Franck Patrick Vidal

Principles and Applications of Computer Graphics in Medicine

The medical domain provides excellent opportunities for the application of computer graphics, visualization and virtual environments, with the potential to help improve healthcare and bring benefits to patients. This survey paper provides a comprehensive overview of the state‐of‐the‐art in this exciting field. It has been written from the perspective of both computer scientists and practising clinicians and documents past and current successes together with the challenges that lie ahead. The article begins with a description of the software algorithms and techniques that allow visualization of and interaction with medical data. Example applications from research projects and commercially available products are listed, including educational tools; diagnostic aids; virtual endoscopy; planning aids; guidance aids; skills training; computer augmented reality and use of high performance computing. The final section of the paper summarizes the current issues and looks ahead to future developments.

Tuning of Patient-Specific Deformable Models Using an Adaptive Evolutionary Optimization Strategy

We present and analyze the behavior of an evolutionary algorithm designed to estimate the parameters of a complex organ behavior model. The model is adaptable to account for patients specificities. The aim is to finely tune the model to be accurately adapted to various real patient datasets. It can then be embedded, for example, in high fidelity simulations of the human physiology. We present here an application focused on respiration modeling. The algorithm is automatic and adaptive. A compound fitness function has been designed to take into account for various quantities that have to be minimized. The algorithm efficiency is experimentally analyzed on several real test cases: 1) three patient datasets have been acquired with the “breath hold” protocol, and 2) two datasets corresponds to 4-D CT scans. Its performance is compared with two traditional methods (downhill simplex and conjugate gradient descent): a random search and a basic real-valued genetic algorithm. The results show that our evolutionary scheme provides more significantly stable and accurate results.

A prototype percutaneous transhepatic cholangiography training simulator with real-time breathing motion

PurposeWe present here a simulator for interventional radiology focusing on percutaneous transhepatic cholangiography (PTC). This procedure consists of inserting a needle into the biliary tree using fluoroscopy for guidance.MethodsThe requirements of the simulator have been driven by a task analysis. The three main components have been identified: the respiration, the real-time X-ray display (fluoroscopy) and the haptic rendering (sense of touch). The framework for modelling the respiratory motion is based on kinematics laws and on the Chainmail algorithm. The fluoroscopic simulation is performed on the graphic card and makes use of the Beer-Lambert law to compute the X-ray attenuation. Finally, the haptic rendering is integrated to the virtual environment and takes into account the soft-tissue reaction force feedback and maintenance of the initial direction of the needle during the insertion.ResultsFive training scenarios have been created using patient-specific data. Each of these provides the user with variable breathing behaviour, fluoroscopic display tuneable to any device parameters and needle force feedback.ConclusionsA detailed task analysis has been used to design and build the PTC simulator described in this paper. The simulator includes real-time respiratory motion with two independent parameters (rib kinematics and diaphragm action), on-line fluoroscopy implemented on the Graphics Processing Unit and haptic feedback to feel the soft-tissue behaviour of the organs during the needle insertion.

Interventional radiology virtual simulator for liver biopsy

Purpose xa0xa0xa0Training in Interventional Radiology currently uses the apprenticeship model, where clinical and technical skills of invasive procedures are learnt during practice in patients. This apprenticeship training method is increasingly limited by regulatory restrictions on working hours, concerns over patient risk through trainees’ inexperience and the variable exposure to case mix and emergencies during training. To address this, we have developed a computer-based simulation of visceral needle puncture procedures.Methodsxa0xa0xa0A real-time framework has been built that includes: segmentation, physically based modelling, haptics rendering, pseudo-ultrasound generation and the concept of a physical mannequin. It is the result of a close collaboration between different universities, involving computer scientists, clinicians, clinical engineers and occupational psychologists.Resultsxa0xa0xa0The technical implementation of the framework is a robust and real-time simulation environment combining a physical platform and an immersive computerized virtual environment. The face, content and construct validation have been previously assessed, showing the reliability and effectiveness of this framework, as well as its potential for teaching visceral needle puncture.Conclusionxa0xa0xa0A simulator for ultrasound-guided liver biopsy has been developed. It includes functionalities and metrics extracted from cognitive task analysis. This framework can be useful during training, particularly given the known difficulties in gaining significant practice of core skills in patients.

New genetic operators in the fly algorithm: application to medical PET image reconstruction

This paper presents an evolutionary approach for image reconstruction in positron emission tomography (PET). Our reconstruction method is based on a cooperative coevolution strategy (also called Parisian evolution): the “fly algorithm”. Each fly is a 3D point that mimics a positron emitter. The flies’ position is progressively optimised using evolutionary computing to closely match the data measured by the imaging system. The performance of each fly is assessed using a “marginal evaluation” based on the positive or negative contribution of this fly to the performance of the population. Using this property, we propose a “thresholded-selection” method to replace the classical tournament method. A mitosis operator is also proposed. It is triggered to automatically increase the population size when the number of flies with negative fitness becomes too low.

