Automatic C-arm pose estimation via 2D/3D hybrid registration of a radiographic fiducial

E. Moult; E. C. Burdette; D. Y. Song; P. Abolmaesumi; G. Fichtinger; P. Fallavollita

doi:10.1117/12.877713

1 March 2011 Automatic C-arm pose estimation via 2D/3D hybrid registration of a radiographic fiducial

E. Moult, E. C. Burdette, D. Y. Song, P. Abolmaesumi, G. Fichtinger, P. Fallavollita

Proceedings Volume 7964, Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling; 79642S (2011) https://doi.org/10.1117/12.877713
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

Motivation: In prostate brachytherapy, real-time dosimetry would be ideal to allow for rapid evaluation of the implant quality intra-operatively. However, such a mechanism requires an imaging system that is both real-time and which provides, via multiple C-arm fluoroscopy images, clear information describing the three-dimensional position of the seeds deposited within the prostate. Thus, accurate tracking of the C-arm poses proves to be of critical importance to the process. Methodology: We compute the pose of the C-arm relative to a stationary radiographic fiducial of known geometry by employing a hybrid registration framework. Firstly, by means of an ellipse segmentation algorithm and a 2D/3D feature based registration, we exploit known FTRAC geometry to recover an initial estimate of the C-arm pose. Using this estimate, we then initialize the intensity-based registration which serves to recover a refined and accurate estimation of the C-arm pose. Results: Ground-truth pose was established for each C-arm image through a published and clinically tested segmentation-based method. Using 169 clinical C-arm images and a ±10° and ±10 mm random perturbation of the ground-truth pose, the average rotation and translation errors were 0.68° (std = 0.06°) and 0.64 mm (std = 0.24 mm). Conclusion: Fully automated C-arm pose estimation using a 2D/3D hybrid registration scheme was found to be clinically robust based on human patient data.

Citation Download Citation

E. Moult, E. C. Burdette, D. Y. Song, P. Abolmaesumi, G. Fichtinger, and P. Fallavollita "Automatic C-arm pose estimation via 2D/3D hybrid registration of a radiographic fiducial", Proc. SPIE 7964, Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 79642S (1 March 2011); https://doi.org/10.1117/12.877713

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