3D-2D image registration by nonlinear regression
We propose a 3D-2D image registration method that relates image features of 2D projection images to the transformation parameters of the 3D image by nonlinear regression. The method is compared with a conventional registration method based on iterative optimization. For evaluation, simulated X-ray images (DRRs) were generated from coronary artery tree models derived from 3D CTA scans. Registration of nine vessel trees was performed, and the alignment quality was measured by the mean target registration error (mTRE). The regression approach was shown to be slightly less accurate, but much more robust than the method based on an iterative optimization approach.
|2D/3D image registration, image guided interventions, regression|
|2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012|
|Organisation||Erasmus MC: University Medical Center Rotterdam|
Gouveia, A.R, Metz, C.T, Freire, R, & Klein, S. (2012). 3D-2D image registration by nonlinear regression. Presented at the 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012. doi:10.1109/ISBI.2012.6235814