Comparison of Similarity Measures for Rigid-body CT/Dual X-ray Image Registrations

A set of experiments were conducted to evaluate six similarity measures for intensity-based rigid-body 3D/2D image registration. Similarity measure is an index that measures the similarity between a digitally reconstructed radiograph (DRR) and an x-ray planar image. The registration is accomplished by maximizing the sum of the similarity measures between biplane x-ray images and the corresponding DRRs in an iterative fashion. We have evaluated the accuracy and attraction ranges of the registrations using six different similarity measures on phantom experiments for head, thorax, and pelvis. The images were acquired using Varian Medial System On-Board Imager. Our results indicated that normalized cross correlation and entropy of difference showed a wide attraction range (62 deg and 83 mm mean attraction range, ωmean), but the worst accuracy (4.2 mm maximum error, emax). The gradient-based similarity measures, gradient correlation and gradient difference, and the pattern intensity showed sub-millimeter accuracy, but narrow attraction ranges (ωmean=29 deg, 31 mm). Mutual information was in-between of these two groups (emax=2.5 mm, ωmean= 48 deg, 52 mm). On the data of 120 x-ray pairs from eight IRB approved prostate patients, the gradient difference showed the best accuracy. In the clinical applications, registrations starting with the mutual information followed by the gradient difference may provide the best accuracy and the most robustness.

[1]  M. V. van Herk,et al.  Physical aspects of a real-time tumor-tracking system for gated radiotherapy. , 2000, International journal of radiation oncology, biology, physics.

[2]  M. Levoy,et al.  Fast volume rendering using a shear-warp factorization of the viewing transformation , 1994, SIGGRAPH.

[3]  T. Boult,et al.  Registration of planar film radiographs with computed tomography , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[4]  Colin Studholme,et al.  Automated 3-D registration of MR and CT images of the head , 1996, Medical Image Anal..

[5]  J Kim,et al.  A feasibility study of mutual information based setup error estimation for radiotherapy. , 2001, Medical physics.

[6]  Max A. Viergever,et al.  f-information measures in medical image registration , 2004, IEEE Transactions on Medical Imaging.

[7]  J. Kettenbach,et al.  Rigid 2D/3D slice-to-volume registration and its application on fluoroscopic CT images. , 2006, Medical physics.

[8]  D L Hill,et al.  Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures. , 1997, Medical physics.

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

[10]  Jeremy T. Booth,et al.  Modeling the impact of treatment uncertainties in radiotherapy , 2002 .

[11]  Serge Miguet,et al.  Patient setup error measurement using 3D intensity-based image registration techniques. , 2003, International journal of radiation oncology, biology, physics.

[12]  Jürgen Weese,et al.  A comparison of similarity measures for use in 2-D-3-D medical image registration , 1998, IEEE Transactions on Medical Imaging.

[13]  David Djajaputra,et al.  Real-time 3D surface-image-guided beam setup in radiotherapy of breast cancer. , 2004, Medical physics.

[14]  David J. Hawkes,et al.  Voxel similarity measures for 3-D serial MR brain image registration , 1999, IEEE Transactions on Medical Imaging.

[15]  Gregory C Sharp,et al.  Integrated radiotherapy imaging system (IRIS): design considerations of tumour tracking with linac gantry-mounted diagnostic x-ray systems with flat-panel detectors , 2004, Physics in medicine and biology.

[16]  L. Verhey,et al.  Computer-assisted positioning of radiotherapy patients using implanted radiopaque fiducials. , 1993, Medical physics.

[17]  M van Herk,et al.  Automatic three-dimensional inspection of patient setup in radiation therapy using portal images, simulator images, and computed tomography data. , 1996, Medical physics.

[18]  Morten Bro-Nielsen.  Rigid registration of CT, MR and cryosection images using a GLCM framework , 1997, CVRMed.

[19]  Jürgen Weese,et al.  Voxel-based 2-D/3-D registration of fluoroscopy images and CT scans for image-guided surgery , 1997, IEEE Transactions on Information Technology in Biomedicine.

[20]  Jürgen Weese,et al.  An approach to 2D/3D registration of a vertebra in 2D X-ray fluoroscopies with 3D CT images , 1997, CVRMed.

[21]  Jinkoo Kim,et al.  Effects of x-ray and CT image enhancements on the robustness and accuracy of a rigid 3D/2D image registration. , 2005, Medical physics.

[22]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[23]  Alan Liu,et al.  3D/2D Registration via Skeletal Near Projective Invariance in Tubular Objects , 1998, MICCAI.

[24]  J. Buatti,et al.  The University of Florida frameless high-precision stereotactic radiotherapy system. , 1997, International journal of radiation oncology, biology, physics.

[25]  J Bijhold,et al.  Three-dimensional verification of patient placement during radiotherapy using portal images. , 1993, Medical physics.

[26]  M. Murphy.  An automatic six-degree-of-freedom image registration algorithm for image-guided frameless stereotaxic radiosurgery. , 1997, Medical physics.

[27]  M. Uematsu,et al.  A dual computed tomography linear accelerator unit for stereotactic radiation therapy: a new approach without cranially fixated stereotactic frames. , 1996, International journal of radiation oncology, biology, physics.

[28]  David J. Hawkes,et al.  Voxel Similarity Measures for 3D Serial MR Brain Image Registration , 2000, IEEE Trans. Medical Imaging.

[29]  Kawal S. Rhode,et al.  Intensity-based 2-D - 3-D registration of cerebral angiograms , 2003, IEEE Transactions on Medical Imaging.

[30]  Nicholas Ayache,et al.  3D-2D Projective Registration of Free-Form Curves and Surfaces , 1997, Comput. Vis. Image Underst..