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International Journal of Computer Assisted Radiology and Surgery
Published in: International Journal of Computer Assisted Radiology and Surgery 10/2018

01-10-2018 | Original Article

Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching

Authors: Inês Machado, Matthew Toews, Jie Luo, Prashin Unadkat, Walid Essayed, Elizabeth George, Pedro Teodoro, Herculano Carvalho, Jorge Martins, Polina Golland, Steve Pieper, Sarah Frisken, Alexandra Golby, William Wells III

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 10/2018

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Abstract

Purpose

The brain undergoes significant structural change over the course of neurosurgery, including highly nonlinear deformation and resection. It can be informative to recover the spatial mapping between structures identified in preoperative surgical planning and the intraoperative state of the brain. We present a novel feature-based method for achieving robust, fully automatic deformable registration of intraoperative neurosurgical ultrasound images.

Methods

A sparse set of local image feature correspondences is first estimated between ultrasound image pairs, after which rigid, affine and thin-plate spline models are used to estimate dense mappings throughout the image. Correspondences are derived from 3D features, distinctive generic image patterns that are automatically extracted from 3D ultrasound images and characterized in terms of their geometry (i.e., location, scale, and orientation) and a descriptor of local image appearance. Feature correspondences between ultrasound images are achieved based on a nearest-neighbor descriptor matching and probabilistic voting model similar to the Hough transform.

Results

Experiments demonstrate our method on intraoperative ultrasound images acquired before and after opening of the dura mater, during resection and after resection in nine clinical cases. A total of 1620 automatically extracted 3D feature correspondences were manually validated by eleven experts and used to guide the registration. Then, using manually labeled corresponding landmarks in the pre- and post-resection ultrasound images, we show that our feature-based registration reduces the mean target registration error from an initial value of 3.3 to 1.5 mm.

