Intensity‐based 2D‐3D registration for an ACL reconstruction navigation system

To improve the positioning accuracy of tunnels for anterior cruciate ligament (ACL) reconstruction, we proposed an intensity‐based 2D‐3D registration method for an ACL reconstruction navigation system. Methods for digitally reconstructed radiograph (DRR) generation, similarity measurement, and optimization are crucial to 2D‐3D registration. We evaluated the accuracy, success rate, and processing time of different methods: (a) ray‐casting and splating were compared for DRR generation; (b) normalized mutual information (NMI), Mattes mutual information (MMI), and Spearman's rank correlation coefficient (SRC) were assessed for similarity between registrations; and (c) gradient descent (GD) and downhill simplex (DS) were compared for optimization. The combination of splating, SRC, and GD provided the best composite performance and was applied in an augmented reality (AR) ACL reconstruction navigation system. The accuracy of the navigation system could fulfill the clinical needs of ACL reconstruction, with an end pose error of 2.50 mm and an angle error of 2.74°.

[1]  Ma Li-pin,et al.  Comparative study of tibial tunnel enlargement after hamstring anterior cruciate ligament reconstruction by preservation of the remnants of ACL or not , 2014 .

[2]  Grégoire Malandain,et al.  A tree-topology preserving pairing for 3D/2D registration , 2015, International Journal of Computer Assisted Radiology and Surgery.

[3]  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.

[4]  A. Tria,et al.  MIS Techniques in Orthopedics , 2006 .

[5]  Jiankang He,et al.  A Method of Accurate Bone Tunnel Placement for Anterior Cruciate Ligament Reconstruction Based on 3-Dimensional Printing Technology: A Cadaveric Study. , 2017, Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association.

[6]  Luc Soler,et al.  On-patient see-through augmented reality based on visual SLAM , 2016, International Journal of Computer Assisted Radiology and Surgery.

[7]  Marwan Abdellah,et al.  Parallel generation of digitally reconstructed radiographs on heterogeneous multi-GPU workstations , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[8]  Li Ma.  Micro Operation Robot for Optical Precise Assembly , 2009 .

[9]  A H Eskandari,et al.  Subject-specific 2D/3D image registration and kinematics-driven musculoskeletal model of the spine. , 2017, Journal of biomechanics.

[10]  Tomoyuki Mochizuki,et al.  Cadaveric knee observation study for describing anatomic femoral tunnel placement for two-bundle anterior cruciate ligament reconstruction. , 2006, Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association.

[11]  J. Jenny,et al.  Navigated anterior cruciate ligament reconstruction: radiographic validation of a nonimage-based system. , 2012, Orthopedics.

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

[13]  Gerhard Kleinszig,et al.  Automatic masking for robust 3D-2D image registration in image-guided spine surgery , 2016, SPIE Medical Imaging.

[14]  Hong-Yu Chu,et al.  Registration of 2D C-Arm and 3D CT Images for a C-Arm Image-Assisted Navigation System for Spinal Surgery , 2015, Applied bionics and biomechanics.

[15]  Masaaki Takahashi,et al.  Anatomical Study of the Femoral and Tibial Insertions of the Anteromedial and Posterolateral Bundles of Human Anterior Cruciate Ligament , 2006, The American journal of sports medicine.

[16]  Farida Cheriet,et al.  3D-2D Registration Based on Mesh-Derived Image Bisection , 2012, WBIR.

[17]  David J. Hawkes,et al.  A comparison of a similarity-based and a feature-based 2-D-3-D registration method for neurointerventional use , 2005, IEEE Transactions on Medical Imaging.

[18]  Guoyan Zheng,et al.  Gaussian mixture models based 2D–3D registration of bone shapes for orthopedic surgery planning , 2016, Medical & Biological Engineering & Computing.

[19]  Bostjan Likar,et al.  A framework for automatic creation of gold-standard rigid 3D–2D registration datasets , 2017, International Journal of Computer Assisted Radiology and Surgery.

[20]  Bostjan Likar,et al.  A review of 3D/2D registration methods for image-guided interventions , 2012, Medical Image Anal..

[21]  Guoyan Zheng,et al.  Non-rigid free-form 2D-3D registration using a B-spline-based statistical deformation model , 2017, Pattern Recognit..

[22]  Petros Koumoutsakos,et al.  Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.

[23]  G. Kuduvalli,et al.  A fast, accurate, and automatic 2D-3D image registration for image-guided cranial radiosurgery. , 2008, Medical physics.

[24]  Wolfgang Birkfellner,et al.  Fast DRR Generation for 2D/3D Registration , 2005, MICCAI.

[25]  Torsten Rohlfing,et al.  Intensity-based registration algorithm for probabilistic images and its application for 2D to 3D image registration , 2002, SPIE Medical Imaging.

[26]  Yong Ma,et al.  Failed anterior cruciate ligament reconstruction: analysis of factors leading to instability after primary surgery. , 2013, Chinese medical journal.

[27]  Ning Ting-min.  Arthroscopic reconstruction of anterior cruciate ligament with preservation of the remnants , 2007 .

[28]  Russell H. Taylor,et al.  Rendering tetrahedral meshes with higher-order attenuation functions for digital radiograph reconstruction , 2005, VIS 05. IEEE Visualization, 2005..

[29]  Yuehaw Khoo,et al.  Non-Iterative Rigid 2D/3D Point-Set Registration Using Semidefinite Programming , 2015, IEEE Transactions on Image Processing.

[30]  Stefan Hunsche,et al.  Intensity-based 2D 3D registration for lead localization in robot guided deep brain stimulation , 2017, Physics in medicine and biology.

[31]  Pavel Zemcík,et al.  Intensity-based femoral atlas 2D/3D registration using Levenberg-Marquardt optimisation , 2016, SPIE Medical Imaging.

[32]  A Uneri,et al.  3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch , 2016, Physics in medicine and biology.

[33]  Yoshinobu Sato,et al.  Robust patella motion tracking using intensity-based 2D-3D registration on dynamic bi-plane fluoroscopy: towards quantitative assessment in MPFL reconstruction surgery , 2016, SPIE Medical Imaging.

[34]  Zhiqiang Chen,et al.  Reconstruction of 3D Lumvar Vertebra from Two X-ray Images Based on 2D/3D Registration , 2016, CSI@MICCAI.