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A framework for automatic creation of gold-standard rigid 3D–2D registration datasets

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

Abstract

Purpose

Advanced image-guided medical procedures incorporate 2D intra-interventional information into pre-interventional 3D image and plan of the procedure through 3D/2D image registration (32R). To enter clinical use, and even for publication purposes, novel and existing 32R methods have to be rigorously validated. The performance of a 32R method can be estimated by comparing it to an accurate reference or gold standard method (usually based on fiducial markers) on the same set of images (gold standard dataset). Objective validation and comparison of methods are possible only if evaluation methodology is standardized, and the gold standard  dataset is made publicly available. Currently, very few such datasets exist and only one contains images of multiple patients acquired during a procedure. To encourage the creation of gold standard 32R datasets, we propose an automatic framework.

Methods

The framework is based on rigid registration of fiducial markers. The main novelty is spatial grouping of fiducial markers on the carrier device, which enables automatic marker localization and identification across the 3D and 2D images.

Results

The proposed framework was demonstrated on clinical angiograms of 20 patients. Rigid 32R computed by the framework was more accurate than that obtained manually, with the respective target registration error below 0.027 mm compared to 0.040 mm.

Conclusion

The framework is applicable for gold standard setup on any rigid anatomy, provided that the acquired images contain spatially grouped fiducial markers. The gold standard datasets and software will be made publicly available.

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Notes

  1. The distance between the closest elements of the clusters.

  2. We assumed that the projection of spherical marker is circular, and the same circular template, sampled at the resolution of the X-ray image, is used to detect markers across the entire 2D gradient direction image. In general, the projections of ball-shaped objects are not circular but rather elliptic, the more eccentric the further away they lie from the projection axis. Under our C-arm projection geometry, the ratio of major and minor marker semi-axes was always less than 1.037, which is negligible and did not have an impact on marker localization.

  3. In all 3D and 2D the images, the fiducial markers were masked and cubic spline interpolation was used to impaint new intensity values. Hence, markers are not visible and thus cannot bias the registration.

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Funding

This research was supported by Slovenian Research Agency (Grants Nos. J2-5473 and P2-0232).

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Authors and Affiliations

  1. Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, 1000, Ljubljana, Slovenia

    Hennadii Madan, Franjo Pernuš, Boštjan Likar & Žiga Špiclin

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  1. Hennadii Madan
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  2. Franjo Pernuš
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Correspondence to Hennadii Madan.

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Madan, H., Pernuš, F., Likar, B. et al. A framework for automatic creation of gold-standard rigid 3D–2D registration datasets. Int J CARS 12, 263–275 (2017). https://doi.org/10.1007/s11548-016-1482-4

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  • DOI: https://doi.org/10.1007/s11548-016-1482-4

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