3D ultrasound-CT registration of the liver using combined landmark-intensity information

PurposeAn important issue in computer-assisted surgery of the liver is a fast and reliable transfer of preoperative resection plans to the intraoperative situation. One problem is to match the planning data, derived from preoperative CT or MR images, with 3D ultrasound images of the liver, acquired during surgery. As the liver deforms significantly in the intraoperative situation non-rigid registration is necessary. This is a particularly challenging task because pre- and intraoperative image data stem from different modalities and ultrasound images are generally very noisy.MethodsOne way to overcome these problems is to incorporate prior knowledge into the registration process. We propose a method of combining anatomical landmark information with a fast non-parametric intensity registration approach. Mathematically, this leads to a constrained optimization problem. As distance measure we use the normalized gradient field which allows for multimodal image registration.ResultsA qualitative and quantitative validation on clinical liver data sets of three different patients has been performed. We used the distance of dense corresponding points on vessel center lines for quantitative validation. The combined landmark and intensity approach improves the mean and percentage of point distances above 3 mm compared to rigid and thin-plate spline registration based only on landmarks.ConclusionThe proposed algorithm offers the possibility to incorporate additional a priori knowledge—in terms of few landmarks—provided by a human expert into a non-rigid registration process.

[1]  E. Haber,et al.  Numerical methods for volume preserving image registration , 2004 .

[2]  A Fenster,et al.  Evaluation of voxel-based registration of 3-D power Doppler ultrasound and 3-D magnetic resonance angiographic images of carotid arteries. , 2001, Ultrasound in medicine & biology.

[3]  Hans-Peter Meinzer,et al.  Bildverarbeitung für die Medizin 2007, Algorithmen, Systeme, Anwendungen, Proceedings des Workshops vom 25.-27. März 2007 in München , 2007, Bildverarbeitung für die Medizin.

[4]  David J. Hawkes,et al.  Registration of freehand 3D ultrasound and magnetic resonance liver images , 2004, Medical Image Anal..

[5]  Julien Jomier,et al.  Registration and Analysis of Vascular Images , 2003, International Journal of Computer Vision.

[6]  N. Papenberg,et al.  A Fast and Flexible Image Registration Toolbox Design and Implementation of the General Approach , 2006 .

[7]  Terry M. Peters,et al.  Ultrasound/MRI Overlay with Image Warping for Neurosurgery , 2000, MICCAI.

[8]  D. Rubens,et al.  Three-dimensional registration and fusion of ultrasound and MRI using major vessels as fiducial markers , 2001, IEEE Transactions on Medical Imaging.

[9]  Logan W. Clements,et al.  Concepts and Preliminary Data Toward the Realization of Image-guided Liver Surgery , 2007, Journal of Gastrointestinal Surgery.

[10]  K. Do,et al.  Standardized measurement of the future liver remnant prior to extended liver resection: methodology and clinical associations. , 2000, Surgery.

[11]  Heinz-Otto Peitgen,et al.  Impact of virtual tumor resection and computer-assisted risk analysis on operation planning and intraoperative strategy in major hepatic resection. , 2005, Archives of surgery.

[12]  Jürgen Weese,et al.  Landmark-based elastic registration using approximating thin-plate splines , 2001, IEEE Transactions on Medical Imaging.

[13]  Markus Kleemann,et al.  Intraoperative online navigation of dissection of the hepatical tissue - a new dimension in liver surgery? , 2004, CARS.

[14]  Jan Modersitzki,et al.  Numerical Methods for Image Registration , 2004 .

[15]  William M. Wells,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 , 1998, Lecture Notes in Computer Science.

[16]  C. Broit Optimal registration of deformed images , 1981 .

[17]  Steven A Curley,et al.  Extended hepatectomy in patients with hepatobiliary malignancies with and without preoperative portal vein embolization. , 2002, Archives of surgery.

[18]  Karl Rohr,et al.  Hybrid Spline-Based Elastic Image Registration Using Analytic Solutions of the Navier Equation , 2007, Bildverarbeitung für die Medizin.

[19]  Maxime Descoteaux,et al.  Validation of vessel-based registration for correction of brain shift , 2007, Medical Image Anal..

[20]  Nicholas Ayache,et al.  Rigid registration of 3-D ultrasound with MR images: a new approach combining intensity and gradient information , 2001, IEEE Transactions on Medical Imaging.

[21]  Bernhard Preim,et al.  Analysis of vasculature for liver surgical planning , 2002, IEEE Transactions on Medical Imaging.

