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3D CT to 2D X-ray image registration for improved visualization of tibial vessels in endovascular procedures

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

Abstract

Purpose

During endovascular revascularization interventions for peripheral arterial disease, the standard modality of X-ray fluoroscopy (XRF) used for image guidance is limited in visualizing distal segments of infrapopliteal vessels. To enhance visualization of arteries, an image registration technique was developed to align pre-acquired computed tomography (CT) angiography images and to create fusion images highlighting arteries of interest.

Methods

X-ray image metadata capturing the position of the X-ray gantry initializes a multiscale iterative optimization process, which uses a local-variance masked normalized cross-correlation loss to rigidly align a digitally reconstructed radiograph (DRR) of the CT dataset with the target X-ray, using the edges of the fibula and tibia as the basis for alignment. A precomputed library of DRRs is used to improve run-time, and the six-degree-of-freedom optimization problem of rigid registration is divided into three smaller sub-problems to improve convergence. The method was tested on a dataset of paired cone-beam CT (CBCT) and XRF images of ex vivo limbs, and registration accuracy at the midline of the artery was evaluated.

Results

On a dataset of CBCTs from 4 different limbs and a total of 17 XRF images, successful registration was achieved in 13 cases, with the remainder suffering from input image quality issues. The method produced average misalignments of less than 1 mm in horizontal projection distance along the artery midline, with an average run-time of 16 s.

Conclusion

The sub-mm spatial accuracy of artery overlays is sufficient for the clinical use case of identifying guidewire deviations from the path of the artery, for early detection of guidewire-induced perforations. The semiautomatic workflow and average run-time of the algorithm make it feasible for integration into clinical workflows.

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Data and code availability

Code will be made available via a public repository at time of publishing. The evaluation dataset cannot be shared, as per the institutional data sharing agreement.

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Funding

MS, JHP, CDS, and GAW receive research support from the Canadian Institutes for Health Research (Grant Number: PJT156041), and the Vector Scholarship in Artificial Intelligence, provided through the Vector Institute.

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Authors and Affiliations

  1. Department of Medical Biophysics, University of Toronto, Toronto, Canada

    Moujan Saderi, Jaykumar H. Patel, Calder D. Sheagren & Graham A. Wright

  2. Physical Sciences, Sunnybrook Research Institute, Toronto, Canada

    Moujan Saderi, Jaykumar H. Patel, Calder D. Sheagren & Graham A. Wright

  3. DeBakey Heart & Vascular Center, Houston Methodist Hospital, Houston, USA

    Judit Csőre & Trisha L. Roy

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Correspondence to Moujan Saderi.

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The authors (MS, JHP, CDS, JC, TLR, GAW) have no competing interests to declare that are relevant to the content of this article.

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This study was performed in line with the principles of the Declaration of Helsinki, with approval under protocol number PRO00025278 by the Institutional Review Board at Houston Methodist.

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Saderi, M., Patel, J.H., Sheagren, C.D. et al. 3D CT to 2D X-ray image registration for improved visualization of tibial vessels in endovascular procedures. Int J CARS (2025). https://doi.org/10.1007/s11548-024-03302-z

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