The Community for Technology Leaders
RSS Icon
Issue No.12 - Dec. (2011 vol.17)
pp: 2163-2172
Bernhard Kainz , Graz University of Technology
Judith Muehl , Graz University of Technology
Rostislav Khlebnikov , Graz University of Technology
In modern clinical practice, planning access paths to volumetric target structures remains one of the most important and most complex tasks, and a physician's insufficient experience in this can lead to severe complications or even the death of the patient. In this paper, we present a method for safety evaluation and the visualization of access paths to assist physicians during preoperative planning. As a metaphor for our method, we employ a well-known, and thus intuitively perceivable, natural phenomenon that is usually called crepuscular rays. Using this metaphor, we propose several ways to compute the safety of paths from the region of interest to all tumor voxels and show how this information can be visualized in real-time using a multi-volume rendering system. Furthermore, we show how to estimate the extent of connected safe areas to improve common medical 2D multi-planar reconstruction (MPR) views. We evaluate our method by means of expert interviews, an online survey, and a retrospective evaluation of 19 real abdominal radio-frequency ablation (RFA) interventions, with expert decisions serving as a gold standard. The evaluation results show clear evidence that our method can be successfully applied in clinical practice without introducing substantial overhead work for the acting personnel. Finally, we show that our method is not limited to medical applications and that it can also be useful in other fields.
Accessibility, ray casting, medical visualization.
Bernhard Kainz, Judith Muehl, Rostislav Khlebnikov, "Crepuscular Rays for Tumor Accessibility Planning", IEEE Transactions on Visualization & Computer Graphics, vol.17, no. 12, pp. 2163-2172, Dec. 2011, doi:10.1109/TVCG.2011.184
[1] I. Altrogge, T. Kröger, T. Preusser, C. Büskens, P. L. Pereira, D. Schmidt, A. Weihusen, and H. O. Peitgen, Towards optimization of probe placement for radio-frequency ablation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 4190 of LNCS, pages 486–493. Springer, 2006.
[2] D. F. Alwin and J. A. Krosnick, The Reliability of Survey Attitude Measurement. Sociological Methods & Research, 20 (1): 139–181, Aug. 1991.
[3] A. M. Alyassin, J. L. Lancaster, J. H. Downs, and P. T. Fox, Evaluation of new algorithms for the interactive measurement of surface area and volume. Med. Phys., 6: 741–52, 1994.
[4] F. M. Andrews, Construct validity and error components of survey measures: A structural modeling approach. Public Opinion Quarterly, 48 (2): 409–442, 1984.
[5] C. Baegert, C. Villard, P. Schreck, and L. Soler, Multi-criteria trajectory planning for hepatic radiofrequency ablation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 4792/200 of LNCS, pages 676–684, 2007.
[6] C. Baegert, C. Villard, P. Schreck, and L. Soler, Precise determination of regions of interest for hepatic RFA planning. In SPIE Medical Imaging 2007: Visualization and Image-Guided Procedures, volume 6509, pages 650923–650923–8, 2007.
[7] I. Baran, J. Chen, J. Ragan-Kelley, F. Durand, and J. Lehtinen, A hierarchical volumetric shadow algorithm for single scattering. ACM Trans. Graph., 29:178:1–178:10, December 2010.
[8] J. Beyer, M. Hadwiger, S. Wolfsberger, and K. Buhler, High-quality multimodal volume rendering for preoperative planning of neurosurgi-cal interventions. IEEE Trans. on Visualization and Computer Graphics, 13: 1696–1703, 2007.
[9] E. J. L. Brunenberg, A. Vilanova, V. Visser-Vandewalle, Y. Temel, L. Ackermans, B. Platel, and B. M. T. H. Romeny, Automatic trajectory planning for deep brain stimulation: a feasibility study. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), LNCS, pages 584–592. Springer, 2007.
[10] N. Chentanez, R. Alterovitz, D. Ritchie, L. Cho, K. K. Hauser, K. Goldberg, J. R. Shewchuk, and J. F. O'Brien., Interactive simulation of surgical needle insertion and steering. In ACM Trans. Graph., page 88:188:10. ACM, 2009.
