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Proceedings Paper

Evaluation of 3D-2D registration methods for registration of 3D-DSA and 2D-DSA cerebral images
Author(s): Uroš Mitrović; Žiga Špiclin; Boštjan Likar; Franjo Pernuš
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Paper Abstract

Recent C-arm systems used for endovascular image-guided interventions enable the acquisition of three-dimensional (3D) and dynamic two-dimensional (2D+t) images in the same interventional suite. The 3D images are used to observe the vascular morphology while the 2D+t images show the current state of the intervention. By spatial alignment of 3D and 2D+t images one can facilitate the endovascular interventions, e.g. by displaying the intra-interventional tools and contrast-agent flow in the augmented 3D+t images. To achieve the spatial alignment several 3D-2D registration methods were proposed that are concerned with finding the rigid-body parameters of the 3D image. Meanwhile, the pose of the C-arm system is usually obtained through a dedicated C-arm calibration. In practice, the calibrated C-arm pose parameters are typically valid only if the imaged object is positioned in the C-arm’s isocenter. To compensate this, the 3D-2D registration should search simultaneously for the rigid-body as well as the C-arm pose parameters. For verification, we tested three 3D-2D registration methods on real, clinical 3D and 2D+t angiographic images of twenty patients, ten of which were imaged with attached fiducial markers to obtain a “gold standard” registration. The results indicate that, compared to searching solely the rigid-body parameters, by searching simultaneously for rigid-body and the C-arm pose parameters significantly improves the accuracy and success rate of 3D-2D registration methods. Among the three tested methods the intensity-based method using mutual information was the most robust, as it successfully registered all clinical datasets, and highly accurate, as the maximal fiducial registration error was less or equal than 0.34 mm.

Paper Details

Date Published: 13 March 2013
PDF: 12 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866931 (13 March 2013); doi: 10.1117/12.2007009
Show Author Affiliations
Uroš Mitrović, Univ. of Ljubljana (Slovenia)
Žiga Špiclin, Univ. of Ljubljana (Slovenia)
Boštjan Likar, Univ. of Ljubljana (Slovenia)
Sensum Computer Vision Systems (Slovenia)
Franjo Pernuš, Univ. of Ljubljana (Slovenia)
Sensum Computer Vision Systems (Slovenia)


Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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