A clinically applicable laser-based image-guided system for laparoscopic liver procedures

PurposeLaser range scanners (LRS) allow performing a surface scan without physical contact with the organ, yielding higher registration accuracy for image-guided surgery (IGS) systems. However, the use of LRS-based registration in laparoscopic liver surgery is still limited because current solutions are composed of expensive and bulky equipment which can hardly be integrated in a surgical scenario.MethodsIn this work, we present a novel LRS-based IGS system for laparoscopic liver procedures. A triangulation process is formulated to compute the 3D coordinates of laser points by using the existing IGS system tracking devices. This allows the use of a compact and cost-effective LRS and therefore facilitates the integration into the laparoscopic setup. The 3D laser points are then reconstructed into a surface to register to the preoperative liver model using a multi-level registration process.ResultsExperimental results show that the proposed system provides submillimeter scanning precision and accuracy comparable to those reported in the literature. Further quantitative analysis shows that the proposed system is able to achieve a patient-to-image registration accuracy, described as target registration error, of $$3.2\pm 0.57\,\hbox {mm}$$3.2±0.57mm.ConclusionsWe believe that the presented approach will lead to a faster integration of LRS-based registration techniques in the surgical environment. Further studies will focus on optimizing scanning time and on the respiratory motion compensation.

[1]  Eric Friets,et al.  Endoscopic laser range scanner for minimally invasive, image guided kidney surgery , 2013, Medical Imaging.

[2]  Naoki Suzuki,et al.  Laser-scan endoscope system for intraoperative geometry acquisition and surgical robot safety management , 2006, Medical Image Anal..

[3]  Naoki Suzuki,et al.  Intraoperative Fast 3D Shape Recovery of Abdominal Organs in Laparoscopy , 2002, MICCAI.

[4]  J Schlaier,et al.  Registration accuracy and practicability of laser-directed surface matching. , 2002, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

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

[6]  E. Sjolie,et al.  3D ultrasound-based navigation for radiofrequency thermal ablation in the treatment of liver malignancies , 2003, Surgical Endoscopy And Other Interventional Techniques.

[7]  Valerie Duay,et al.  A method to track cortical surface deformations using a laser range scanner , 2005, IEEE Transactions on Medical Imaging.

[8]  Benoit M Dawant,et al.  Incorporation of a laser range scanner into image-guided liver surgery: surface acquisition, registration, and tracking. , 2003, Medical physics.

[9]  Erlend Fagertun Hofstad,et al.  Navigated laparoscopy – liver shift and deformation due to pneumoperitoneum in an animal model , 2012, Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy.

[10]  Andreas H. Mahnken,et al.  CT- and MR-guided interventions in radiology , 2009 .

[11]  R. Galloway,et al.  Feasibility Studies of Frameless Stereotactic Liver Surgery , 1999 .

[12]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  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.

[14]  Derek L. G. Hill,et al.  Registration Methodology: Concepts and Algorithms , 2001 .

[15]  W. O'Dell,et al.  Modeling liver motion and deformation during the respiratory cycle using intensity-based nonrigid registration of gated MR images. , 2004, Medical physics.

[16]  Robert J. Webster,et al.  Minimally Invasive Holographic Surface Scanning for Soft-Tissue Image Registration , 2010, IEEE Transactions on Biomedical Engineering.

[17]  J.-Angelo Beraldin,et al.  INTEGRATION OF LASER SCANNING AND CLOSE-RANGE PHOTOGRAMMETRY – THE LAST DECADE AND BEYOND , 2004 .

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

[19]  Logan W. Clements,et al.  Model-updated image-guided liver surgery: preliminary results using surface characterization. , 2010, Progress in Biophysics and Molecular Biology.

[20]  Colin P McDonald,et al.  A comparison of registration techniques for computer- and image-assisted elbow surgery , 2007, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[21]  Paul J. Besl,et al.  Method for registration of 3-D shapes , 1992, Other Conferences.

[22]  Terry M. Peters,et al.  An integrated range-sensing, segmentation and registration framework for the characterization of intra-surgical brain deformations in image-guided surgery , 2003, Comput. Vis. Image Underst..

[23]  Haiying Liu,et al.  A Generic Framework for Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations , 2001, MICCAI.

[24]  Heinz-Otto Peitgen,et al.  Assessment of Intraoperative Liver Deformation During Hepatic Resection: Prospective Clinical Study , 2010, World Journal of Surgery.

[25]  J. L. Herring,et al.  Surface registration for use in interactive, image-guided liver surgery. , 2000 .

[26]  C F Buck,et al.  Clinical efficacy of high frequency jet ventilation during extracorporeal shock wave lithotripsy of renal and ureteral calculi: a comparison with conventional mechanical ventilation. , 1988, The Journal of urology.

[27]  David J. Hawkes,et al.  A Statistical Model of Respiratory Motion and Deformation of the Liver , 2001, MICCAI.

[28]  Robert J. Webster,et al.  Comparison Study of Intraoperative Surface Acquisition Methods for Surgical Navigation , 2013, IEEE Transactions on Biomedical Engineering.

[29]  P Biro,et al.  High-frequency jet ventilation for minimizing breathing-related liver motion during percutaneous radiofrequency ablation of multiple hepatic tumours. , 2009, British journal of anaesthesia.

[30]  John R. Warmath,et al.  Ultrasound-to-computer-tomography registration for image-guided laparoscopic liver surgery , 2005, Surgical Endoscopy And Other Interventional Techniques.

[31]  M Shoham,et al.  Image-guided system with miniature robot for precise positioning and targeting in keyhole neurosurgery , 2006, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[32]  Kenneth I. Joy,et al.  Uncertainty, Baseline, and Noise Analysis for L1 Error-Based Multi-view Triangulation , 2014, 2014 22nd International Conference on Pattern Recognition.

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

[34]  Hao Li,et al.  Global Correspondence Optimization for Non‐Rigid Registration of Depth Scans , 2008, Comput. Graph. Forum.

[35]  Yoshitaka Masutani,et al.  Modally Controlled Free Form Deformation for Non-rigid Registration in Image-Guided Liver Surgery , 2001, MICCAI.