[22]  Karl Rohr,et al.  Physics-Based Elastic Image Registration Using Splines and Including Landmark Localization Uncertainties , 2006, MICCAI.

[23]  Imran A. Pirwani,et al.  Introduction to the Non-rigid Image Registration Evaluation Project (NIREP) , 2006, WBIR.

[24]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[25]  Jan Modersitzki,et al.  Combination of automatic non-rigid and landmark based registration: the best of both worlds , 2003, SPIE Medical Imaging.

[26]  Thomas Lange,et al.  Vessel-Based Non-Rigid Registration of MR/CT and 3D Ultrasound for Navigation in Liver Surgery , 2003, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[27]  P M Schlag,et al.  Registration of different phases of contrast‐enhanced CT/MRI data for computer‐assisted liver surgery planning: Evaluation of state‐of‐the‐art methods , 2005, The international journal of medical robotics + computer assisted surgery : MRCAS.

[28]  J. Modersitzki,et al.  Combining landmark and intensity driven registrations , 2003 .

[29]  T. Utsunomiya,et al.  Postoperative liver failure after major hepatic resection for hepatocellular carcinoma in the modern era with special reference to remnant liver volume. , 1999, Journal of the American College of Surgeons.

[30]  Markus W. Büchler,et al.  Liver Surgery in the Era of Tissue-preserving Resections: Early and Late Outcome in Patients with Primary and Secondary Hepatic Tumors , 2002, World Journal of Surgery.

[31]  Julian A. Kim,et al.  Determinants of Survival following Hepatic Resection for Metastatic Colorectal Cancer , 1998, World Journal of Surgery.

[32]  Thomas Lange,et al.  Image registration for CT and intra-operative ultrasound data of the liver , 2008, SPIE Medical Imaging.

[33]  A. Khotanzad,et al.  A physics-based coordinate transformation for 3-D image matching , 1997, IEEE Transactions on Medical Imaging.

[34]  Thomas Lange,et al.  Feasibility of Navigated Resection of Liver Tumors Using Multiplanar Visualization of Intraoperative 3-dimensional Ultrasound Data , 2007, Annals of surgery.

[35]  Fred L. Bookstein,et al.  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Karl Rohr,et al.  A New Class of Elastic Body Splines for Nonrigid Registration of Medical Images , 2005, Journal of Mathematical Imaging and Vision.

[37]  Karl Rohr,et al.  Landmark-Based Image Analysis , 2001, Computational Imaging and Vision.

[38]  Jan Modersitzki Image Registration with Local Rigidity Constraints , 2007, Bildverarbeitung für die Medizin.

[39]  E. Haber,et al.  Intensity Gradient Based Registration and Fusion of Multi-modal Images , 2007, Methods of Information in Medicine.

[40]  Grace Wahba,et al.  Spline Models for Observational Data , 1990 .

[41]  Eldad Haber,et al.  A Multilevel Method for Image Registration , 2005, SIAM J. Sci. Comput..

[42]  Thomas Lange,et al.  Augmenting Intraoperative 3D Ultrasound with Preoperative Models for Navigation in Liver Surgery , 2004, MICCAI.

[43]  P M Schlag,et al.  Image‐guided surgery of liver metastases by three‐dimensional ultrasound‐based optoelectronic navigation , 2007, The British journal of surgery.

[44]  Lawrence H. Schwartz,et al.  Volumetric analysis predicts hepatic dysfunction in patients undergoing major liver resection , 2003, Journal of Gastrointestinal Surgery.

[45]  Stefan Heldmann,et al.  A Fast and Flexible Image Registration Toolbox , 2007, Bildverarbeitung für die Medizin.

[46]  Jan Modersitzki,et al.  FLIRT: A Flexible Image Registration Toolbox , 2003, WBIR.

[47]  Jan Modersitzki,et al.  FLIRT with Rigidity—Image Registration with a Local Non-rigidity Penalty , 2008, International Journal of Computer Vision.

[48]  Thomas Lange,et al.  A Distance Measure for Non-Rigid Registration of Geometrical Models to Intensity Data , 2007 .

[49]  Thomas Lange,et al.  Validation Metrics for Non-rigid Registration of Medical Images Containing Vessel Trees , 2008, Bildverarbeitung für die Medizin.

[50]  L H Blumgart,et al.  Clinical score for predicting recurrence after hepatic resection for metastatic colorectal cancer: analysis of 1001 consecutive cases. , 1999, Annals of surgery.

[51]  D. Louis Collins,et al.  Clinical validation of vessel-based registration for correction of brain-shift , 2007, Medical Image Anal..