A new gold-standard dataset for 2D/3D image registration evaluation

In this paper, we propose a new gold standard data set for the validation of 2D/3D image registration algorithms for image guided radiotherapy. A gold standard data set was calculated using a pig head with attached fiducial markers. We used several imaging modalities common in diagnostic imaging or radiotherapy which include 64-slice computed tomography (CT), magnetic resonance imaging (MRI) using T1, T2 and proton density (PD) sequences, and cone beam CT (CBCT) imaging data. Radiographic data were acquired using kilovoltage (kV) and megavoltage (MV) imaging techniques. The image information reflects both anatomy and reliable fiducial marker information, and improves over existing data sets by the level of anatomical detail and image data quality. The markers of three dimensional (3D) and two dimensional (2D) images were segmented using Analyze 9.0 (AnalyzeDirect, Inc) and an in-house software. The projection distance errors (PDE) and the expected target registration errors (TRE) over all the image data sets were found to be less than 1.7 mm and 1.3 mm, respectively. The gold standard data set, obtained with state-of-the-art imaging technology, has the potential to improve the validation of 2D/3D registration algorithms for image guided therapy.

[1]  Ma Bin-rong,et al.  A review of medical image registration , 1999 .

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

[3]  Wolfgang Birkfellner,et al.  Wobbled splatting—a fast perspective volume rendering method for simulation of x-ray images from CT , 2005, Physics in medicine and biology.

[4]  Boštjan Likar,et al.  “Gold standard” data for evaluation and comparison of 3D/2D registration methods , 2004, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[5]  Bostjan Likar,et al.  Evaluation of similarity measures for reconstruction-based registration in image-guided radiotherapy and surgery. , 2006, International journal of radiation oncology, biology, physics.

[6]  R. Sibson.  Studies in the Robustness of Multidimensional Scaling: Perturbational Analysis of Classical Scaling , 1979 .

[7]  P. Schönemann,et al.  Fitting one matrix to another under choice of a central dilation and a rigid motion , 1970 .

[8]  Lik-Kwan Shark,et al.  A computationally efficient method for automatic registration of orthogonal x-ray images with volumetric CT data , 2008, Physics in medicine and biology.

[9]  S. P. Mudur,et al.  Three-dimensional computer vision: a geometric viewpoint , 1993 .

[10]  Pantelis Karaiskos,et al.  Registration of electronic portal images for patient set-up verification. , 2004, Physics in medicine and biology.

[11]  J C de Munck,et al.  The accuracy of image registration for the brain and the nasopharynx using external anatomical landmarks. , 2000, Physics in medicine and biology.

[12]  J. Kettenbach,et al.  Stochastic rank correlation: a robust merit function for 2D/3D registration of image data obtained at different energies. , 2009, Medical physics.

[13]  L. Xing,et al.  Overview of image-guided radiation therapy. , 2006, Medical dosimetry : official journal of the American Association of Medical Dosimetrists.

[14]  Graeme P. Penney,et al.  Standardized evaluation methodology for 2-D-3-D registration , 2005, IEEE Transactions on Medical Imaging.

[15]  J H Siewerdsen,et al.  A performance comparison of flat-panel imager-based MV and kV cone-beam CT. , 2002, Medical physics.

[16]  D. R. Fish,et al.  A patient-to-computed-tomography image registration method based on digitally reconstructed radiographs. , 1994, Medical physics.

[17]  M. Fitzpatrick.  A Review of Medical Image Registration , 1993 .

[18]  Jay B. West,et al.  Fiducial Point Placement and the Accuracy of Point-based, Rigid Body Registration , 2001, Neurosurgery.

[19]  R.J. Maciunas,et al.  An automatic technique for finding and localizing externally attached markers in CT and MR volume images of the head , 1996, IEEE Transactions on Biomedical Engineering.

[20]  Jay B. West,et al.  Predicting error in rigid-body point-based registration , 1998, IEEE Transactions on Medical Imaging.