Ultrasound registration and tracking for robot-assisted laproscopic surgery

In the past two decades, there has been considerable research interest in medical image registration during surgery. The overlay of medical images over the images from a surgical camera allows the surgeon to see sub-surface features such as tumor boundaries and vasculature. Ultrasound imaging is a prime candidate for medical image registration, as it is a real-time imaging modality and therefore is commonly-used for intraoperative surgical guidance. Prior technologies that attempted ultrasound-based registration have used external trackers in order to establish a geometric correspondence between the surgical cameras and the ultrasound probes; this requires probe and camera calibration, which is time-consuming, requires additional equipment, and adds additional sources of error to the registration. Another problem is how to maintain a registration between the ultrasound image and the underlying tissues, since tissues will move and deform from patient breathing and heartbeat, and from surgical instrument interaction with tissues. In order to overcome this, the underlying tissue should be tracked, and previously acquired ultrasound images should be registered and moved with the tracked tissue. Prior work has had limited success in providing a real-time solution for estimating local tissue deformation and movement; furthermore, there has been no work in estimating the accuracy of maintaining a registration — that is, the accuracy of the registration after having been moved with the tracked tissue. In this work, we establish an image registration method between ultrasound images and endoscopic stereo-cameras using a novel registration tool; this method does not require external tracking or ultrasound probe calibration, thus providing a simple method for performing a registration. In order to maintain an image registration over time, we developed a tissue tracking framework. Its key innovation

[1]  Luc Soler,et al.  Beating heart tracking in robotic surgery using 500 Hz visual servoing, model predictive control and an adaptive observer , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[2]  Chao Liu,et al.  Three-dimensional Motion Tracking for Beating Heart Surgery Using a Thin-plate Spline Deformable Model , 2010, Int. J. Robotics Res..

[3]  Robert Rohling,et al.  Intra-operative "Pick-Up" Ultrasound for Robot Assisted Surgery with Vessel Extraction and Registration: A Feasibility Study , 2011, IPCAI.

[4]  Philippe Poignet,et al.  Three-dimensional heart motion estimation using endoscopic monocular vision system: From artificial landmarks to texture analysis , 2007, Biomed. Signal Process. Control..

[5]  Guang-Zhong Yang,et al.  Affine-invariant anisotropic detector for soft tissue tracking in minimally invasive surgery , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[6]  Mehdi Moradi,et al.  A Robotic System for Intra-operative Trans-Rectal Ultrasound and Ultrasound Elastography in Radical Prostatectomy , 2011, IPCAI.

[7]  C. Schmid,et al.  Indexing based on scale invariant interest points , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  Tobias Ortmaier,et al.  Motion estimation in beating heart surgery , 2005, IEEE Transactions on Biomedical Engineering.

[9]  Chao Liu,et al.  Deformable motion tracking of the heart surface , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[11]  Osamu Ukimura,et al.  Real-time transrectal ultrasound guidance during laparoscopic radical prostatectomy: impact on surgical margins. , 2006, The Journal of urology.

[12]  Robert N Rohling,et al.  Tracking a 3-D ultrasound probe with constantly visible fiducials. , 2007, Ultrasound in medicine & biology.

[13]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[14]  Guang-Zhong Yang,et al.  A Probabilistic Framework for Tracking Deformable Soft Tissue in Minimally Invasive Surgery , 2007, MICCAI.

[15]  Darius Burschka,et al.  DaVinci Canvas: A Telerobotic Surgical System with Integrated, Robot-Assisted, Laparoscopic Ultrasound Capability , 2005, MICCAI.

[16]  Guang-Zhong Yang,et al.  i-BRUSH: A Gaze-Contingent Virtual Paintbrush for Dense 3D Reconstruction in Robotic Assisted Surgery , 2009, MICCAI.

[17]  Andrew D. Wiles,et al.  Virtual reality-enhanced ultrasound guidance: A novel technique for intracardiac interventions , 2008, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[18]  S. Herrell Toward image-guided robotic surgery: Determining intrinsic accuracy of the da Vinci robot , 2007 .

[19]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using unit quaternions , 1987 .

[20]  Antony J Hodgson,et al.  Bone surface localization in ultrasound using image phase-based features. , 2009, Ultrasound in medicine & biology.

[21]  R. Rohling,et al.  Measurement of viscoelastic properties of tissue-mimicking material using longitudinal wave excitation , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[22]  Guang-Zhong Yang,et al.  Soft-Tissue Motion Tracking and Structure Estimation for Robotic Assisted MIS Procedures , 2005, MICCAI.

[23]  Nan Zhang,et al.  Computing Optimised Parallel Speeded-Up Robust Features (P-SURF) on Multi-Core Processors , 2010, International Journal of Parallel Programming.

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

[25]  Po-Wei Hsu,et al.  Comparison of freehand 3-D ultrasound calibration techniques using a stylus. , 2008, Ultrasound in medicine & biology.

[26]  G. Janetschek,et al.  Laparoscopic partial nephrectomy: how far have we gone? , 2007, Current opinion in urology.

[27]  Luc Van Gool,et al.  Fast scale invariant feature detection and matching on programmable graphics hardware , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[28]  Tobias Ortmaier,et al.  Tracking local motion on the beating heart , 2002, SPIE Medical Imaging.

[29]  Gregory D. Hager,et al.  Stereo-Based Endoscopic Tracking of Cardiac Surface Deformation , 2004, MICCAI.

[30]  Takayuki Kitasaka,et al.  An Application Driven Comparison of Several Feature Extraction Algorithms in Bronchoscope Tracking During Navigated Bronchoscopy , 2010, MIAR.

