Proceedings Article | 14 March 2011
KEYWORDS: Image registration, X-rays, X-ray imaging, Stochastic processes, 3D image processing, 3D acquisition, Data acquisition, Chromium, Radiology, Image processing
The advantage of 2D-3D image registration methods versus direct image-to-patient registration, is that these
methods generally do not require user interaction (such as manual annotations), additional machinery or additional
acquisition of 3D data.
A variety of intensity-based similarity measures has been proposed and evaluated for different applications. These
studies showed that the registration accuracy and capture range are influenced by the choice of similarity measure.
However, the influence of the optimization method on intensity-based 2D-3D image registration has not been
investigated. We have compared the registration performance of seven optimization methods in combination with
three similarity measures: gradient difference, gradient correlation, and pattern intensity. Optimization methods
included in this study were: regular step gradient descent, Nelder-Mead, Powell-Brent, Quasi-Newton, nonlinear
conjugate gradient, simultaneous perturbation stochastic approximation, and evolution strategy. Registration
experiments were performed on multiple patient data sets that were obtained during cerebral interventions.
Various component combinations were evaluated on registration accuracy, capture range, and registration time.
The results showed that for the same similarity measure, different registration accuracies and capture ranges
were obtained when different optimization methods were used. For gradient difference, largest capture ranges
were obtained with Powell-Brent and simultaneous perturbation stochastic approximation. Gradient correlation
and pattern intensity had the largest capture ranges in combination with Powell-Brent, Nelder-Mead, nonlinear
conjugate gradient, and Quasi-Newton. Average registration time, expressed in the number of DRRs required
for convergence, was the lowest for Powell-Brent. Based on these results, we conclude that Powell-Brent is a
reliable optimization method for intensity-based 2D-3D registration of x-ray images to CBCT, regardless of the
similarity measure used.
