Abstract
The main challenge preventing a fully-automatic X-ray to CT registration is an initialization scheme that brings the X-ray pose within the capture range of existing intensity-based registration methods. By providing such an automatic initialization, the present study introduces the first end-to-end fully-automatic registration framework. A network is first trained once on artificial X-rays to extract 2D landmarks resulting from the projection of CT-labels. A patient-specific refinement scheme is then carried out: candidate points detected from a new set of artificial X-rays are back-projected onto the patient CT and merged into a refined meaningful set of landmarks used for network re-training. This network-landmarks combination is finally exploited for intraoperative pose-initialization with a runtime of 102 ms. Evaluated on 6 pelvis anatomies (486 images in total), the mean Target Registration Error was \(15.0\pm 7.3\) mm. When used to initialize the BOBYQA optimizer with normalized cross-correlation, the average (± STD) projection distance was \(3.4\pm 2.3\) mm, and the registration success rate (projection distance \(<2.5\%\) of the detector width) greater than \(97\%\).
J. Esteban and M. Grimm-Contributed equally to this work.
This work was supported by the German Federal Ministry of Research and Education (FKZ: 13GW0236B) and a GPU Grant from Nvidia.
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ImFusion GmbH, Munich, Germany (https://www.imfusion.de).
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Esteban, J., Grimm, M., Unberath, M., Zahnd, G., Navab, N. (2019). Towards Fully Automatic X-Ray to CT Registration. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_70
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