[31]  Jun Hasegawa,et al.  Estimation of the surface topography from monocular endoscopic images , 1994 .

[32]  M. Feuerstein,et al.  Navigation in endoscopic soft tissue surgery: perspectives and limitations. , 2008, Journal of endourology.

[33]  Richard Szeliski,et al.  A Database and Evaluation Methodology for Optical Flow , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[34]  A. Madabhushi,et al.  Distinguishing Lesions from Posterior Acoustic Shadowing in Breast Ultrasound via Non-Linear Dimensionality Reduction , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[35]  Chao Liu,et al.  Efficient 3D Tracking for Motion Compensation in Beating Heart Surgery , 2008, MICCAI.

[36]  Kurt Konolige,et al.  CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching , 2008, ECCV.

[37]  Inderbir S. Gill,et al.  Augmented Reality for Computer-Assisted Image-Guided Minimally Invasive Urology , 2009 .

[38]  Ali Serdar Gözen,et al.  Augmented reality: a new tool to improve surgical accuracy during laparoscopic partial nephrectomy? Preliminary in vitro and in vivo results. , 2009, European urology.

[39]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[40]  W. Eric L. Grimson,et al.  Clinical Experience with a Hich Precision Image-Guided Neurosurgery System , 1998, MICCAI.

[41]  Hugh Durrant-Whyte,et al.  Simultaneous localization and mapping (SLAM): part II , 2006 .

[42]  Haibin Ling,et al.  Deformation invariant image matching , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[43]  Osamu Ukimura,et al.  Real-time transrectal ultrasonography during laparoscopic radical prostatectomy. , 2004, The Journal of urology.

[44]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[45]  Petia Radeva,et al.  Intravascular Ultrasound Images Vessel Characterization Using AdaBoost , 2003, FIMH.

[46]  Thomas Langø,et al.  Probe calibration for freehand 3-D ultrasound. , 2003, Ultrasound in medicine & biology.

[47]  Hans-Peter Meinzer,et al.  Comparing calibration approaches for 3D ultrasound probes , 2008, International Journal of Computer Assisted Radiology and Surgery.

[48]  Guang-Zhong Yang,et al.  Soft tissue tracking for minimally invasive surgery: learning local deformation online. , 2008, Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention.

[49]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[50]  Russell H. Taylor,et al.  Augmented reality during robot-assisted laparoscopic partial nephrectomy: toward real-time 3D-CT to stereoscopic video registration. , 2009, Urology.

[51]  J. Coleman,et al.  Current and future imaging for urologic interventions , 2008, Current opinion in urology.

[52]  R W Prager,et al.  Rapid calibration for 3-D freehand ultrasound. , 1998, Ultrasound in medicine & biology.

[53]  Luc Van Gool,et al.  GPU-Accelerated Robotic Intra-operative Laparoscopic 3D Reconstruction , 2010, IPCAI.

[54]  Guang-Zhong Yang,et al.  Three-Dimensional Tissue Deformation Recovery and Tracking , 2010, IEEE Signal Processing Magazine.

[55]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[56]  Philippe C. Cattin,et al.  Markerless Endoscopic Registration and Referencing , 2006, MICCAI.

[57]  Yakup Genc,et al.  GPU-based Video Feature Tracking And Matching , 2006 .

[58]  Jean-Yves Bouguet,et al.  Camera calibration toolbox for matlab , 2001 .

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

[60]  Hugh F. Durrant-Whyte,et al.  Simultaneous localization and mapping: part I , 2006, IEEE Robotics & Automation Magazine.

[61]  I. Gill,et al.  Imaging-assisted endoscopic surgery: Cleveland Clinic experience. , 2008, Journal of endourology.

[62]  John Kenneth Salisbury,et al.  The Intuitive/sup TM/ telesurgery system: overview and application , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[63]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[64]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

[65]  Gustavo Carneiro,et al.  Automatic Fetal Measurements in Ultrasound Using Constrained Probabilistic Boosting Tree , 2007, MICCAI.

[66]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[67]  Michael C. Yip,et al.  3D Ultrasound to Stereoscopic Camera Registration through an Air-Tissue Boundary , 2010, MICCAI.

[68]  Darius Burschka,et al.  Scale-Invariant Registration of Monocular Endoscopic Images to CT-Scans for Sinus Surgery , 2004, MICCAI.

[69]  Terry M. Peters,et al.  Fused Video and Ultrasound Images for Minimally Invasive Partial Nephrectomy: A Phantom Study , 2010, MICCAI.

[70]  H. G. van der Poel,et al.  Peroperative transrectal ultrasonography‐guided bladder neck dissection eases the learning of robot‐assisted laparoscopic prostatectomy , 2008, BJU international.

[71]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[72]  Daisuke Deguchi,et al.  Hybrid Bronchoscope Tracking Using a Magnetic Tracking Sensor and Image Registration , 2005, MICCAI.

[73]  Terry M. Peters,et al.  Fusion of stereoscopic video and laparoscopic ultrasound for minimally invasive partial nephrectomy , 2009, Medical Imaging.

[74]  Po-Wei Hsu,et al.  Freehand 3D Ultrasound Calibration: A Review , 2009 .

[75]  Philippe Poignet,et al.  Motion prediction for tracking the beating heart , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[76]  R. Thoranaghatte,et al.  Endoscope-based hybrid navigation system for minimally invasive ventral spine surgeries , 2005, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[77]  Yoshihiko Nakamura,et al.  Heartbeat synchronization for robotic cardiac surgery , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).