Name
Title | ||
|---|---|---|
Evaluation of optimization methods for intensity-based 2D-3D registration in x-ray guided interventions |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1117/12.877655 | Proceedings of SPIE |
Keywords | Field | DocType |
2D-3D registration,x-ray guided procedures,optimization | Convergence (routing),Computer vision,Data set,Gradient descent,Simultaneous perturbation stochastic approximation,Similarity measure,Evolution strategy,Artificial intelligence,Nonlinear conjugate gradient method,Image registration,Physics | Conference |
Volume | ISSN | Citations |
7962 | 0277-786X | 6 |
PageRank | References | Authors |
0.49 | 14 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| i m j van der bom | 1 | 6 | 0.49 |
| Stanley A. Klein | 2 | 98 | 13.93 |
| Marius Staring | 3 | 971 | 59.25 |
| R Homan | 4 | 9 | 2.84 |
| Lambertus W. Bartels | 5 | 19 | 3.41 |
| Josien P. W. Pluim | 6 | 2439 | 178.67 |