Estimating {hbox {FLE}} mathrm{image} distributions of manual fiducial localization in CT imagesReportar como inadecuado

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International Journal of Computer Assisted Radiology and Surgery

, Volume 11, Issue 6, pp 1043–1049

First Online: 30 March 2016Received: 10 February 2016Accepted: 15 March 2016


PurposeThe fiducial localization error distribution FLE and fiducial configuration govern the application accuracy of point-based registration and drive target registration error TRE prediction models. The error of physically localizing patient fiducials \{\hbox {FLE}} \mathrm{patient}\ is negligible when a registration probe matches the implanted screws with mechanical precision. Reliable trackers provide an unbiased estimate of the positional error \{\hbox {FLE}} \mathrm{tracker}\ with cheap repetitions. FLE further contains the localization error in the imaging data \{\hbox {FLE}} \mathrm{image}\, sampling of which in general is expensive and possibly biased. Finding the best techniques for estimating \{\hbox {FLE}} \mathrm{image}\ is crucial for the applicability of the TRE prediction methods.

MethodsWe built a ground-truth gt-based unbiased estimator \\widehat{{\hbox {FLE}} \mathrm{gt}}\ of \{\hbox {FLE}} \mathrm{image}\ from the samples collected in a virtual CT dataset in which the true locations of image fiducials are known by definition. Replacing true locations in \{\hbox {FLE}} \mathrm{gt}\ by the sample mean creates a practical difference-to-mean dtm-based estimator \\widehat{{\hbox {FLE}} \mathrm{dtm}}\ that is applicable on any dataset. To check the practical validity of the dtm estimator, ten persons manually localized nine fiducials ten times in the virtual CT and the resulting \{\hbox {FLE}} \mathrm{dtm}\ and \{\hbox {FLE}} \mathrm{gt}\ distributions were tested for statistical equality with a kernel-based two-sample test using the maximum mean discrepancy MMD Gretton in J Mach Learn Res 13:723–773, 2012 statistics at \\alpha =0.05\.

Results\{\hbox {FLE}} \mathrm{dtm}\ and \{\hbox {FLE}} \mathrm{gt}\ were found for most of the cases not to be statistically significantly different; conditioning them on persons and-or screws however yielded statistically significant differences much more often.

ConclusionsWe conclude that \\widehat{{\hbox {FLE}} \mathrm{dtm}}\ is the best candidate within our model for estimating \{\hbox {FLE}} \mathrm{image}\ in homogeneous TRE prediction models. The presented approach also allows ground-truth-based numerical validation of \{\hbox {FLE}} \mathrm{image}\ estimators and manual-automatic image fiducial localization methods in phantoms with parameters similar to clinical datasets.

KeywordsNavigation Registration Virtual CT FLE  Download fulltext PDF

Autor: Zoltan Bardosi - Wolfgang Freysinger


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