Epipolar Consistency in Fluoroscopy for Image-Based Tracking

André Aichert, Jian Wang, Roman Schaffert, Arnd Dörfler, Joachim Hornegger and Andreas Maier

Abstract

Geometry and physics of absorption imaging impose certain constraints on been introduced and applied to motion correction in flat-detector computed tomography (CT). They are based on redundant information in transmission images along epipolar lines. Unlike other consistency conditions for CT scans, they act directly on an arbitrary pair of X-ray images. This paper proposes an application of ECC to 3D patient tracking in interventional radiology. We evaluate the proposed method against 2D-3D registration with a previously acquired CT. Our experiments on synthetic data based on a patient CT and phantom data from an interventional C-arm demonstrate that our method is able to compensate online for rotations of up to ±10° and translations of ±25 mm between consecutive frames. We successfully track rotations of as much as 45° over 45 images. The outstanding property of the approach is that no 3D scan is required for tracking a 3D object in space. We show, that small rotations of about 3° in space and translations of about 50 mm can be tracked based on just two reference X-ray images. Since the proposed approach works directly on X-ray images, it exceeds regular 2D-3D registration with a CT in an order of magnitude in computational speed. We conclude that ECC are a simple and effective new tool for pre-aligment and online patient tracking for fluoroscopic sequences.

Session

Medical Applications

Files

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DOI

10.5244/C.29.82
https://dx.doi.org/10.5244/C.29.82

Citation

André Aichert, Jian Wang, Roman Schaffert, Arnd Dörfler, Joachim Hornegger and Andreas Maier. Epipolar Consistency in Fluoroscopy for Image-Based Tracking. In Xianghua Xie, Mark W. Jones, and Gary K. L. Tam, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 82.1-82.10. BMVA Press, September 2015.

Bibtex

@inproceedings{BMVC2015_82,
	title={Epipolar Consistency in Fluoroscopy for Image-Based Tracking},
	author={André Aichert and Jian Wang and Roman Schaffert and Arnd Dörfler and Joachim Hornegger and Andreas Maier},
	year={2015},
	month={September},
	pages={82.1-82.10},
	articleno={82},
	numpages={10},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.82},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.82}
}