Subject-specific models for image-guided cardiac surgery

Three-dimensional visualization for planning and guidance is still not routinely available for minimally invasive cardiac surgery (MICS). This can be addressed by providing the surgeon with subject-specific geometric models derived from 3D preoperative images for planning of port locations or to rehearse the procedure. For guidance purposes, these models can also be registered to the subject using intraoperative images. In this paper, we present a method for extracting subject-specific heart geometry from preoperative MR images. The main obstacle we face is the low quality of clinical data in terms of resolution, signal-to-noise ratio, and presence of artefacts. Instead of using these images directly, we approach the problem in three steps: (1) generate a high quality template model, (2) register the template with the preoperative data, and (3) animate the result over the cardiac cycle. Validation of this approach showed that dynamic subject-specific models can be generated with a mean error of 3.6+/-1.1 mm from low resolution target images (6 mm slices). Thus, the models are sufficiently accurate for MICS training and procedure planning. In terms of guidance, we also demonstrate how the resulting models may be adapted to the operating room using intraoperative ultrasound imaging.

[1]  David Atkinson,et al.  A study of the motion and deformation of the heart due to respiration , 2002, IEEE Transactions on Medical Imaging.

[2]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[3]  Juha Koikkalainen,et al.  Statistical shape model of atria, ventricles and epicardium from short- and long-axis MR images , 2004, Medical Image Anal..

[4]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[5]  M. Schwerzmann,et al.  Gender differences in coronary artery size per 100 g of left ventricular mass in a population without cardiac disease. , 2001, Swiss medical weekly.

[6]  Milan Sonka,et al.  3-D active appearance models: segmentation of cardiac MR and ultrasound images , 2002, IEEE Transactions on Medical Imaging.

[7]  Holger Wendland,et al.  Piecewise polynomial, positive definite and compactly supported radial functions of minimal degree , 1995, Adv. Comput. Math..

[8]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[9]  Karl Rohr,et al.  Radial basis functions with compact support for elastic registration of medical images , 2001, Image Vis. Comput..

[10]  Cristian Lorenz,et al.  A comprehensive shape model of the heart , 2006, Medical Image Anal..

[11]  Terry M. Peters,et al.  A High Resolution Dynamic Heart Model Based on Averaged MRI Data , 2003, MICCAI.

[12]  Max A. Viergever,et al.  Registration-based interpolation , 2004, IEEE Transactions on Medical Imaging.

[13]  Ève Coste-Manière,et al.  Planning and Simulation of Robotically Assisted Minimal Invasive Surgery , 2000, MICCAI.

[14]  Nicolas Courty,et al.  Accelerating 3D Non-Rigid Registration Using Graphics Hardware , 2008, Int. J. Image Graph..

[15]  Terry Peters,et al.  Dose reduction for cardiac CT using a registration-based approach. , 2007, Medical physics.

[16]  Jean Meunier,et al.  Average Brain Models: A Convergence Study , 2000, Comput. Vis. Image Underst..

[17]  W D Spotnitz,et al.  Minimally invasive coronary artery bypass grafting decreases hospital stay and cost. , 1997, Annals of surgery.

[18]  B Kiaii,et al.  A comparison of robot-assisted versus manually constructed endoscopic coronary anastomosis. , 2000, The Annals of thoracic surgery.

[19]  Daniel Rueckert,et al.  Atlas-Based Segmentation and Tracking of 3D Cardiac MR Images Using Non-rigid Registration , 2002, MICCAI.

[20]  Stefan Schaller,et al.  Multi-detector row CT versus coronary angiography: preoperative evaluation before totally endoscopic coronary artery bypass grafting. , 2003, Radiology.

[21]  Alejandro F Frangi,et al.  Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration , 2003, IEEE Transactions on Medical Imaging.

[22]  Olivier Ecabert,et al.  Automatic Whole Heart Segmentation in Static Magnetic Resonance Image Volumes , 2007, MICCAI.

[23]  T. Peters Image-guidance for surgical procedures , 2006, Physics in medicine and biology.

[24]  Louaï Adhami,et al.  Planning, simulation, and augmented reality for robotic cardiac procedures: The STARS system of the ChIR team. , 2003, Seminars in thoracic and cardiovascular surgery.

[25]  Alejandro F. Frangi,et al.  Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling , 2002, IEEE Transactions on Medical Imaging.

[26]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1995, Proceedings of IEEE International Conference on Computer Vision.

[27]  Jayaram K. Udupa,et al.  Shape-based interpolation of multidimensional grey-level images , 1996, IEEE Trans. Medical Imaging.

[28]  Joy C. Peacock,et al.  Robotic techniques improve quality of life in patients undergoing atrial septal defect repair. , 2004, The Annals of thoracic surgery.

[29]  Carolyn A. Bucholtz,et al.  Shape-based interpolation , 1992, IEEE Computer Graphics and Applications.

[30]  Terry M. Peters,et al.  Validation of dynamic heart models obtained using non-linear registration for virtual reality training, planning, and guidance of minimally invasive cardiac surgeries , 2004, Medical Image Anal..

[31]  Terry M. Peters,et al.  Towards Subject-Specific Models of the Dynamic Heart for Image-Guided Mitral Valve Surgery , 2007, MICCAI.

[32]  Chris Wedlake,et al.  Image-guided laser projection for port placement in minimally invasive surgery. , 2006, Studies in health technology and informatics.

[33]  T. Peters,et al.  High Quality Appearance Models of Heart Sub-Components Based on MR Images , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.