Multimodal 3D ultrasound and CT in image-guided spinal surgery: public database and new registration algorithms.

Autor: Masoumi N; PERFORM Centre, Concordia University, Montreal, Canada. n_masoum@encs.concordia.ca.; Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada. n_masoum@encs.concordia.ca., Belasso CJ; Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada., Ahmad MO; Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada., Benali H; PERFORM Centre, Concordia University, Montreal, Canada.; Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada., Xiao Y; Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada., Rivaz H; PERFORM Centre, Concordia University, Montreal, Canada.; Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada.
Jazyk: angličtina
Zdroj: International journal of computer assisted radiology and surgery [Int J Comput Assist Radiol Surg] 2021 Apr; Vol. 16 (4), pp. 555-565. Date of Electronic Publication: 2021 Mar 08.
DOI: 10.1007/s11548-021-02323-2
Abstrakt: Purpose: Accurate multimodal registration of intraoperative ultrasound (US) and preoperative computed tomography (CT) is a challenging problem. Construction of public datasets of US and CT images can accelerate the development of such image registration techniques. This can help ensure the accuracy and safety of spinal surgeries using image-guided surgery systems where an image registration is employed. In addition, we present two algorithms to register US and CT images.
Methods: We present three different datasets of vertebrae with corresponding CT, US, and simulated US images. For each of the two latter datasets, we also provide 16 landmark pairs of matching structures between the CT and US images and performed fiducial registration to acquire a silver standard for assessing image registration. Besides, we proposed two patch-based rigid image registration algorithms, one based on normalized cross-correlation (NCC) and the other based on correlation ratio (CR) to register misaligned CT and US images.
Results: The CT and corresponding US images of the proposed database were pre-processed and misaligned with different error intervals, resulting in 6000 registration problems solved using both NCC and CR methods. Our results show that the methods were successful in aligning the pre-processed CT and US images by decreasing the warping index.
Conclusions: The database provides a resource for evaluating image registration techniques. The simulated data have two applications. First, they provide the gold standard ground-truth which is difficult to obtain with ex vivo and in vivo data for validating US-CT registration methods. Second, the simulated US images can be used to validate real-time US simulation methods. Besides, the proposed image registration techniques can be useful for developing methods in clinical application.
Databáze: MEDLINE
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