Conclusions

This result demonstrates that the 3D features promise to offer a robust and accurate solution for 3D ultrasound registration and to correct for brain shift in image-guided neurosurgery.
Literature
1.
Bucholz RD, Smith KR, Laycock KA, McDurmont LL (2001) Three-dimensional localization: from image-guided surgery to information-guided therapy. Methods 25(2):186–200CrossRefPubMed
2.
Hill DG, Maurer CR, Maciunas RJ, Barwise JA, Fitzpatrick JM, Wang MY (1998) Measurement of intraoperative brain surface deformation under a craniotomy. Neurosurgery 43(3):514–528CrossRefPubMed
3.
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 43:749–758CrossRefPubMed
4.
Letteboer MMJ, Willems PW, Viergever MA, Niessen WJ (2005) Brain shift estimation in image-guided neurosurgery using 3-D ultrasound. IEEE Trans Biomed Eng 52(2):268–276CrossRefPubMed
5.
Audette MA, Siddiqi K, Ferrie FP, Peters TM (2003) An integrated range-sensing, segmentation and registration framework for the characterization of intra-surgical brain deformations in image-guided surgery. Comput Vis Image Underst 89(2–3):226–251CrossRef
6.
Marko NF, Weil RJ, Schroeder JL, Lang FF, Suki D, Sawaya RE (2014) Extent of resection of glioblastoma revisited: personalized survival modeling facilitates more accurate survival prediction and supports a maximum-safe-resection approach to surgery. J Clin Oncol 32(8):774CrossRefPubMedPubMedCentral
7.
Coburger J, Merkel A, Scherer M, Schwartz F, Gessler F, Roder C, Jungk C (2015) Low-grade glioma surgery in intraoperative magnetic resonance imaging: results of a multicenter retrospective assessment of the German Study Group for Intraoperative Magnetic Resonance Imaging. Neurosurgery 78(6):775–786CrossRef
8.
Brown TJ, Brennan MC, Li M, Church EW, Brandmeir NJ, Rakszawski KL, Glantz M (2016) Association of the extent of resection with survival in glioblastoma: a systematic review and meta-analysis. JAMA Oncol 2(11):1460–1469CrossRefPubMedPubMedCentral
9.
Almeida JP, Chaichana KL, Rincon-Torroella J, Quinones-Hinojosa A (2015) The value of extent of resection of glioblastomas: clinical evidence and current approach. Curr Neurol Neurosci Rep 15(2):517CrossRefPubMed
10.
Hatiboglu MA, Weinberg JS, Suki D, Rao G, Prabhu SS, Shah K, Jackson E, Sawaya R (2009) Impact of intra-operative high-field magnetic resonance imaging guidance on glioma surgery: a prospective volumetric study. Neurosurgery 64:1073–1081CrossRefPubMed
11.
Claus EB, Horlacher A, Hsu L, Schwartz RB, Dello-Iacono D, Talos F, Jolesz FA, Black PM (2005) Survival rates in patients with low-grade glioma after intra-operative magnetic resonance image guidance. Cancer 103:1227–1233CrossRefPubMed
12.
Nabavi A, Black PM, Gering DT, Westin CF, Mehta V, Pergolizzi RS Jr, Ferrant M, Warfield SK, Hata N, Schwartz RB, Wells WM, Kikinis R, Jolesz F (2001) Serial intra-operative magnetic resonance imaging of brain shift. Neurosurgery 48:787–797PubMed
13.
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 40:175–185CrossRefPubMed
14.
Tirakotai D, Miller S, Heinze L, Benes L, Bertalanffy H, Sure U (2006) A novel platform for image-guided ultrasound. Neurosurgery 58:710–718CrossRefPubMed
15.
Zhou H, Rivaz H (2016) Registration of pre-and postresection ultrasound volumes with noncorresponding regions in neurosurgery. IEEE J Biomed Health Inform 20(5):1240–1249CrossRefPubMed
16.
Rivaz H, Collins DL (2015) Near real-time robust non-rigid registration of volumetric ultrasound images for neurosurgery. Ultrasound Med Biol 41(2):574–587CrossRefPubMed
17.
Mercier L, Araujo D, Haegelen C, Del Maestro RF, Petrecca K, Collins DL (2013) Registering pre- and post-resection 3-dimensional ultrasound for improved visualization of residual brain tumor. Ultrasound Med Biol 39(1):16–29CrossRefPubMed
18.
Gerard IJ, Kersten-Oertel M, Petrecca K, Sirhan D, Hall JA, Collins DL (2017) Brain shift in neuronavigation of brain tumors: a review. Med Image Anal 35:403–420CrossRefPubMed
19.
Pheiffer TS, Thompson RC, Rucker DC, Simpson AL, Miga MI (2014) Model-based correction of tissue compression for tracked ultrasound in soft tissue image-guided surgery. Ultrasound Med Biol 40(4):788–803CrossRefPubMedPubMedCentral
20.
Morin F, Chabanas M, Courtecuisse H, Payan Y (2017) Biomechanical modeling of brain soft tissues for medical applications. Biomechanics of living organs. Academic Press, Cambridge, pp 127–146
21.
Morin F, Courtecuisse H, Reinertsen I, Lann FL, Palombi O, Payan Y, Chabanas M (2017) Brain-shift compensation using intraoperative ultrasound and constraint-based biomechanical simulation. Med Image Anal 40:133–153 ISSN 1361-8415CrossRefPubMed
22.
Luo M, Frisken SF, Weis JA, Clements LW, Unadkat P, Thompson RC, Golby AJ, Miga MI (2017) Validation of model-based brain shift correction in neurosurgery via intraoperative magnetic resonance imaging: preliminary results. In: Proceedings of SPIE 10135, medical imaging: image-guided procedures, robotic interventions, and modeling
23.
Rivaz H, Collins DL (2015) Deformable registration of preoperative MR, pre-resection ultrasound, and post-resection ultrasound images of neurosurgery. Int J Comput Assist Radiol Surg 10(7):1017–1028CrossRefPubMed
24.
Blumenthal T, Hartov A, Lunn K, Kennedy FE, Roberts DW, Paulsen KD (2005) Quantifying brain shift during neurosurgery using spatially tracked ultrasound. In: Proceedings of SPIE 5744, medical imaging: visualization, image-guided procedures, and display
