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This paper illustrates the concept of an algorithm to register specified markers on the neuroArm surgical manipulators, an image-guided MR-compatible tele-operated robot for microsurgery and stereotaxy. Two range-finding algorithms, namely time-of-flight and phase-shift, are evaluated for registration and supervisory control. The time-of-flight approach is implemented in a semi-field experiment to determine the precise position of a tiny retro-reflective moving object. The moving object simulates a surgical tool tip. The tool is a target that would be connected to the neuroArm end-effector during surgery inside the magnet bore of the MR imaging system. In order to apply flight approach, a 905-nm pulsed laser diode and an avalanche photodiode are utilized as the transmitter and receiver, respectively. For the experiment, a high frequency time to digital converter was designed using a field-programmable gate arrays. In the phase-shift approach, a continuous green laser beam with a wavelength of 530 nm was used as the transmitter. Results showed that a positioning error of 0.1 mm occurred when the scanner-target point distance was set in the range of 2.5 to 3 meters. The effectiveness of this non-contact approach exhibited that the method could be employed as an alternative for conventional mechanical registration arm. Furthermore, the approach is not limited by physical contact and extension of joint angles., {"references":["Y. Maddahi, K. Zareinia, L. S. Gan, S. Lama, G. R. Sutherland, and N. Sepehri, \"Positional and force characteristics of neuroArm robotic manipulators: A pilot study,\" Int. Conf. of Control, Dynamic Systems, and Robotics, Ottawa, Canada, 2015.","G. R. Sutherland, I. Latour, and A. D. Greer, \"Integrating an image guided robot with intaroperative MRI: A review of design and construction,\" IEEE Eng. Med. Biol., vol. 27, pp. 59–65, 2008.","Y. Maddahi, L. S. Gan, K. Zareinia, S. Lama, N. Sepehri, and G. R. Sutherland, \"Quantifying workspace and forces of surgical tools during robot-assisted neurosurgery,\" Int. J. Med. Robotics Comput. Assist. Surg., vol. 12, no. 3, pp. 528–37, 2016.","A. D. Greer, P. Newhook, and G. R. Sutherland, \"Human-machine interface for Robotic Surgery and Stereotaxy,\" IEEE/ASME Trans. on MRI Compatible Mechatronic Systems, vol. 13, pp. 355–361, 2008.","Y. Maddahi, K. Zareinia K, L. S. Gan, S. Lama, G. R. Sutherland, N. Sepehri, \"Positional and force characteristics of neuroArm robotic manipulators: a pilot study.,\" in Proc. Intern. Conf. of Control, Dynamic Systems and Robotics, Ottawa, 2015.","M. C. Amann, T. Bosch, M. Lescure, R. Myllyla, M. Rioux, \"Laser ranging: a critical review of usual techniques for distance measurement\", Opt. Eng., vol. 40, no. 1, pp. 10–19, 2001.","Y. Bae, \"An Improved Measurement Method for the Strength of Radiation of Reflective Beam in an Industrial Optical Sensor Based on Laser Displacement Meter\", Sensors, vol. 16, no. 5, pp. 752, 2016.","S. Mohammad Nejad and S. Olyaee, \"Comparison of TOF, FMCW and phase-shift laser range finding methods by simulation and measurement,\" J. Tech. Educ., vol. 1, no. 1, pp. 11–18, 2006.","S. Mohammad Nejad and S. Olyaee, \"Low-noise high-accuracy TOF laser range finder,\" Am. J. Appl. Sci., vol. 5-7, pp. 755–762, 2008.\n[10]\tS. Mohammad Nejad and S. Olyaee, \"Unified pulsed laser range finder and velocimeter using ultra-fast time-to-digital converter,\" Iranian J. Elect. Electron. Eng., vol. 5, no. 2, pp. 112–121, 2009.\n[11]\tPhoton Detection Datasheet, C30659 Series-Rev.1.1, pp. 1–10, 2013. www.excelitas.com.\n[12]\tJ. Song, Q. An, and S. Liu, \"A high-resolution time-to-digital converter implemented in field-programmable-gate-arrays,\" IEEE Trans. Nuclear Science, vol. 53, no. 1, pp. 236–241, 2006.\n[13]\tM. C. Lin, G. R. Tsai, C. Y. Liu, and S. S. Chu, \"FPGA-based high area efficient time-to-digital IP design,\" in TENCON IEEE Region 10 Conf., Hong Kong, 2006, pp. 1–4.