Interventional radiology virtual simulator for liver biopsy
- 506 Downloads
Training 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.
A 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.
The 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.
A 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.
KeywordsBiomedical computing Image segmentation Simulation Virtual reality
We would like to thank the late Roger Phillips for his significant contributions to the project. We acknowledge funding from the UK’s Department of Health through the Health Technology Devices programme.
Conflict of Interest
P. F. Villard, F. P. Vidal, L. ap Cenydd, R. Holbrey, S. Pisharody, S. Johnson, A. Bulpitt, N.W. John, F. Bello and D. Gould declare that they have no conflict of interest.
- 3.Chi Y, Cashman PMM, Bello F, Kitney RI (2007) A discussion on the evaluation of a new automatic liver volume segmentation method for specified CT image datasets. In: van Ginneken B, Heimann T, Styner M (eds) workshop on 3D segmentation in the clinic: a grand challenge. Med Image Comput Comput Assist Interv. MICCAI, pp 167–175Google Scholar
- 4.Coles TR, John NW, Gould DA, Caldwell DG (2011) Integrating haptics with augmented reality in a femoral palpation and needle insertion training simulation. IEEE Trans Haptics 4(3): 199–209Google Scholar
- 8.Forest C, Comas O, Vaysière C, Soler L, Marescaux J (2007) Ultrasound and needle insertion simulators built on real patient-based data. In: Proceedings of MMVR 15, pp 136–139Google Scholar
- 10.Gibson SF (1997) 3D chainmail: a fast algorithm for deforming volumetric objects. In: Proceedings of the symppsium on interactive 3D graphics. ACM, New York, pp 149–154. ISBN 0-89791-884-3. http://doi.acm.org/10.1145/253284.253324
- 11.Hoppe H, DeRose T, Duchamp T, Mcdonald J, Stuetzle W (1992) Surface reconstruction from unorganized points. In: Computer graphics (SIGGRAPH ’92 proceedings), pp 71–78Google Scholar
- 12.Ibanez L, Schroeder W, Ng L, Cates J (2005) The ITK software guide, 2nd edn. Kitware. ISBN 1-930934-15-7. http://www.itk.org/ItkSoftwareGuide.pdf
- 13.Johnson S, Hunt C, Woolnough H, Crawshaw M, Kilkenny C, Gould D, Sinha A, England A, Villard PF (2011) Virtual reality, ultrasound-guided liver biopsy simulator: development and performance discrimination. Br J Radiol. doi: 10.1259/bjr/47436030
- 14.Karuppasamy K, Zhai J, How T, Gould D (2008) Development and validation of an unobtrusive sensor for in-vivo force data collection during interventional procedures. Cardiovasc Interv Radiol 32: 22–23 Google Scholar
- 17.Lovquist E, O’Sullivan O, Oh’Ainle D, Baitson G, Shorten G, Avis N (2011) Vr-based training and assessment in ultrasound-guided regional anesthesia: from error analysis to system design. In: MMVR, vol 163. IOS Press, pp 304–310Google Scholar
- 19.Maurin B, Barbé L, Bayle B, Zanne P, Gangloff J, de Mathelin M, Soler L, Forgione A (2004) In vivo study of forces during needle insertions. In: Proceedings of the medical robotics, navigation and visualisation scientific workshop 2004. Germany, RemagenGoogle Scholar
- 22.Parikh SS, Chan S, Agrawal SK, Hwang PH, Salisbury CM, Rafii BY, Varma G, Salisbury KJ, Blevins NH (2009) Integration of patient-specific paranasal sinus computed tomographic data into a virtual surgical environment. Am J Rhinol Allergy 23(4):442–7. doi: 10.2500/ajra.2009.23.3335 PubMedCrossRefGoogle Scholar
- 23.Shams R, Hartley R, Navab N (2008) Real-time simulation of medical ultrasound from CT images. LNCS 5242:734–741Google Scholar
- 26.van Gerwen DJ, Dankelman J, van den Dobbelsteen JJ (2012) Needle-tissue interaction forces—a survey of experimental data. Med Eng Phys Google Scholar
- 29.Villard PF, Boshier P, Bello F, Gould D (2011) Virtual reality simulation of liver biopsy with a respiratory component. In: Takahashi H (ed) Liver biopsy. InTech. http://hal.inria.fr/inria-00621263
- 31.Wahba G (1990) Spline models for observational data, vol 59. Society for Industrial and, Applied Mathematics, PhilaedelphiaGoogle Scholar