Bello, Fernando

Bulpitt, Andrew

Gould, Derek

John, Nigel

Vidal, Franck

Zhang, Yan

English

We present an integrated system for training visceral needle puncture procedures. Our aim is to provide a cost effective and validated training tool that uses actual patient data to enable interventional radiology trainees to learn how to carry out image-guided needle puncture. The input data required is a computed tomography scan of the patient that is used to create the patient specific models. Force measurements have been made on real tissue and the resulting data is incorporated into the simulator. Respiration and soft tissue deformations are also carried out to further improve the fidelity of the simulator

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ImaGiNe-SBello, Fernando ; Bulpitt, Andrew ; Gould, Derek; Vidal, Franck; John, Nigel; Zhang, YanPublication date:2009Document VersionAuthor accepted manuscriptLink to publication in ResearchOnlineCitation for published version (Harvard):Bello, F, Bulpitt, A, Gould, D, Vidal, F, John, N & Zhang, Y 2009, 'ImaGiNe-S: imaging guided needle simulation',Paper presented at 30th Annual Conference of the European Association for Computer Graphics, Munich,Germany, 30/03/09 - 3/04/09 pp. 5-8.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.Take down policyIf you believe that this document breaches copyright please view our takedown policy at https://edshare.gcu.ac.uk/id/eprint/5179 for detailsof how to contact us.Download date: 29. Jun. 2022ImaGINe-S: Imaging Guided Interventional NeedleSimulationFernando Bello 1, Andrew Bulpitt 2, Derek A. Gould 3, Richard Holbrey 2,Carrie Hunt 4, Thien How 3, Nigel W. John 5, Sheena Johnson 4,Roger Phillips 6, Amrita Sinha 3, Franck P. Vidal 5, Pierre-Frédéric Villard 1,Helen Woolnough 4, and Yan Zhang 61 Biosurgery and Surgical Technology Department, Imperial College, London, UK2 School of Computing, The University of Leeds, UK3 Royal Liverpool University Hospital, UK4 Manchester Business School, Manchester University, UK5 School of Computer Science, Bangor University, UK6 Department Computer Science, University of Hull, UKAbstractWe present an integrated system for training visceral needle puncture procedures. Ouraim is to provide a cost effective and validated training tool that uses actual patient datato enable interventional radiology trainees to learn how to carry out image-guided needlepuncture. The input data required is a computed tomography scan of the patient that isused to create the patient specific models. Force measurements have been made on realtissue and the resulting data is incorporated into the simulator. Respiration and soft tissuedeformations are also carried out to further improve the fidelity of the simulator.Keywords: Computer graphics, physically based modelling, virtual reality.1 IntroductionInterventional radiology (IR) uses minimal access techniques to treat a range of conditions, withgenerally less risk and post-operative pain, and shorter recovery time compared to open surgery.IR procedures use medical imaging to guide needles and other instruments to target specific pa-tient anatomy and pathology. Vascular IR techniques involve blood vessels (arteries, veins), whilevisceral IR is concerned with organ systems (liver, bile ducts, urinary tract, gut). Fluoroscopicimaging and ultrasound (US) are the most commonly employed imaging modalities. Fluoroscopyis the mainstay for image guidance in some procedures such as percutaneous transhepatic cholan-giography (PTC); US is a key imaging modality in biopsy and nephrostomy [5].Basic IR skills such as visceral needle puncture are still acquired during an apprenticeship inpatients, though a trainee’s inexpert manipulations can increase patients’ pain and discomfort, aswell as the procedure time and overall risk. A computer-based simulation can offer an attract-ive alternative, and this has been the motivation for developing ImaGINe-S (Imaging GuidedInterventional Needle Simulation). ImaGINe-S delivers a cost-effective virtual environment inwhich a trainee can practice and develop basic skills. It has been designed following a comprehens-ive task analysis of needle puncture procedures. A stereoscopic projection of the virtual patientis co-located with haptic feedback devices to provide a high fidelity simulation – see Figure 1.Patient-specific data is used and the simulator can be configured for a variety of different scen-Figure 1: ImaGINe-S Platform: A virtual workbench with two PHANToM Omni haptic devices(one for the needle and one for the ultrasound transducer)arios. ImaGINe-S is currently undergoing a series of validation studies at hospitals and trainingworkshops.To our knowledge, the Haptic Operative Realistic Ultrasonography Simulator (HORUS) is theonly other computer-based simulator to address needle puncture [1] and is particularly aimed atUS-guided needle insertion for biopsy and radio frequency thermal ablation. The unique differen-tiators of ImaGINe-S are that:• It is based on a detailed task analysis.