25.
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(1):125–136CrossRefPubMed
26.
Poon T, Rohling R (2006) Three-dimensional extended field-of-view ultrasound. Ultrasound Med Biol 32:357–369CrossRefPubMed
27.
Schers J, Troccaz J, Daanen V, Fouard C, Plaskos C, Kilian P (2007) 3D/4D ultrasound registration of bone. In: Proceedings of the IEEE ultrasonics symposium, pp 2519–2522
28.
Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE TPAMI 24(5):603–619CrossRef
29.
Grimson WEL, Lozano-Perez T (1987) Localizing overlapping parts by searching the interpretive tree. IEEE TPAMI 9(4):469–482CrossRef
30.
Beis JS, Lowe DG (1997) Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In: CVPR, pp 1000–1006
31.
Biederman I (1987) Recognition-by-components: a theory of human image understanding. Psychol Rev 2(94):115–147CrossRef
32.
Pratikakis I (2003) Robust multiscale deformable registration of 3D ultrasound images. Int J Image Graph 3:547–565CrossRef
33.
Cen F, Jiang Y, Zhang Z, Tsui HT, Lau TK, Xie H (2004) Robust registration of 3D-ultrasound images based on gabor filter and mean-shift. In: Method, pp 304–316
34.
Schneider RJ, Perrin DP, Vasilyev NV, Marx GR, Pedro J, Howe RD (2012) Real-time image-based rigid registration of three-dimensional ultrasound. Med Image Anal 16:402–414CrossRefPubMed
35.
Toews M, Wells WM III (2013) Efficient and robust model-to-image registration using 3D scale-invariant features. Med Image Anal 17(3):271–282CrossRefPubMed
36.
Ni D, Qu Y, Yang X, Chui YP, Wong TT, Ho SS, Heng PA (2008) Volumetric ultrasound panorama based on 3D SIFT. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 52-60CrossRef
37.
Bersvendsen J, Toews M, Danudibroto A, Wells WM, Urheim S, Estépar RSJ, Samset E (2016) Robust spatio-temporal registration of 4D cardiac ultrasound sequences. In: Medical imaging 2016: ultrasonic imaging and tomography, vol. 9790. International society for optics and photonics, p 97900F
38.
Kadir T, Brady M (2001) Saliency, scale and image description. Int J Comput Vis 45(2):83–105CrossRef
39.
Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell 27(10):1615–1630CrossRefPubMed
40.
Toews M, Wells WM (2009) Sift-rank: ordinal description for invariant feature correspondence. In: IEEE conference on computer vision and pattern recognition, 2009. CVPR 2009, pp 172–177
41.
Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5(2):143–156CrossRefPubMed
42.
Bookstein FL (1989) Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans Pattern Anal Mach Intell 11(6):567–585CrossRef
43.
Rohr K, Stiehl HS, Sprengel R, Beil W, Buzug TM, Weese J, Kuhn MH (1996) Point-based elastic registration of medical image data using approximating thin-plate splines. In: Visualization in biomedical computing. Springer, Berlin, pp 297–306
44.
Tempany C, Jayender J, Kapur T, Bueno R, Golby A, Agar N, Jolesz FA (2015) Multimodal imaging for improved diagnosis and treatment of cancers. Cancer 121(6):817–827CrossRefPubMed
45.
Strong EB, Rafii A, Holhweg-Majert B, Fuller SC, Metzger MC (2008) Comparison of 3 optical navigation systems for computer-aided maxillofacial surgery. Arch Otolaryngol Head Neck Surg 134(10):1080–1084CrossRefPubMed
46.
Tokuda J, Fischer GS, Papademetris X, Yaniv Z, Ibanez L, Cheng P, Liu H, Blevins J, Arata J, Golby AJ, Kapur T, Pieper S, Burdette EC, Fichtinger G, Tempany CM, Hata N (2009) OpenIGTLink: an open network protocol for image-guided therapy environment. Int J Med Robot 5(4):423–34CrossRefPubMedPubMedCentral
47.
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(10):2527–2537CrossRefPubMedPubMedCentral
48.
Gobbi DG, Peters TM (2002) Interactive intra-operative 3D ultrasound reconstruction and visualization. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 156–163CrossRef
49.
Boisvert J, Gobbi D, Vikal S, Rohling R, Fichtinger G, Abolmaesumi P (2008) An open-source solution for interactive acquisition, processing and transfer of interventional ultrasound images. In: The MIDAS journal-systems and architectures for computer assisted interventions, p 70
50.
51.
Kikinis R, Pieper SD, Vosburgh K (2014) 3D Slicer: a platform for subject-specific image analysis, visualization, and clinical support. Intraoperative Imaging Image Guided Ther 3(19):277–289. ISBN: 978-1-4614-7656-6
52.
Jannin P, Fitzpatrick JM, Hawkes D, Pennec X, Shahidi R, Vannier M (2002) Validation of medical image processing in image-guided therapy. IEEE Trans Med Imaging 21(12):1445–9CrossRefPubMed
53.
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(6Part1):3253–3261CrossRefPubMed
54.
Xiao Y, Fortin M, Unsgård G, Rivaz H, Reinertsen I (2017) REtroSpective Evaluation of Cerebral Tumors (RESECT): a clinical database of pre-operative MRI and intra-operative ultrasound in low-grade glioma surgeries. Med Phys 44(7):3875–3882CrossRefPubMed
Metadata
Title
Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching
Authors
Inês Machado
Matthew Toews
Jie Luo
Prashin Unadkat
Walid Essayed
Elizabeth George
Pedro Teodoro
Herculano Carvalho
Jorge Martins
Polina Golland
Steve Pieper
Sarah Frisken
Alexandra Golby
William Wells III
Publication date
01-10-2018
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 10/2018
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-018-1786-7

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