Spectral-based 2D/3D X-ray to CT image rigid registration

We present a spectral-based method for the 2D/3D rigid registration of X-ray images to a CT scan. The method uses a Fourier-based representation to decompose the six rigid transformation parameters problem into a twoparameter out-of-plane rotation and a four-parameter in-plane transformation problems. Preoperatively, a set of Digitally Reconstructed Radiographs (DRRs) are generated offline from the CT in the expected in-plane location ranges of the fluoroscopic X-ray imaging devices. Each DRR is transformed into a imaging device in-plane invariant features space. Intraoperatively, a few 2D projections of the patient anatomy are acquired with an X-ray imaging device. Each projection is transformed into its in-plane invariant representation. The out-of-plane parameters are first computed by maximization of the Normalized Cross-Correlation between the invariant representations of the DRRs and the X-ray images. Then, the in-plane parameters are computed with the phase correlation method based on the Fourier-Mellin transform. Experimental results on publicly available data sets show that our method can robustly estimate the out-of-plane parameters with accuracy of 1.5° in less than 1sec for out-of-plane rotations of 10° or more, and perform the entire registration in less than 10secs.

[1]  Bostjan Likar,et al.  A review of 3D/2D registration methods for image-guided interventions , 2012, Medical Image Anal..

[2]  Frank Sauer,et al.  Automatic registration of portal images and volumetric CT for patient positioning in radiation therapy , 2006, Medical Image Anal..

[3]  B. N. Chatterji,et al.  An FFT-based technique for translation, rotation, and scale-invariant image registration , 1996, IEEE Trans. Image Process..

[4]  David J. Hawkes,et al.  A Comparison of 2D-3D Intensity-Based Registration and Feature-Based Registration for Neurointerventions , 2002, MICCAI.

[5]  Bostjan Likar,et al.  Robust Gradient-Based 3-D/2-D Registration of CT and MR to X-Ray Images , 2008, IEEE Transactions on Medical Imaging.

[6]  Boštjan Likar,et al.  Standardized evaluation methodology for 3D/2D registration based on the Visible Human data set. , 2010, Medical physics.

[7]  Benjamin B. Kimia,et al.  2D-3D registration based on shape matching , 2000, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. MMBIA-2000 (Cat. No.PR00737).

[8]  W. Eric L. Grimson,et al.  2D-3D rigid registration of X-ray fluoroscopy and CT images using mutual information and sparsely sampled histogram estimators , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[9]  Nassir Navab,et al.  New CTA Protocol and 2D-3D Registration Method for Liver Catheterization , 2006, MICCAI.

[10]  G. Kuduvalli,et al.  A fast, accurate, and automatic 2D-3D image registration for image-guided cranial radiosurgery. , 2008, Medical physics.

[11]  Roland Göcke,et al.  Optical flow estimation using Fourier Mellin Transform , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Bostjan Likar,et al.  3-D/2-D registration by integrating 2-D information in 3-D , 2006, IEEE Transactions on Medical Imaging.

[13]  Wolfgang Birkfellner,et al.  The Zernike Expansion - An Example of a Merit Function for 2D/3D Registration Based on Orthogonal Functions , 2008, MICCAI.

[14]  L. Joskowicz,et al.  Gradient-based 2-D/3-D rigid registration of fluoroscopic X-ray to CT , 2003, IEEE Transactions on Medical Imaging.

[15]  Daniel B. Russakoff,et al.  Intensity-based 2D-3D spine image registration incorporating a single fiducial marker. , 2005, Academic radiology.

[16]  Leo Joskowicz,et al.  Effective Intensity-Based 2D/3D Rigid Registration between Fluoroscopic X-Ray and CT , 2003, MICCAI.

[17]  Daniel Rueckert,et al.  Fast generation of digitally reconstructed radiographs using attenuation fields with application to 2D-3D image registration , 2005, IEEE Transactions on Medical Imaging.