Intra-operative Update of Boundary Conditions for Patient-Specific Surgical Simulation

Patient-specific Biomechanical Models (PBMs) can enhance computer assisted surgical procedures with critical information. Although pre-operative data allow to parametrize such PBMs based on each patient’s properties, they are not able to fully characterize them. In particular, simulation boundary conditions cannot be determined from preoperative modalities, but their correct definition is essential to improve the PBM predictive capability. In this work, we introduce a pipeline that provides an up-to-date estimate of boundary conditions, starting from the pre-operative model of patient anatomy and the displacement undergone by points visible from an intra-operative vision sensor. The presented pipeline is experimentally validated in realistic conditions on an ex vivo pararenal fat tissue manipulation. We demonstrate its capability to update a PBM reaching clinically acceptable performances, both in terms of accuracy and intra-operative time constraints.

[1]  Nazim Haouchine,et al.  Vision-Based Force Feedback Estimation for Robot-Assisted Surgery Using Instrument-Constrained Biomechanical Three-Dimensional Maps , 2018, IEEE Robotics and Automation Letters.

[2]  Stephane Cotin,et al.  Estimation of boundary conditions for patient-specific liver simulation during augmented surgery , 2020, International Journal of Computer Assisted Radiology and Surgery.

[3]  Olaf Schenk,et al.  Solving unsymmetric sparse systems of linear equations with PARDISO , 2002, Future Gener. Comput. Syst..

[4]  Sébastien Ourselin,et al.  High-Speed Nonlinear Finite Element Analysis for Surgical Simulation Using Graphics Processing Units , 2008, IEEE Transactions on Medical Imaging.

[5]  Sergei Nikolaev,et al.  Data-Driven Simulation for Augmented Surgery , 2020, Advanced Structured Materials.

[6]  Neil Liversedge,et al.  The mechanical properties of human adipose tissues and their relationships to the structure and composition of the extracellular matrix. , 2013, American journal of physiology. Endocrinology and metabolism.

[7]  Nazim Haouchine,et al.  Image-Driven Stochastic Identification of Boundary Conditions for Predictive Simulation , 2017, MICCAI.

[8]  Peter Eisert,et al.  Stereo Correspondence and Reconstruction of Endoscopic Data Challenge , 2021, ArXiv.

[9]  Cuneyt Akinlar,et al.  STag: A Stable Fiducial Marker System , 2017, Image Vis. Comput..

[10]  Olaf Schenk,et al.  Solving unsymmetric sparse systems of linear equations with PARDISO , 2004, Future Gener. Comput. Syst..

[11]  Keenan Crane,et al.  A Laplacian for Nonmanifold Triangle Meshes , 2020, Comput. Graph. Forum.

[12]  Nazim Haouchine,et al.  Patient-Specific Biomechanical Modeling for Guidance During Minimally-Invasive Hepatic Surgery , 2015, Annals of Biomedical Engineering.

[13]  F. Galbusera,et al.  Image-based biomechanical models of the musculoskeletal system , 2020, European Radiology Experimental.

[14]  ZoomOut , 2019, ACM Transactions on Graphics.

[15]  Allan Hanbury,et al.  Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool , 2015, BMC Medical Imaging.

[16]  Christian Duriez,et al.  SOFA: A Multi-Model Framework for Interactive Physical Simulation , 2012 .

[17]  Joan Bruna,et al.  Deep Geometric Prior for Surface Reconstruction , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Zeike A. Taylor,et al.  Prostate motion modelling using biomechanically-trained deep neural networks on unstructured nodes , 2020, MICCAI.

[19]  Karol Miller,et al.  On the prospect of patient-specific biomechanics without patient-specific properties of tissues. , 2013, Journal of the mechanical behavior of biomedical materials.

[20]  Paolo Fiorini,et al.  Data-Driven Intra-Operative Estimation of Anatomical Attachments for Autonomous Tissue Dissection , 2021, IEEE Robotics and Automation Letters.

[21]  Stephane Cotin,et al.  Physics-Based Deep Neural Network for Augmented Reality During Liver Surgery , 2019, MICCAI.

[22]  Jing Ren,et al.  ZoomOut: Spectral Upsampling for Efficient Shape Correspondence , 2019, ACM Trans. Graph..

[23]  Paolo Fiorini,et al.  Physics-Based Deep Neural Network for Real-Time Lesion Tracking in Ultrasound-Guided Breast Biopsy , 2019, Computational Biomechanics for Medicine.

[24]  Stephane Cotin,et al.  Model-Based Identification of Anatomical Boundary Conditions in Living Tissues , 2014, IPCAI.

[25]  Christian Duriez,et al.  On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward , 2020, Proceedings of the National Academy of Sciences.

[26]  Stefanie Speidel,et al.  Non-Rigid Volume to Surface Registration using a Data-Driven Biomechanical Model , 2020, MICCAI.

[27]  Stefanie Speidel,et al.  Learning soft tissue behavior of organs for surgical navigation with convolutional neural networks , 2019, International Journal of Computer Assisted Radiology and Surgery.