Real-time image-based rigid registration of three-dimensional ultrasound

Registration of three-dimensional ultrasound (3DUS) volumes is necessary in several applications, such as when stitching volumes to expand the field of view or when stabilizing a temporal sequence of volumes to cancel out motion of the probe or anatomy. Current systems that register 3DUS volumes either use external tracking systems (electromagnetic or optical), which add expense and impose limitations on acquisitions, or are image-based methods that operate offline and are incapable of providing immediate feedback to clinicians. This paper presents a real-time image-based algorithm for rigid registration of 3DUS volumes designed for acquisitions in which small probe displacements occur between frames. Described is a method for feature detection and descriptor formation that takes into account the characteristics of 3DUS imaging. Volumes are registered by determining a correspondence between these features. A global set of features is maintained and integrated into the registration, which limits the accumulation of registration error. The system operates in real-time (i.e. volumes are registered as fast or faster than they are acquired) by using an accelerated framework on a graphics processing unit. The algorithm's parameter selection and performance is analyzed and validated in studies which use both water tank and clinical images. The resulting registration accuracy is comparable to similar feature-based registration methods, but in contrast to these methods, can register 3DUS volumes in real-time.

[1]  A. Hero,et al.  Image matching using alpha-entropy measures and entropic graphs , 2005 .

[2]  Rebecca Fahrig,et al.  Towards Guidance of Electrophysiological Procedures with Real-Time 3D Intracardiac Echocardiography Fusion to C-arm CT , 2009, MICCAI.

[3]  Andrew H. Gee,et al.  Rapid Registration for Wide Field-of-View Freehand 3D Ultrasound , 2003, IEEE Trans. Medical Imaging.

[4]  Nassir Navab,et al.  Multi-modal Registration Based Ultrasound Mosaicing , 2009, MICCAI.

[5]  Steve R. Gunn,et al.  On the discrete representation of the Laplacian of Gaussian , 1999, Pattern Recognit..

[6]  J. Alison Noble,et al.  Nonrigid registration of 3-D free-hand ultrasound images of the breast , 2002, IEEE Transactions on Medical Imaging.

[7]  Raj Shekhar,et al.  Registration of real-time 3-D ultrasound images of the heart for novel 3-D stress echocardiography , 2004, IEEE Transactions on Medical Imaging.

[8]  Kashif Rajpoot,et al.  Multiview RT3D Echocardiography Image Fusion , 2009, FIMH.

[9]  Robert Rohling,et al.  Automatic registration of 3-D ultrasound images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[10]  Christian Barillot,et al.  Robust statistical registration of 3D ultrasound images using texture information , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[11]  Wolfgang Wein,et al.  Ultrasound based Respiratory Motion Compensation in the Abdomen , 2008 .

[12]  Tien-Tsin Wong,et al.  Volumetric Ultrasound Panorama Based on 3D SIFT , 2008, MICCAI.

[13]  K. S. Arun,et al.  Least-Squares Fitting of Two 3-D Point Sets , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Robert Allen,et al.  Handbook of Medical Imaging—Processing and Analysis , 2001 .

[15]  Ioannis Pratikakis,et al.  Robust Multiscale Deformable Registration of 3D Ultrasound Images , 2003, Int. J. Image Graph..

[16]  Zhijun Zhang,et al.  Robust Registration of 3-D Ultrasound Images Based on Gabor Filter and Mean-Shift Method , 2004, ECCV Workshops CVAMIA and MMBIA.

[17]  Nassir Navab,et al.  Fast Deformable Registration of 3D-Ultrasound Data Using a Variational Approach , 2006, MICCAI.

[18]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[19]  Andrew H. Gee,et al.  Rapid registration for wide field of view freehand three-dimensional ultrasound , 2003, IEEE Transactions on Medical Imaging.

[20]  Robert N Rohling,et al.  Three-dimensional extended field-of-view ultrasound. , 2006, Ultrasound in medicine & biology.

[21]  Brian C Porter,et al.  Three-Dimensional Medical Ultrasound Acquisition and Data Registration and Fusion , 2004 .

[22]  J. Alison Noble,et al.  Adaptive Multiscale Ultrasound Compounding Using Phase Information , 2005, MICCAI.

[23]  M Desco,et al.  Cardiac motion analysis from ultrasound sequences using nonrigid registration: validation against Doppler tissue velocity. , 2006, Ultrasound in medicine & biology.

[24]  Carlos R. Castro-Pareja,et al.  High-Speed of Three- and Four-dimensional Medical Images by Using Voxel Similarity 1 , 2003 .

[25]  P.L. Carson,et al.  Rapid elastic image registration for 3-D ultrasound , 2002, IEEE Transactions on Medical Imaging.

[26]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[27]  Nassir Navab,et al.  Registration strategies and similarity measures for three-dimensional ultrasound mosaicing. , 2008, Academic radiology.

[28]  Kashif Rajpoot,et al.  The evaluation of single-view and multi-view fusion 3D echocardiography using image-driven segmentation and tracking , 2011, Medical Image Anal..

[29]  YingLiang Ma,et al.  Registration-based propagation for whole heart segmentation from compounded 3D echocardiography , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[30]  Robert D. Howe,et al.  Mitral Annulus Segmentation From 3D Ultrasound Using Graph Cuts , 2010, IEEE Transactions on Medical Imaging.

[31]  Tong Fang,et al.  REGISTRATION OF ULTRASOUND IMAGES USING AN INFORMATION-THEORETIC FEATURE DETECTOR , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[32]  Hervé Delingette,et al.  Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart , 2009 .

[33]  Purang Abolmaesumi,et al.  Deformable registration using scale space keypoints , 2006, SPIE Medical Imaging.

[34]  P. Allain,et al.  Comparison of fusion techniques for 3D+T echocardiography acquisitions from different acoustic windows , 2005, Computers in Cardiology, 2005.

[35]  Andrew Zisserman,et al.  Multiple View Geometry in Computer Vision (2nd ed) , 2003 .

[36]  Purang Abolmaesumi,et al.  Intra-subject elastic registration of 3D ultrasound images , 2006, Medical Image Anal..

[37]  Patrick Clarysse,et al.  A review of cardiac image registration methods , 2002, IEEE Transactions on Medical Imaging.

[38]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[39]  K. Mikolajczyk.  Detection of local features invariant to affine transformations : application to matching and recognition , 2002 .

[40]  J. Alison Noble,et al.  Registration of Multiview Real-Time 3-D Echocardiographic Sequences , 2007, IEEE Transactions on Medical Imaging.

[41]  L. Collins,et al.  A review of calibration techniques for freehand 3-D ultrasound systems. , 2005, Ultrasound in medicine & biology.

[42]  Graeme P. Penney,et al.  Spatial compounding of large sets of 3D echocardiography images , 2009, Medical Imaging.