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Proceedings Paper

Fiducial registration error and target registration error are uncorrelated
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Paper Abstract

Image-guidance systems based on fiducial registration typically display some measure of registration accuracy based on the goodness of fit of the fiducials. A common measure is fiducial registration error (FRE), which equals the root-meansquare error in fiducial alignment between image space and physical space. It is natural for the surgeon to regard the displayed estimate of error as an indication of the accuracy of the system's ability to provide guidance to surgical targets for a given case. Thus, when the estimate is smaller than usual, it may be assumed that the target registration error (TRE) is likely to be smaller than usual. We show that this assumption, while intuitively convincing, is in fact wrong. We show it in two ways. First, we prove to first order that for a given system with a given level of normally distributed fiducial localization error, all measures of goodness of fit are statistically independent of TRE, and therefore FRE and TRE are uncorrelated. Second, we demonstrate by means of computer simulations that they are uncorrelated for the exact problem as well. Since TRE is the true measure of registration accuracy of importance to the success of the surgery, our results show that no estimate of accuracy for a given patient that is based on goodness of fiducial fit for that patient gives any information whatever about true registration accuracy for that patient. Therefore surgeons should stop using such measures as indicators of registration quality for the patients on whom they are about to operate.

Paper Details

Date Published: 13 March 2009
PDF: 12 pages
Proc. SPIE 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, 726102 (13 March 2009); doi: 10.1117/12.813601
Show Author Affiliations
J. Michael Fitzpatrick, Vanderbilt Univ. (United States)


Published in SPIE Proceedings Vol. 7261:
Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling
Michael I. Miga; Kenneth H. Wong, Editor(s)

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