):928–945

    Article  PubMed  Google Scholar 

  39. Jannin P, Fitzpatrick JM, Hawkes DJ, Pennec X, Shahidi R, Vannier MW (2002) Validation of medical image processing in image-guided therapy. Neurosurgery 21(2):1445–1449

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank anonymous reviewers for their constructive feedback. This work was financed by the Fonds Québécois de la recherche sur la nature et les technologies, the Canadian Institute of Health Research (MOP-97820), and the Natural Science and Engineering Research Council of Canada. H. Rivaz is supported by a postdoctoral fellowship from the Natural Sciences and Engineering Research Council of Canada.

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Authors and Affiliations

  1. PERFORM Centre, Department of Electrical and Computer Engineering, Concordia University, 1455 Maisonneuve West, Montreal, QC, H3G 1M8, Canada

    Hassan Rivaz

  2. McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), McGill University, 3801 University Street, Montreal, QC, H3A 2B4, Canada

    D. Louis Collins

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Correspondence to Hassan Rivaz or D. Louis Collins.

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Rivaz, H., Collins, D.L. Deformable registration of preoperative MR, pre-resection ultrasound, and post-resection ultrasound images of neurosurgery. Int J CARS 10, 1017–1028 (2015). https://doi.org/10.1007/s11548-014-1099-4

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  • DOI: https://doi.org/10.1007/s11548-014-1099-4

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