Global registration of ultrasound to MRI using the LC2 metric for enabling neurosurgical guidance

Wolfgang Wein; Alexander Ladikos; Bernhard Fuerst; Amit Shah; Kanishka Sharma; Nassir Navab

doi:10.1007/978-3-642-40811-3_5

Global registration of ultrasound to MRI using the LC² metric for enabling neurosurgical guidance

Wolfgang Wein, Alexander Ladikos, Bernhard Fuerst, Amit Shah, Kanishka Sharma, Nassir Navab

Informatics 16 - Chair of Computer Aided Medical Procedures

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

57 Scopus citations

Abstract

Automatic and robust registration of pre-operative magnetic resonance imaging (MRI) and intra-operative ultrasound (US) is essential to neurosurgery. We reformulate and extend an approach which uses a Linear Correlation of Linear Combination (LC²)-based similarity metric, yielding a novel algorithm which allows for fully automatic US-MRI registration in the matter of seconds. It is invariant with respect to the unknown and locally varying relationship between US image intensities and both MRI intensity and its gradient. The overall method based on this both recovers global rigid alignment, as well as the parameters of a free-form-deformation (FFD) model. The algorithm is evaluated on 14 clinical neurosurgical cases with tumors, with an average landmark-based error of 2.52mm for the rigid transformation. In addition, we systematically study the accuracy, precision, and capture range of the algorithm, as well as its sensitivity to different choices of parameters.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Pages	34-41
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-40811-3_5
State	Published - 2013
Event	16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan Duration: 22 Sep 2013 → 26 Sep 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	8149 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Country/Territory	Japan
City	Nagoya
Period	22/09/13 → 26/09/13

Access to Document

10.1007/978-3-642-40811-3_5

Cite this

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-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings (PART 1 ed., pp. 34-41). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8149 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-40811-3_5

Wein, Wolfgang ; Ladikos, Alexander ; Fuerst, Bernhard et al. / Global registration of ultrasound to MRI using the LC² metric for enabling neurosurgical guidance. Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 1. ed. 2013. pp. 34-41 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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abstract = "Automatic and robust registration of pre-operative magnetic resonance imaging (MRI) and intra-operative ultrasound (US) is essential to neurosurgery. We reformulate and extend an approach which uses a Linear Correlation of Linear Combination (LC2)-based similarity metric, yielding a novel algorithm which allows for fully automatic US-MRI registration in the matter of seconds. It is invariant with respect to the unknown and locally varying relationship between US image intensities and both MRI intensity and its gradient. The overall method based on this both recovers global rigid alignment, as well as the parameters of a free-form-deformation (FFD) model. The algorithm is evaluated on 14 clinical neurosurgical cases with tumors, with an average landmark-based error of 2.52mm for the rigid transformation. In addition, we systematically study the accuracy, precision, and capture range of the algorithm, as well as its sensitivity to different choices of parameters.",

author = "Wolfgang Wein and Alexander Ladikos and Bernhard Fuerst and Amit Shah and Kanishka Sharma and Nassir Navab",

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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-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 8149 LNCS, pp. 34-41, 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, Nagoya, Japan, 22/09/13. https://doi.org/10.1007/978-3-642-40811-3_5

Global registration of ultrasound to MRI using the LC² metric for enabling neurosurgical guidance. / Wein, Wolfgang; Ladikos, Alexander; Fuerst, Bernhard et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 1. ed. 2013. p. 34-41 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8149 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Wein W, Ladikos A, Fuerst B, Shah A, Sharma K, Navab N. Global registration of ultrasound to MRI using the LC² metric for enabling neurosurgical guidance. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 1 ed. 2013. p. 34-41. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-40811-3_5