\n[14]\tM. P. Mattada, and H. Guhilot, \"Area efficient vernier time to digital converter (TDC) with improved resolution using identical ring oscillators on FPGA,\" in IEEE Int. Conf. Smart Struct. Syst. (ICSSS), Chennai, 2013, pp. 125–130.\n[15]\tJ. Wang, S. Liu, Q. Shen, H. Li, and Q. An, \"A fully fledged TDC implemented in field¬programmable-gate-arrays,\" IEEE Trans. Nuclear Science, vol. 57, no. 2, pp. 446–450, April 2010.\n[16]\tJ. Kostamovaara, S. Kurtti, and J. P. Jansson, \"A receiver – TDC chip set for accurate pulsed Time-of-flight laser ranging,\" CDNLive EMEA Conf. Proc., Munich, 2012.\n[17]\tF. Zhong, L. Wan, and Y. Yue, \"High-precision interval measuring chip TDC-GP2 in accurate distance measuring,\" Comp. Indust. Control, vol. 4, pp. 69–72, 2007.\n[18]\tA. Aloisio, P. Branchini, R. Cicalese, R. Giordano, V. Izzo, and S. Loffredo, \"FPGA implementation of a high-resolution time-to-digital converter,\" in IEEE Nuclear Science Symposium Conference Record, 2007, pp. 504–507.\n[19]\tJ. Kalisz, R. Szplet, J. Pasierbinski, and A. Poniecki, \"Field programmable gate array based time-to-digital converter with 200-ps resolution\", IEEE Trans. Instrum. Meas., vol. 46, no.1, pp 51–55, Feb. 1997.\n[20]\tP. Dudek, S. Szezepanski, and J. Hatfield, \"A high resolution CMOS time-to-digital converter utilizing a Vernier delay loop,\" IEEE Trans. Solid State Circuits, vol. 35, no. 2, pp. 240–247, 2000. \n[21]\tS. Poujouly, B. Journet and D. Placko, \"Digital laser range finder: phase-shift estimation by undersampling technique,\" Indust. Electron. Society, The 25th Annu. Conf. IEEE, San Jose, CA, vol. 3, 1999, pp. 1312–1317. \n[22]\tT. Y. Cossetto, K. Zareinia and G. R. Sutherland, \"Neurosurgery,\" in Surgical Robotics, vol. 3, P. Gomes, Ed. Cambridge: Wood Head Publishing Ltd., pp. 59-77, 2012.\n[23]\tG. R. Sutherland, S. Wolfsberger, S. Lama, K. Zareinia. \"The Evolution of neuroArm,\" Neurosurgery, vol. 72, pp. A27–A32, 2013.\n[24]\tA. Mert, L. S. Gan, E. Knosp, G. R. Sutherland, S. Wolfsberger, \"Advanced Cranial Navigation,\" Neurosurgery, vol. 72, pp. A43-A53, 2013."]}

BACKGROUND Standard MRI has been used for high-grade gliomas detection, albeit with limited success as it does not provide sufficient specificity and sensitivity to detect complex tumor structure. Therefore targeted contrast agents based on iron oxide, that shorten mostly T2 relaxation time, have been recently applied. However pulse sequences for molecular imaging in animal models of gliomas have not been yet fully studied. The aim of this study was therefore to compare contrast-to-noise ratio (CNR) and explain its origin using spin-echo (SE), gradient echo (GE), GE with flow compensation (GEFC) as well as susceptibility weighted imaging (SWI) in T2 and T2* contrast-enhanced molecular MRI of glioma. METHODS A mouse model was used. U87MGdEGFRvIII cells (U87MG), derived from a human tumor, were injected intracerebrally. A 9.4 T MRI system was used and MR imaging was performed on the 10 day after the inoculation of the tumor. The CNR was measured prior, 20 min, 2 hrs and 24 hrs post intravenous tail administration of glioma targeted paramagnetic nanoparticles (NPs) using SE, SWI, GE and GEFC pulse sequences. RESULTS The results showed significant differences in CNR among all pulse sequences prior injection. GEFC provided higher CNR post contrast agent injection when compared to GE and SE. Post injection CNR was the highest with SWI and significantly different from any other pulse sequence. CONCLUSIONS Molecular MR imaging using targeted contrast agents can enhance the detection of glioma cells at 9.4 T if the optimal pulse sequence is used. Hence, the use of flow compensated pulse sequences, beside SWI, should to be considered in the molecular imaging studies.

The 15th Annual Medicine Meets Virtual Reality Conference, Long Beach, California, February 06-09, 2007