• It provides an immersive virtual environment that closely replicates the set up of the reallife situation.• It models organ motion due to respiration together with simulation of fluoroscopy or USimages, and can do this interactively by exploiting both the central processor unit (CPU)and the graphics processor unit (GPU) capabilities of a modern PC.• In vitro studies of the forces obtained from needle insertion into tissues is being used to helpcreate a realistic haptics model.• Patient specific data is used.2• It is being fully validated as a training tool.2 Methods and ToolsThe main components of ImaGINe-S are described in this section.2.1 Task analysisA detailed task analysis of the procedure is essential to identify the individual steps and to docu-ment the decision process involved at each point. This information can be obtained by analysingvideo footage of the procedure being carried out, and through interviews with subject experts. Ourpsychologist team have carried out this process together with the subject matter experts [3]. Thisinformation then informs the actual design and realisation of the simulator. Pre-procedure stepsand general preparation such as cleaning the incision site and injection of local anaesthetic havealso been documented in this way. All of this information must be provided within the trainingcurriculum. At this time, however, our simulation begins with the ultrasound image guided needlepuncture.The task analysis also identified the key metrics to be measured in the simulator and usedfor validation. The metrics are performance indicators and include: any errors made, puncturesite location, time taken, path taken by the needle and the final catheter position. Each of thesemetrics will be further assessed during validation of ImaGINe-S to allow investigation of theirutility as performance indicators.2.2 Patient specific dataWith patient consent, selected, routine image data sets (computed tomography, magnetic reson-ance, ultrasound) of straightforward and complex anatomy and pathology was anonymised anduploaded to a repository. Data sets are then segmented using interactive processes to label targetanatomy. This is used to create surface (triangular) meshes and volume (tetrahedral) meshes.Currently, ImaGINe-S uses selected patient case studies. However, the support for patient specificdata allows us to extend the use of the simulator to also include pre-operative rehearsal.2.3 Hardware set upCommercial off the shelf components have been used. A desktop PC with Intel Xeon 3GHzCPU, 2GB of RAM, and a NVIDIA Quadro FX graphics card provides the computational powerrequired. For the needle puncture step, two PHANToM Omni haptic devices have been connectedto the PC using the high speed IEEE 1394 (FireWire) interface – one to act as an ultrasound (US)transducer, and the other to act as the needle. These are housed within the virtual workbenchsystem that can be seen in Figure 1. To provide face validity, and based on the task analysis, thefollowing steps have been taken:• The Omni’s are carefully positioned so that the operator’s hands are in the correct positionwhen using the needle and/or probe. A custom made stand holds the Omni’s in place.• A wrist rest can be placed into the workspace at the appropriate height to allow the operatorto rest his hand during the procedure. In the actual task, some radiologists will rest theirhand against the patient’s body.• The simulated ultrasound or fluoroscopy image is output to a second monitor, not super-imposed in a window within the 3D virtual environment. This represents the real situationwhere the radiologist looks away from the patient to focus on the image being displayed onthe scanner.• The stylus of the Omni can be replaced with a mock-up of a transducer.3Figure 2: Respiration - diaphragm motion is simulated by the up and down motion of the centraltendon coupled with its rib attachments2.4 Algorithms and softwareImaGINe-S has been coded in C++ and makes use of OpenGL, GLSL (the OpenGL ShadingLanguage), the H3D haptics library from Sensegraphics, and a wxPython interface to allow patientparameters to be changed interactively.Our first prototype platform used static CT volume data [7]. In reality, however, the image databeing viewed will alter dynamically in response to respiration and deformation forces introducedby the needle insertion. We have developed a methodology for deriving such dynamic volumerendering from patient