Abstract
Purpose
We present a novel approach for the registration of pre-operative magnetic resonance images to intra-operative ultrasound images for the context of image-guided neurosurgery.
Method
Our technique relies on the maximization of gradient orientation alignment in a reduced set of high confidence locations of interest and allows for fast, accurate, and robust registration. Performance is compared with multiple state-of-the-art techniques including conventional intensity-based multi-modal registration strategies, as well as other context-specific approaches. All methods were evaluated on fourteen clinical neurosurgical cases with brain tumors, including low-grade and high-grade gliomas, from the publicly available MNI BITE dataset. Registration accuracy of each method is evaluated as the mean distance between homologous landmarks identified by two or three experts. We provide an analysis of the landmarks used and expose some of the limitations in validation brought forward by expert disagreement and uncertainty in identifying corresponding points.
Results
The proposed approach yields a mean error of 2.57 mm across all cases (the smallest among all evaluated techniques). Additionally, it is the only evaluated technique that resolves all cases with a mean distance of less than 1 mm larger than the theoretical minimal mean distance when using a rigid transformation.
Conclusion
Finally, our proposed method provides reduced processing times with an average registration time of 0.76 s in a GPU-based implementation, thereby facilitating its integration into the operating room.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Note that the third expert in the MNI BITE dataset identified homologous landmarks for the first six cases only.
References
Automatic non-linear mri-ultrasound registration for the correction of intra-operative brain deformations. In: Niessen W, Viergever M (eds) Medical image computing and computer-assisted intervention- MICCAI 2001, Lecture Notes in Computer Science, vol. 2208, pp 913–922. Springer, Berlin (2001)
Beyond mutual information: a simple and robust alternative. In: Meinzer HP, Handels H, Horsch A, Tolxdorff T (eds) Bildverarbeitung für die Medizin 2005, Informatik aktuell, pp 350–354. Springer, Berlin (2005)
Brooks R, Collins D, Morandi X, Arbel T (2008) Deformable ultrasound registration without reconstruction. In: Medical image computing and computer-assisted intervention-MICCAI 2008, Lecture Notes in Computer Science, vol. 5242, pp 1023–1031. Springer, Berlin
Dai Y, Tian J, Dong D, Yan G, Zheng H (2010) Real-time visualized freehand 3d ultrasound reconstruction based on gpu. IEEE Trans Inf Technol Biomed 14(6):1338–1345
De Nigris D, Collins D, Arbel T (2012) Fast and robust registration based on gradient orientations: case study matching intra-operative ultrasound to pre-operative mri in neurosurgery. Springer, Berlin, pp 125–134
De Nigris D, Collins DL, Arbel T (2012) Multi-modal image registration based on gradient orientations of minimal uncertainty. IEEE Trans Med Imaging PP(99):1
De Nigris D, Mercier L, Del Maestro R, Collins DL, Arbel T (2010) Hierarchical multimodal image registration based on adaptive local mutual information. In: Medical image computing and computer-assisted intervention-MICCAI 2010, Lecture Notes in Computer Science, vol. 6362, pp 643–651. Springer, Berlin
Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. Evolut Comput 9:159–195
Heinrich M, Schnabel J, Gleeson F, Brady F, Jenkinson M (2010) Non-rigid multimodal medical image registration using optical flow and gradient orientation. In: Proceedings of medical image understanding and analysis
Ji S, Wu Z, Hartov A, Roberts DW, Paulsen KD (2008) Mutual-information-based image to patient re-registration using intraoperative ultrasound in image-guided neurosurgery. Med Phys 35(10):4612–4624
Karaçali B (2007) Information theoretic deformable registration using local image information. Int J Comput Vis 72(3):219–237
Letteboer M, Willems P, Viergever M, Niessen W (2005) Brain shift estimation in image-guided neurosurgery using 3-d ultrasound. IEEE Trans Biomed Eng 52(2):268–276
Loupas T, McDicken W, Allan P (1989) An adaptive weighted median filter for speckle suppression in medical ultrasonic images. IEEE Trans Circuits Syst 36(1):129–135
Mellor M, Brady M (2004) Non-rigid multimodal image registration using local phase. In: Medical image computing and computer-assisted intervention—MICCAI 2004, vol. 3216, pp 789–796
Mercier L, Del Maestro R, Petrecca K, Araujo D, Haegelen C, Collins DL (2012) Online database of clinical mr and ultrasound images of brain tumors. Med Phys 39(6):3253–3261
Mercier L, Fonov V, Haegelen C, Del Maestro R, Petrecca K, Collins D (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):125–136
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66
Pluim J, Maintz J, Viergever M (2000) Image registration by maximization of combined mutual information and gradient information. IEEE Trans Med Imaging 19(8):809–814
Pluim J, Maintz J, Viergever M (2000) Image registration by maximization of combined mutual information and gradient information. In: Medical image computing and computer-assisted intervention- MICCAI 2000, Lecture Notes in Computer Science, vol. 1935, pp 103–129. Springer, Berlin
Roche A, Pennec X, Malandain G, Ayache N (2001) Rigid registration of 3D ultrasound with MR images: a new approach combining intensity and gradient information. IEEE Trans Med Imaging 20:1038–1049
Sibson R (1978) Studies in the robustness of multidimensional scaling: procrustes statistics. J R Stat Soc Ser B (Methodological) 40(2):234–238
Trobaugh JW, Trobaugh DJ, Richard WD (1994) Three-dimensional imaging with stereotactic ultrasonography. Comput Med Imaging Graph 18(5):315–323
Unsgaard G, Selbekk T, Brostrup Mller T, Ommedal S, Torp SH, Myhr G, Bang J, Nagelhus Hernes TA (2005) Ability of navigated 3d ultrasound to delineate gliomas and metastases comparison of image interpretations with histopathology. Acta Neurochir 147:1259–1269
Wein W, Brunke S, Khamene A, Callstrom MR, Navab N (2008) Automatic ct-ultrasound registration for diagnostic imaging and image-guided intervention. Med Image Anal 12(5):577–585
Zhang W, Noble J, Brady J (2007) Adaptive non-rigid registration of real time 3d ultrasound to cardiovascular mr images. In: Karssemeijer N, Lelieveldt B (eds) Information processing in medical imaging, Lecture Notes in Computer Science, vol. 4584, pp 50–61. Springer, Berlin
Acknowledgments
We would like to thank Dr. Laurence Mercier for her work putting together the MNI BITE dataset, and Simon Drouin and Anka Kochanowska for their technical support for rendering Figs. 2 and 3. We acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Canadian Institutes of Health Research (CIHR MOP 97820, MOP-74725 & CHRP).
Conflict of Interest
None.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nigris, D.D., Collins, D.L. & Arbel, T. Fast rigid registration of pre-operative magnetic resonance images to intra-operative ultrasound for neurosurgery based on high confidence gradient orientations. Int J CARS 8, 649–661 (2013). https://doi.org/10.1007/s11548-013-0826-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11548-013-0826-6