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Real-time 6DoF pose recovery from X-ray images using library-based DRR and hybrid optimization

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Real-time 6 degrees of freedom (6DoF) pose recovery and tracking from X-ray images is a key enabling technology for many interventional imaging applications. However, real-time 2D/3D registration is a very challenging problem because of the heavy computation in iterative digitally reconstructed radiograph (DRR) generation. In this paper, we propose a real-time 2D/3D registration framework using library-based DRRs to achieve high computational efficiency.

Method

The proposed method pre-computes a library of canonical DRRs and reconstructs library-based DRRs (libDRRs) during registration without online rendering. The transformation parameters are decoupled to 2 geometry-relevant and 4 geometry-irrelevant ones so that canonical DRRs only need to cover the variation of 2 geometry-relevant parameters, making it practical to be pre-computed and stored. The 2D/3D registration using libDRRs is then solved as a hybrid optimization problem, i.e., continuous in geometry-irrelevant parameters while discrete in geometry-relevant parameters.

Results

On 5 fluoroscopic sequences with 246 frames acquired during animal studies with a transesophageal echocardiography (TEE) probe in the field of view, 6DoF tracking of the TEE probe using the proposed method achieved a mean target registration error in the projection direction (mTREproj) of 0.81 mm, a success rate of 100 % (defined as mTREproj \(<\)2.5 mm), and a registration frame rate of 23.1 fps on a pure CPU-based implementation executed in a single thread.

Conclusion

Using libDRRs with a hybrid optimization can significantly improve the computational efficiency (up to tenfold) for 6DoF pose recovery and tracking with little degradation in robustness and accuracy, compared to conventional intensity-based 2D/3D registration using ray casting DRRs with a continuous optimization.

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Authors and Affiliations

  1. Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, 08540, USA

    S. Miao, A. Tuysuzoglu & R. Liao

  2. Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada

    Z. J. Wang

Authors
  1. S. Miao
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  2. A. Tuysuzoglu
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  3. Z. J. Wang
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  4. R. Liao
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Correspondence to S. Miao.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Miao, S., Tuysuzoglu, A., Wang, Z.J. et al. Real-time 6DoF pose recovery from X-ray images using library-based DRR and hybrid optimization. Int J CARS 11, 1211–1220 (2016). https://doi.org/10.1007/s11548-016-1387-2

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  • DOI: https://doi.org/10.1007/s11548-016-1387-2

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