imaging data. The deformation module has been implemented on the CPU.After studying the physiology involved, we chose to control the respiration by the rib cage and thediaphragm. Mesh models of the relevant anatomy are obtained through standard segmentationtechniques [9]. Ribs are modeled as rigid bodies with kinematic laws, while diaphragm motion issimulated by the up and down motion of the central tendon coupled with its rib attachments [8].Soft-tissue deformation is handled by an extended Chain Mail algorithm allowing fast multi-organ interaction [4]. Parameters to simulate the deformation behaviour control compression,stretching and shearing. Figure 2 illustrates the effect of the diaphragm moving during exhalation.Deformations due to the needle insertion require the detection of collisions between the needle andthe organ concerned. This is performed using the H3D API and allows us to know the area of thecollision and the magnitude of the touching force. The deformation is then computed in real-timeusing the mass-spring method. Full details of our algorithm can be found in [9]The X-ray simulation has been implemented on the GPU. It is a three pass algorithm usingan OpenGL pipeline. For each X-ray pixel, the first pass computes tissue penetration, the secondcomputes an intermediary result required in the final pass to compute the cumulative attenuationusing the Beer-Lambert law. An example of the images produced can be seen in Figure 3.The ultrasound simulation is also implemented on the GPU. Currently, a 2D multi-planarreconstruction (MPR) image is extracted from the CT voxel data set based on the position andorientation of the transducer. This can be efficiently achieved using the OpenGL frame bufferobject architecture (FBO). All voxels that have been penetrated by the needle are assigned ahigh value corresponding to the metallic material of the needle shaft, which reflects US. Acousticshadowing effects are simulated by postprocessing the MPR image to compute a shadow mask,and high frequency noise is also added. Bright reflections may occur in US images at interfacessuch as with bone, gas, and fat/tissue. This effect can be produced in the final image by detectingand enhancing horizontal edges in the MPR image using a Sobel filter. Finally, the MPR image,the shadow mask and the noise data are blended using multi-texturing. Images from both linearand curvilinear US probes can be generated; an example can be seen in Figure 4.As the trainee moves the Omni that is used as the US transducer over the skin of the virtualpatient, an US-like image corresponding to the position and orientation of the image plane is4Figure 3: X-Ray image calculated on the GPUFigure 4: Example of US-like images generated in real time from original CT datacomputed and displayed in real-time (see Figure 5). The force of the probe against the skin can befelt by the operator. Similarly, the sensation of puncturing into the viscera with the virtual needlecan be felt through the feedback provided by the second Omni. The force feedback delivered invirtual environment simulators is generally an approximation to a real procedure, as assessed byexperts. Haptics based on actual procedural forces should allow a more realistic simulation of thesubtle cues [2]. In evaluating needle puncture procedures in vitro studies are essential for detailedunderstanding of the physical components and effects of overall summated forces, and we arecollecting such experimental data [6]. The observed behaviour can then be modelled analyticallyusing a radial-basis function (RBF) network, where each RBF corresponds to a Gaussian function.Thus we obtain essential information on tissue properties and we use this to help create a hapticslook up table (LUT). The position of the tip of the virtual needle is tracked as it penetrates thevirtual patient. The voxel in which the needle tip currently resides can therefore be mapped tothe original CT data set. The CT Hounsfield value of that voxel is used as the index to the LUT,and it returns an appropriate force co-efficient depending on the tissue type. A force calculation(using Hooke’s Law) is then applied to the haptics device. Full details of how the haptic forcesare calculated can be found in [7].5Figure 5: The virtual patient with ultrasound probe and needle. The needle is clearly visiblein the current ultrasound image. Note that the ultrasound image will be displayed on a separatemonitor. It is shown here for illustration purposes.3 ResultsThe most important question in any project to build a medical virtual environment is: doesit actually improve the trainees skill at carrying out the procedure without negative trainingartefacts? Extensive validation is therefore an ongoing process run in parallel to the developmentphase. Following the attainment of ethical approval, experienced and trainee subjects have beenrecruited within the partner hospitals. Construct and content validation studies are currentlybeing carried out and with ethical approval, we have collected data from pilot studies at theCardiovascular and Interventional Radiological Society of Europe (CIRSE 2008, Copenhagen) andthe British Society of Interventional Radiology (BSIR 2008, Manchester). 