[11] A. C. Colchester, J. Zhao, K. S. Holton-Tainter, C. J. Henri, N. Maitland, P. T. Roberts, C. G. Harris, and R. J. Evans, Development and preliminary evaluation of VISLAN, a surgical planning and guidance system using intra-operative video imaging. Med. Image Analysis, 1 (1): 73–90, Mar. 1996.
[12] L. S. Davis and M. L. Benedikt, Computational models of space: Isovists and isovist fields. Computer Graphics and Image Processing, 11 (1): 49– 72, Sept. 1979.
[13] S. DiMaio, N. Archip, N. Hata, I.-F. Talos, S. Warfield, A. Majumdar, N. Mcdannold, K. Hynynen, P. Morrison, W. W. III, D. Kacher, R. Ellis, A. Golby, P. Black, F. Jolesz, and R. Kikinis, Image-guided Neurosurgery at Brigham and Women's Hospital. 25 (5): 67–73, 09 2006.
[14] C. Essert, C. Haegelen, and P. Jannin, Automatic computation of electrodes trajectory for deep brain stimulation. In Proceedings of the 5th international conference on Medical imaging and augmented reality, MIAR'10, pages 149–158, Berlin, Heidelberg, 2010. Springer-Verlag.
[15] I. Fujishiro and Y. Takeshima, Solid fitting: Field interval analysis for effective volume exploration. In Dagstuhl '97, Scientific Visualization, pages 65–70, Washington, DC, USA, 1999. IEEE Computer Society.
[16] B. Geveci, U. Ayachit, J. Baumes, M. Bostock, V. Ogievetsky, B. Wylie, T. M. Shead, E. Santos, T. Ropinski, and J.-S. Praßni, DIY vis applications. In Tutorial at the IEEE Visualization Conf., 2010.
[17] C. Hansen, J. Wieferich, F. Ritter, C. Rieder, and H.-O. Peitgen, Illustrative visualization of 3d planning models for augmented reality in liver surgery. Int. Journal of Computer Assisted Radiology and Surgery, 5: 133–141, 2010.
[18] P. Hildebrand, T. Leibecke, M. Kleemann, L. Mirow, M. Birth, H. Bruch, and C. Burk, Influence of operator experience in radiofrequency ablation of malignant liver tumours on treatment outcome. European Journal of Surgical Oncology, 32 (4): 430–434, May 2006.
[19] B. Kainz, M. Grabner, A. Bornik, S. Hauswiesner, J. Muehl, and D. Schmalstieg, Ray casting of multiple volumetric datasets with polyhedral boundaries on manycore gpus. ACM Trans. Graph., 28 (5):Article No. 152, 2009.
[20] T. Marin, M. N. Wernick, Y. Yang, and J. G. Brankov, Motion-compensated reconstruction of gated cardiac spect images using a de-formable mesh model. In IEEE Int. Conf. on biomedical imaging: from nano to macro, pages 520–523. IEEE Press, 2010.
[21] N. L. Max, Atmospheric illumination and shadows. In ACM SIGGRAPH Computer Graphics, pages 117–124. ACM, 1986.
[22] R. J. McDonald, L. A. Gray, H. J. Cloft, K. R. Thielen, and D. F. Kallmes, The Effect of Operator Variability and Experience in Vertebroplasty Outcomes. Radiology, 253 (2): 478–485, 2009.
[23] M. Meißner, J. Huang, D. Bartz, K. Mueller, and R. Crawfis, A practical evaluation of popular volume rendering algorithms. In IEEE Symp. on Volume visualization, pages 81–90. ACM, 2000.
[24] C. Mueller, J. M. Hodgson, M. Brutsche, H.-P. Bestehorn, S. Marsch, A. P. Perruchoud, H. Roskamm, and H. J. Buettner, Operator experience and long term outcome after percutaneous coronary intervention. Can J Cardiol, 19: 1047–51, 2003.
