Josh Passenger

On modelling of anisotropic viscoelasticity for soft tissue simulation: Numerical solution and GPU execution

Efficient and accurate techniques for simulation of soft tissue deformation are an increasingly valuable tool in many areas of medical image computing, such as biomechanically-driven image registration and interactive surgical simulation. For reasons of efficiency most analyses are based on simplified linear formulations, and previously almost all have ignored well established features of tissue mechanical response such as anisotropy and time-dependence. We address these latter issues by firstly presenting a generalised anisotropic viscoelastic constitutive framework for soft tissues, particular cases of which have previously been used to model a wide range of tissues. We then develop an efficient solution procedure for the accompanying viscoelastic hereditary integrals which allows use of such models in explicit dynamic finite element algorithms. We show that the procedure allows incorporation of both anisotropy and viscoelasticity for as little as 5.1% additional cost compared with the usual isotropic elastic models. Finally we describe the implementation of a new GPU-based finite element scheme for soft tissue simulation using the CUDA API. Even with the inclusion of more elaborate constitutive models as described the new implementation affords speed improvements compared with our recent graphics API-based implementation, and compared with CPU execution a speed up of 56.3 x is achieved. The validity of the viscoelastic solution procedure and performance of the GPU implementation are demonstrated with a series of numerical examples.

Efficient Nonlinear FEM for Soft Tissue Modelling and Its GPU Implementation within the Open Source Framework SOFA

Accurate biomechanical modelling of soft tissue is a key aspect for achieving realistic surgical simulations. However, because medical simulation is a multi-disciplinary area, researchers do not always have sufficient resources to develop an efficient and physically rigorous model for organ deformation. We address this issue by implementing a CUDA-based nonlinear finite element model into the SOFA open source framework. The proposed model is an anisotropic visco-hyperelastic constitutive formulation implemented on a graphical processor unit (GPU). After presenting results on the models performance we illustrate the benefits of its integration within the SOFA framework on a simulation of cataract surgery.

DEVELOPING A NEXT GENERATION COLONOSCOPY SIMULATOR

Colonoscopy is considered the gold standard for detection and removal of precancerous polyps in the colon. Being a difficult procedure to master, exposure to a large variety of patient and pathology scenarios is crucial for gastroenterologists’ training. Currently, most training is done on patients under supervision of experienced gastroenterologists. Being able to undertake a majority of training on simulators would greatly reduce patient risk and discomfort. A next generation colonoscopy simulator is currently under development, which aims to address the shortfalls of existing simulators. The simulator consists of a computer simulation of the colonoscope camera view and a haptic device that allows insertion of an instrumented colonoscope to drive the simulation and provide force feedback to the user. The simulation combines physically accurate models of the colonoscope, colon and surrounding tissues and organs with photorealistic visualization. It also includes the capability to generate randomized case scenarios where complexity of the colon physiology, pathology and environmental factors, such as colon preparation, can be tailored to suit training requirements. The long term goal is to provide a metrics based training and skill evaluation system that is not only useful for trainee instruction but can be leveraged for skills maintenance and eventual certification.

Instrumentation of a clinical colonoscope for surgical simulation

This paper describes the instrumentation of a clinical colonoscope needed for a novel colonoscopy simulation framework. The simulator consists of a compact and portable haptic interface and a virtual reality environment to provide real-time visualization. The proposed instrumentation enables tracking different functions of the colonoscope while keeping the ergonomic unchanged.

Pneumatic haptic interface fuzzy controller for simulation of abdominal palpations during colonoscopy

Pneumatic control has been used for armature control of robots with pneumatic air muscles achieving accurate position control of the piston actuator with inexpensive solenoid valves. We describe a pneumatic fuzzy controller associated with a pulse width modulation conversion scheme in a novel haptic device within a colonoscopy simulation system. A rubber bladder was used to simulate forces experienced during abdominal palpation during colonoscopy. The haptic device showed good steady-state response when tested with step inputs. A settling time of 0.41s was achieved on positive control step and 0.52–0.81s for negative steps. Dynamic response was adequate for mimicking interactions during inflation stages while noticeably deficient during deflation periods. Tracking accuracy averaged 94.2% within 300ms of the reference input while the user was actively applying abdominal palpation and minor repositioning.

Modelling Anisotropic Viscoelasticity for Real-Time Soft Tissue Simulation

Previously almost all biomechanically-based time-critical surgical simulation has ignored the well established features of tissue mechanical response of anisotropy and time-dependence. We address this issue by presenting an efficient solution procedure for anisotropic viscohyperelastic constitutive models which allows use of these in nonlinear explicit dynamic finite element algorithms. We show that the procedure allows incorporation of both anisotropy and viscoelasticity for as little as 5.1% additional cost compared with the usual isotropic elastic models. When combined with high performance GPU execution the complete framework is suitable for time-critical simulation applications such as interactive surgical simulation and intraoperative image registration.

Echtzeit-Ultraschallsimulation auf Grafik-Prozessoren mit CUDA

Trotz der zunehmenden Verbreitung jungerer bildgebender Verfahren bleibt medizinischer Ultraschall (US) weiterhin ein wichtiges Hilfsmittel bei chirurgischen Eingriffen und der klinischen Diagnose. Viele US-gestutzte medizinische Prozeduren erfordern allerdings ausgiebiges Training, so dass es wunschenswert ist, eine realistische Simulation von US-Bildern zur Verfugung zu stellen. Im Gegensatz zu fruheren Ansatzen simulieren wir solche Bilder auf der „Graphics Processing Unit“. Wir erweitern hierzu eine Methode, die von Wein et al. fur die Abschatzung von US-Reflexionen aus Daten der Computertomographie (CT) vorgeschlagen wurde, zu einer leichter zu berechnenden Form. Zusatzlich schatzen wir die US-Absorption aus den CT-Daten ab. Mit Hilfe von NVIDIAs „Compute Unified Device Architecture“ (CUDA) simulieren wir Reflexion, Verschattung, Rauschen und radiale Unscharfe, ausgehend von unbearbeiteten CT-Daten in Echtzeit und ohne Vorausberechnung.

Deforming a High-Resolution Mesh in Real-Time by Mapping onto a Low-Resolution Physical Model

For interactive surgical simulation the physical model of the soft tissue needs to be solved in real-time. This limits the attainable model density to well below the desired mesh density for visual realism. Previous work avoids this problem by using a high-resolution visual mesh mapped onto a low-resolution physical model. We apply the same approach and present an computationally cheap implementation of a known algorithm to avoid texture artefacts caused by the mapping. We also introduce a spline-based algorithm to prevent groups of high-resolution vertices, mapped to the same low-resolution triangle, from exhibiting movements in which the underlying low-resolution structure can be recognised. The resulting mapping algorithm is very efficient, mapping 54,000 vertices in 8.5 ms on the CPU and in 0.88 ms on the GPU. Consequently, the density of the high-resolution visual mesh is limited only by the detail of the CT data from which the mesh was generated.