53 subjects (49 male,4 female: 30 trainees and 23 subject matter experts) underwent baseline testing on a simulatedpercutaneous nephrostomy. Face and content validation were assessed using a 5-point Likertscale. Outcomes showed that 41/53 (78%) participants thought that the design of ImaGINe-S wasmoderately realistic with content validity being rated averagely for all critical task steps. 44/53(83%) participants thought that ImaGINe-S is a useful model for training skills for nephrostomy.Our preliminary results indicate the usefulness of ImaGINe-S for training skills for neph-rostomy. With further development, it will allow trainees to also train the basic skills of per-cutaneous renal collecting system access. Improvements are being made as a result of these pilotstudies and further assessment of face and content validity is underway.4 ConclusionsAnonymised patient datasets form an essential basis for construction of ‘virtual reality’ trainingmodels such as that offered by ImaGINe-S. Training scenarios can be developed and validated in theacademic environment, combining the skills of a multi-disciplinary team of experts in computerscience, psychology, and medicine. Initial results augur well for the fidelity of our simulator.Further construct and concurrent validation, and skills transfer studies, form a key part of future,proposed work. This will be conducted within IR curricula to evaluate the clinical effectivenessof the simulator to train visceral needle puncture procedures. Other functionality, such as theinclusion of palpation support, is also planned.ImaGINe-S is now set to move into a commercialisation stage. Medic Vision Ltd. (Melbourne,Australia) have been involved in the project to develop this simulator and are currently drawingup productisation plans to package and sell the simulator to hospitals.6References[1] Clément Forest, Olivier Comas, Christophe Vaysière, Luc Soler, and Jacques Marescaux. Ul-trasound and needle insertion simulators built on real patient-based data. In J. D. Westwood,R. S. Haluck, H. M. Hoffman, G. T. Mogeland R. Phillips, R. A. Robb, and K. G. Vosburgh,editors, Medicine Meets Virtual Reality 15, volume 125 of Studies in Health Technology andInformatics, pages 136–139. IOS Press, 2007.[2] Oleg Gerovichev, Panadda Marayong, and Akkison M. Okamura. The effect of visual andhaptic feedback on computer-assisted needle insertion. Computer Aided Surgery, 9(6):243–249, 2004. doi:10.1080/10929080500190441.[3] S. J. Johnson, A. E. Healey, J. C. Evans, M. G. Murphy, M. Crawshaw, and D. Gould. Physicaland cognitive task analysis in interventional radiology. Journal of Clinical Radiology, 61(1):97–103, January 2006. doi:10.1016/j.crad.2005.09.003.[4] Ying Li and Ken Brodlie. Soft object modelling with generalised chainmail - extendingthe boundaries of web-based graphics. Computer Graphics Forum, 22(4):717–727, 2003.doi:10.1111/j.1467-8659.2003.00719.x.[5] T. A. Matalon and B. Silver. US guidance of interventional procedures. Radiology, 174(1):43–47, January 1990. URL http://radiology.rsnajnls.org/cgi/content/abstract/174/1/43.[6] F. P. Vidal, A. E. Healey, N. W. John, and D. A. Gould. Force penetration of chiba needlesfor haptic rendering in ultrasound guided needle puncture training simulator. MICCAI 2008Workshop on Needle Steering: Recent Results and Future Opportunities, September 2008.[7] F. P. Vidal, N. W. John, A. E. Healey, and D. A. Gould. Simulation of ultrasound guidedneedle puncture using patient specific data with 3D textures and volume haptics. ComputerAnimation and Virtual Worlds, 2008. doi:10.1002/cav.217.[8] P.-F. Villard, W. Bourne, and F. Bello. Modelling organ deformation using mass-springs andtensional integrity. In Proc. Biomedical Simulation 2008, volume 5104 of Lecture Notes inComputer Science, pages 221–226. Springer, July 2008.[9] P.-F. Villard, M. Jacob, D. A. Gould, and F. Bello. Haptic simulation of the liver withrespiratory motion. In Medicine Meets Virtual Reality 17, volume 142 of Studies in HealthTechnology and Informatics, pages 401–406. IOS Press, February 2009.7

ImaGiNe-S:imaging guided needle simulation

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ImaGiNe-S:imaging guided needle simulation

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