Simulation of X-ray Attenuation on the GPU

Abstract In this paper, we propose to take advantage of computer graphics hardware to achievean accelerated simulation of X-ray transmission imaging, and we compare results with afast and robust software-only implementation. The running times of the GPU and CPUimplementations are compared in different test cases. The results show that the GPUimplementation with full floating point precision is faster by a factor of about 60 to 65than the CPU implementation, without any significant loss of accuracy. The increase inperformance achieved with GPU calculations opens up new perspectives. Notably, it pavesthewayforphysically-realisticsimulationofX-rayimagingininteractivetime. Categories and Subject Descriptors (accordingtoACMCCS):I.3.5ComputerGraphics:Physically based modeling; I.3.7 Computer Graphics: Raytracing; J.2 Computer Applica-tions: Physics. Keywords: Three-Dimensional Graphics and Realism, Raytracing, Physical Sciences andEngineering,Physics. 1 Introduction The simulation of X-ray imaging techniques such as radiography or tomography is extensivelystudied in the physics community and different physically-based simulation codes are available.Deterministic methods based on ray-tracing are commonly used to compute direct images (i.e.images formed by the X-ray beam transmitted without interaction through the scanned object)ofcomputer-aideddesign(CAD)models. Ray-tracingprovidesafastalternativetoMonteCarlomethods [4]. Such programs are very useful to optimize experiment parameters, to conceiveimagingsystems,ortotakeintoaccountnon-destructivetestingduringthedesignofamechanicalstructure[1,10]. However,evenwithfastraytracingalgorithms,thesimulationofcomplexX-rayimagingsystemsstillrequiresverylongcomputationtimesandisnotsuitableforaninteractiveuseaswouldberequiredinamedicaltrainingtool.Physics-basedsimulationsaretraditionallyperformedonCPUs. However,thereisagrowinginterestforgeneral-purposecomputationonGPUs(GPGPU)andthishasbeenanactiveareaofresearchsometime[13].Inthispaper,wepresentanefficientsimulationofX-rayattenuationthroughcomplexobjects,thatmakesuseofthecapabilityimprovementoftoday’sgraphicscards. Wealsocomparetheper-formanceofthisGPUapproachwithanefficientsoftware-onlyimplementation. ToourknowledgethisisthefirstGPU-basedX-Rayattenuationsimulation. Suchasimulationtoolcanbedeployedinmedicalvirtualinteractiveapplicationsfortrainingfluoroscopyguidanceofneedles,cathetersand guidewires [18], and can also be useful to speed-up current physics-based simulation wherecomputationalaccuracyiscritical.

Artificial evolution for 3D PET reconstruction

This paper presents a method to take advantage of artificial evolution in positron emission tomography reconstruction. This imaging technique produces datasets that correspond to the concentration of positron emitters through the patient. Fully 3D tomographic reconstruction requires high computing power and leads to many challenges. Our aim is to reduce the computing cost and produce datasets while retaining the required quality. Our method is based on a coevolution strategy (also called Parisian evolution) named fly algorithm. Each fly represents a point of the space and acts as a positron emitter. The final population of flies corresponds to the reconstructed data. Using marginal evaluation, the flys fitness is the positive or negative contribution of this fly to the performance of the population. This is also used to skip the relatively costly step of selection and simplify the evolutionary algorithm.

Evolutionary Art Using the Fly Algorithm

This study is about Evolutionary art such as digital mosaics. The most common techniques to generate a digital mosaic effect heavily rely on Centroidal Voronoi diagrams. Our method generates artistic images as an optimisation problem without the introduction of any a priori knowledge or constraint other than the input image. We adapt a cooperative co-evolution strategy based on the Parisian evolution approach, the Fly algorithm, to produce artistic visual effects from an input image (e.g. a photograph). The primary usage of the Fly algorithm is in computer vision, especially stereo-vision in robotics. It has also been used in image reconstruction for tomography. Until now the individuals correspond to simplistic primitives: Infinitely small 3-D points. In this paper, the individuals have a much more complex representation and represent tiles in a mosaic. They have their own position, size, colour, and rotation angle. We take advantage of graphics processing units (GPUs) to generate the images using the modern OpenGL Shading Language. Different types of tiles are implemented, some with transparency, to generate different visual effects, such as digital mosaic and spray paint. A user study has been conducted to evaluate some of our results. We also compare results with those obtained with GIMP, an open-source software for image manipulation.

Development and Validation of Real-time Simulation of X-ray Imaging with Respiratory Motion

We present a framework that combines evolutionary optimisation, soft tissue modelling and ray tracing on GPU to simultaneously compute the respiratory motion and X-ray imaging in real-time. Our aim is to provide validated building blocks with high fidelity to closely match both the human physiology and the physics of X-rays. A CPU-based set of algorithms is presented to model organ behaviours during respiration. Soft tissue deformation is computed with an extension of the Chain Mail method. Rigid elements move according to kinematic laws. A GPU-based surface rendering method is proposed to compute the X-ray image using the Beer-Lambert law. It is provided as an open-source library. A quantitative validation study is provided to objectively assess the accuracy of both components: (i) the respiration against anatomical data, and (ii) the X-ray against the Beer-Lambert law and the results of Monte Carlo simulations. Our implementation can be used in various applications, such as interactive medical virtual environment to train percutaneous transhepatic cholangiography in interventional radiology, 2D/3D registration, computation of digitally reconstructed radiograph, simulation of 4D sinograms to test tomography reconstruction tools.

A Method to Compute Respiration Parameters for Patient-based Simulators

We propose a method to automatically tune a patient-based virtual environment training simulator for abdominal needle insertion. The key attributes to be customized in our framework are the elasticity of soft-tissues and the respiratory model parameters. The estimation is based on two 3D Computed Tomography (CT) scans of the same patient at two different time steps. Results are presented on four patients and show that our new method leads to better results than our previous studies with manually tuned parameters.