[25] N. V. Navkar, N. V. Tsekos, J. R. Stafford, J. S. Weinberg, and Z. Deng, Visualization and planning of neurosurgical interventions with straight access. In Proceedings of the First international conference on Information processing in computer-assisted interventions, IPCAI'10, pages 1–11. Springer, 2010.
[26] S. Nirenstein, E. Blake, and J. Gain, Exact from-region visibility culling. In Eurographics Workshop on Rendering, EGRW, pages 191–202. Eurographics Association, 2002. ACM ID: 581921.
[27] T. Nishita, Y. Miyawaki, and E. Nakamae, A shading model for atmospheric scattering considering luminous intensity distribution of light sources. In ACM SIGGRAPH Computer Graphics, volume 21, pages 303–310. ACM, Aug. 1987.
[28] C. Rieder, F. Ritter, M. Raspe, and H.-O. Peitgen, Interactive visualization of multimodal volume data for neurosurgical tumor treatment. Com-put. Graph. Forum, 27 (3): 1055–1062, 2008.
[29] W. Schroeder, K. Martin, and B. Lorensen, The Visualization Toolkit, Third Edition. Kitware Inc., 2007.
[30] C. Schumann, J. Bieberstein, C. Trumm, D. Schmidt, P. Bruners, M. Ni-ethammer, R. T. Hoffmann, A. H. Mahnken, P. L. Pereira, and H.-O. Peitgen, Fast automatic path proposal computation for hepatic needle placement. volume 7625 of Proceedings of the SPIE, pages 76251J–1 – 76251J–10, 2010.
[31] R. R. Shamir, I. Tamir, E. Dabool, L. Joskowicz, and Y. Shoshan, A method for planning safe trajectories in image-guided keyhole neuro-surgery. In Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III, MIC-CAI'10, pages 457–464. Springer, 2010.
[32] C. R. V. Tandy and A. C. Murray, The Isovist Method of Landscape Survey. Methods of Landscape Analysis, pages 9–10, Oct. 1967.
[33] M. Urschler, M. Werlberger, E. Scheurer, and H. Bischof, Robust optical flow based deformable registration of thoracic ct images. In MICCAI Workshop Medical Image Analysis in the Clinic: A Grand Challenge, LNCS. Springer, September 2010.
[34] M. Vaillant, C. Davatzikos, R. Taylor, and R. Bryan, A path-planning algorithm for image-guided neurosurgery. In Joint Conf. Computer Vision, Virtual Reality and Robotics in Medicine and Medical Robotics and Computer-Assisted Surgery, volume 1205 of LNCS, pages 467–476. Springer, 1997.
[35] L. Vancamberg, A. Sahbani, S. Muller, and G. Morel, Needle path planning for digital breast tomosynthesis biopsy. In Robotics and Automation (ICRA), pages 2062 –2067, May 2010.
[36] R. Viard, N. Betrouni, J. Rousseau, S. Mordon, O. Ernst, and S. Maouche, Needle positioning in interventional mri procedure: real time optical localisation and accordance with the roadmap. In Engineering in Medicine and Biology Society (EMBS), pages 2748–2751, Aug. 2007.
[37] C. Villard, L. Soler, and A. Gangi, Radiofrequency ablation of hepatic tumors: simulation, planning, and contribution of virtual reality and hap-tics. Comput. Methods Biomech Biomed. Engin., 8 (4): 215–227, Aug. 2005.
[38] I. Wolf, M. Vetter, I. Wegner, T. Böttger, M. Nolden, M. Schöbinger, M. Hastenteufel, T. Kunert, and H.-P. Meinzer, The medical imaging interaction toolkit. Medical Image Analysis, 9 (6): 594 – 604, 2005. ITK -Open science - combining open data and open source software: Medical image analysis with the Insight Toolkit.
[39] P. Wonka, M. Wimmer, K. Zhou, S. Maierhofer, G. Hesina, and A. Reshetov, Guided visibility sampling. In ACM Transactions on Graphics, volume 25, pages 494–502. ACM, July 2006.
18 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool