IPCAI 2011: Information Processing in Computer-Assisted Interventions pp 122-132 | Cite as
Intra-operative “Pick-Up” Ultrasound for Robot Assisted Surgery with Vessel Extraction and Registration: A Feasibility Study
Abstract
We propose the use of a “pick-up” ultrasound transducer for robot-assisted minimally invasive surgeries. Unlike prior approaches, the ultrasound transducer is inserted before the procedure and remains in the abdominal cavity throughout. We present a new design for such an intra-abdominal ultrasound transducer with a handle that can be grasped in a repeatable manner using a da Vinci Pro-Grasp tool. The main application is mapping the vasculature, which is segmented from Doppler and B-mode images using a Kalman-filtering approach. Our goal is employ the vasculature to register pre-operative CT to intra-operative camera images. To demonstrate the feasibility of the approach, we use an ultrasound flow phantom to register a CT surface model to extracted ultrasound vessel center points using an iterative closest point method. The transducer was tracked with electromagnetic sensors and a target registration error of 3.2 mm was calculated. The initial application will be nephrectomy where vessel localization is paramount.
Keywords
Robotic surgery kidney ultrasound CT image registrationPreview
Unable to display preview. Download preview PDF.
References
- 1.Barbot, D.J.: Improved staging of liver tumors using laparscopic intraoperative ultrasound. Journal of Surgical Oncology 64, 63–67 (1997)CrossRefGoogle Scholar
- 2.Besl, P., McKay, N.: A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 239–256 (1992)Google Scholar
- 3.Catheline, J.M.: A comparison of laparoscopic ultrasound versus cholangiography in the evaluation of the biliary tree during laparoscopic cholecystectomy. European Journal of Ultrasound 10(1), 1–9 (1999)CrossRefGoogle Scholar
- 4.Dutkiewicz, P., Kietczewski, M., Kowalski, M., Wroblewski, W.: Experimental verification of visual tracking of surgical tools. In: Robot Motion and Control, pp. 237–242. IEEE, Los Alamitos (2005)Google Scholar
- 5.Guerrero, J., Salcudean, S., McEwen, J., Masri, B., Nicolaou, S.: Real-time vessel segmentation and tracking for ultrasound imaging applications. IEEE Transactions on Medical Imaging 26(8), 1079–1090 (2007)CrossRefGoogle Scholar
- 6.Hughes, S., D’Arcy, T., Maxwell, D., Chiu, W., Milner, A., Saunders, J., Sheppard, R.: Volume estimation from multiplanar 2D ultrasound images using a remote electromagnetic position and orientation sensor. Ultrasound in Medicine & Biology 22(5), 561–572 (1996)CrossRefGoogle Scholar
- 7.Jomier, J., Aylward, S.R.: Rigid and deformable vasculature-to-image registration: A hierarchical approach. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 829–836. Springer, Heidelberg (2004)CrossRefGoogle Scholar
- 8.Lange, T., Eulenstein, S., Huenerbein, M., Lamecker, H., Schlag, P.: Augmenting intraoperative 3D ultrasound with preoperative models for navigation in liver surgery. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 534–541. Springer, Heidelberg (2004)CrossRefGoogle Scholar
- 9.Mercier, L., Langø, T., Lindseth, F., Collins, L.: A review of calibration techniques for freehand 3-D ultrasound systems. Ultrasound in Medicine & Biology 31(2), 143–165 (2005)CrossRefGoogle Scholar
- 10.Nakajima, S., Atsumi, H., Kikinis, R., Moriarty, T., Metcalf, D., Jolesz, F., Black, P.: Use of cortical surface vessel registration for image-guided neurosurgery. Neurosurgery 40(6), 1201 (1997)CrossRefGoogle Scholar
- 11.Penney, G., Blackall, J., Hamady, M., Sabharwal, T., Adam, A., Hawkes, D.: Registration of freehand 3D ultrasound and magnetic resonance liver images. Medical Image Analysis 8(1), 81–91 (2004)CrossRefGoogle Scholar
- 12.Polascik, T., Meng, M., Epstein, J., Marshall, F.: Intraoperative sonography for the evaluation and management of renal tumors: Experience with 100 patients. The Journal of Urology 154(5), 1676–1680 (1995)CrossRefGoogle Scholar
- 13.Reinertsen, I., Descoteaux, M., Siddiqi, K., Collins, D.: Validation of vessel-based registration for correction of brain shift. Medical Image Analysis 11(4), 374–388 (2007)CrossRefGoogle Scholar
- 14.Schneider, C., Dachs, G., Hasser, C., Choti, M., DiMaio, S., Taylor, R.: Robot-assisted laparoscopic ultrasound. In: Information Processing in Computer-Assisted Interventions, pp. 67–80 (2010)Google Scholar
- 15.Su, L., Vagvolgyi, B., Agarwal, R., Reiley, C., Taylor, R., Hager, G.: Augmented reality during robot-assisted laparoscopic partial nephrectomy: Toward real-time 3D-CT to stereoscopic video registration. Urology 73(4), 896–900 (2009)CrossRefGoogle Scholar
- 16.Treece, G., Prager, R., Gee, A.: Regularised marching tetrahedra: improved iso-surface extraction. Computers & Graphics 23(4), 583–598 (1999)CrossRefGoogle Scholar
- 17.Yaniv, Z., Wilson, E., Lindisch, D., Cleary, K.: Electromagnetic tracking in the clinical environment. Medical Physics 36, 876 (2009)CrossRefGoogle Scholar
- 18.Yip, M.C., Adebar, T.K., Rohling, R.N., Salcudean, S.E., Nguan, C.Y.: 3D ultrasound to stereoscopic camera registration through an air-tissue boundary. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 626–634. Springer, Heidelberg (2010)CrossRefGoogle Scholar