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

Abstract

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.

Deep Learning-Based Facial Appearance Simulation Driven by Surgically Planned Craniomaxillofacial Bony Movement

“…In the future, we will improve the simulation accuracy by adaptively sampling the points to include more anatomical details in clinically critical regions and integrate incremental learning strategy into our network. In particular, our work can be easily extended to other surgical applications that require correspondence mapping between different shapes/point sets [10,14].…”

Autonomous tissue retraction with a biomechanically informed logic based framework

“…Furthermore, we plan to exploit the output from the SA module to continuously update the simulated environment and compensate for possible modelling uncertainties [34]. In particular, we will integrate strategies to update APs during interaction with the tissue, such as [27], [35], compensating for partial or imprecise knowledge about their position at the beginning of the task. Extension to more complex surgical tasks would also require the integration with more sophisticated motion planning strategies [4].…”

Adding Safety Rules to Surgeon-Authored Virtual Reality Training

“…Early multilayered tissue models for orthopedic trauma surgery were based on 3-dimensional (3D) mass-spring systems accelerated with graphics hardware 9 . Using finite elements, real-time simulation of cardiac electrophysiology, preoperative planning of cryosurgery, and peroperative guidance for laparoscopy has been based on the open-source SOFA soft tissue simulation platform 10–13 . A real-time neurosurgery simulation of skull drilling and surgical interaction with the brain 14 has been reported, and Cecil et al 15 describe a virtual surgical environment for training residents in less invasive stabilization system surgery used to address fractures of the femur.…”

“…9 Using finite elements, real-time simulation of cardiac electrophysiology, preoperative planning of cryosurgery, and peroperative guidance for laparoscopy has been based on the open-source SOFA soft tissue simulation platform. [10][11][12][13] A real-time neurosurgery simulation of skull drilling and surgical interaction with the brain 14 has been reported, and Cecil et al 15 describe a virtual surgical environment for training residents in less invasive stabilization system surgery used to address fractures of the femur. Mitchell et al 16 presented a framework for interactive outlining of regions for simulation of reconstructive plastic surgery.…”