Real-Time FEM-Based Registration of 3-D to 2.5-D Transrectal Ultrasound Images

We present a novel technique for real-time deformable registration of 3-D to 2.5-D transrectal ultrasound (TRUS) images for image-guided, robot-assisted laparoscopic radical prostatectomy (RALRP). For RALRP, a pre-operatively acquired 3-D TRUS image is registered to thin-volumes comprised of consecutive intra-operative 2-D TRUS images, where the optimal transformation is found using a gradient descent method based on analytical first and second order derivatives. Our method relies on an efficient algorithm for real-time extraction of arbitrary slices from a 3-D image deformed given a discrete mesh representation. We also propose and demonstrate an evaluation method that generates simulated models and images for RALRP by modeling tissue deformation through patient-specific finite-element models (FEM). We evaluated our method on in-vivo data from 11 patients collected during RALRP and focal therapy interventions. In the presence of an average landmark deformation of 3.89 and 4.62 mm, we achieved accuracies of 1.15 and 0.72 mm, respectively, on the synthetic and in-vivo data sets, with an average registration computation time of 264 ms, using MATLAB on a conventional PC. The results show that the real-time tracking of the prostate motion and deformation is feasible, enabling a real-time augmented reality-based guidance system for RALRP.]

[1]  Dean C. Barratt,et al.  Population-based prediction of subject-specific prostate deformation for MR-to-ultrasound image registration , 2015, Medical Image Anal..

[2]  Jack Bresenham,et al.  Algorithm for computer control of a digital plotter , 1965, IBM Syst. J..

[3]  Sheng Xu,et al.  Fusion of real-time transrectal ultrasound with pre-acquired MRI for multi-modality prostate imaging , 2007, SPIE Medical Imaging.

[4]  Orcun Goksel,et al.  B-Mode Ultrasound Image Simulation in Deformable 3-D Medium , 2009, IEEE Transactions on Medical Imaging.

[5]  Aaron Fenster,et al.  Efficient Convex Optimization Approach to 3D Non-rigid MR-TRUS Registration , 2013, MICCAI.

[6]  Orcun Goksel,et al.  Prostate Brachytherapy Training With Simulated Ultrasound and Fluoroscopy Images , 2013, IEEE Transactions on Biomedical Engineering.

[7]  Aaron Fenster,et al.  Repeat prostate biopsy accuracy: simulator-based comparison of two- and three-dimensional transrectal US modalities. , 2010, Radiology.

[8]  Nico Karssemeijer,et al.  Biomechanical modeling constrained surface-based image registration for prostate MR guided TRUS biopsy. , 2015, Medical physics.

[9]  Purang Abolmaesumi,et al.  Automatic Localization of the da Vinci Surgical Instrument Tips in 3-D Transrectal Ultrasound , 2013, IEEE Transactions on Biomedical Engineering.

[10]  Sheng Xu,et al.  Closed-Loop Control in Fused MR-TRUS Image-Guided Prostate Biopsy , 2007, MICCAI.

[11]  Purang Abolmaesumi,et al.  A 2D-3D Registration Framework for Freehand TRUS-Guided Prostate Biopsy , 2015, MICCAI.

[12]  Aaron Fenster,et al.  3D prostate MR-TRUS non-rigid registration using dual optimization with volume-preserving constraint , 2016, SPIE Medical Imaging.

[13]  Omid Mohareri,et al.  Intraoperative registered transrectal ultrasound guidance for robot-assisted laparoscopic radical prostatectomy. , 2015, The Journal of urology.

[14]  Guy Nir,et al.  Model-based registration of ex vivo and in vivo MRI of the prostate using elastography , 2013, IEEE Transactions on Medical Imaging.

[15]  Orcun Goksel,et al.  Deformable prostate registration from MR and TRUS images using surface error driven FEM models , 2012, Medical Imaging.

[16]  Pingkun Yan,et al.  Magnetic resonance imaging/ultrasound fusion guided prostate biopsy improves cancer detection following transrectal ultrasound biopsy and correlates with multiparametric magnetic resonance imaging. , 2011, The Journal of urology.

[17]  Jocelyne Troccaz,et al.  3D-2D ultrasound feature-based registration for navigated prostate biopsy: A feasibility study , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[18]  A. Fenster,et al.  Real‐time registration of 3D to 2D ultrasound images for image‐guided prostate biopsy , 2017, Medical physics.

[19]  Nassir Navab,et al.  Multimodal image-guided prostate fusion biopsy based on automatic deformable registration , 2015, International Journal of Computer Assisted Radiology and Surgery.

[20]  Purang Abolmaesumi,et al.  Statistical Biomechanical Surface Registration: Application to MR-TRUS Fusion for Prostate Interventions , 2015, IEEE Transactions on Medical Imaging.

[21]  Septimiu E. Salcudean,et al.  A System for MR-Ultrasound Guidance during Robot-Assisted Laparoscopic Radical Prostatectomy , 2015, MICCAI.

[22]  Aaron Fenster,et al.  Clinical application of a 3D ultrasound-guided prostate biopsy system. , 2011, Urologic oncology.

[23]  Jurgen J Fütterer,et al.  Accuracy of multiparametric MRI for prostate cancer detection: a meta-analysis. , 2014, AJR. American journal of roentgenology.

[24]  Jasjit S. Suri,et al.  Rapid motion compensation for prostate biopsy using GPU , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[25]  Jocelyne Troccaz,et al.  Prostate biopsy tracking with deformation estimation , 2011, Medical Image Anal..