A computational model for real-time calculation of electric field due to transcranial magnetic stimulation in clinics

The aim of this paper is to propose an approach for an accurate and fast (real-time) computation of the electric field induced inside the whole brain volume during a transcranial magnetic stimulation (TMS) procedure. The numerical solution implements the admittance method for a discretized realistic brain model derived from Magnetic Resonance Imaging (MRI). Results are in a good agreement with those obtained using commercial codes and require much less computational time. An integration of the developed codewith neuronavigation toolswill permit real-time evaluation of the stimulated brain regions during the TMSdelivery, thus improving the efficacy of clinical applications

Analysis of the 'Endoworm' prototype's ability to grip the bowel in in vitro and ex vivo models

[EN] Access to the small bowel by means of an enteroscope is difficult, even using current devices such as single-balloon or double-balloon enteroscopes. Exploration time and patient discomfort are the main drawbacks. The prototype 'Endoworm' analysed in this paper is based on a pneumatic translation system that, gripping the bowel, enables the endoscope to move forward while the bowel slides back over its most proximal part. The grip capacity is related to the pressure inside the balloon, which depends on the insufflate volume of air. Different materials were used as in vitro and ex vivo models: rigid polymethyl methacrylate, flexible silicone, polyester urethane and ex vivo pig small bowel. On measuring the pressure-volume relationship, we found that it depended on the elastic properties of the lumen and that the frictional force depended on the air pressure inside the balloons and the lumen's elastic properties. In the presence of a lubricant, the grip on the simulated intestinal lumens was drastically reduced, as was the influence of the lumen's properties. This paper focuses on the Endoworm's ability to grip the bowel, which is crucial to achieving effective endoscope forward advance and bowel foldingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was funded by the Spanish Ministry of Economy and Competitiveness through Project (PI18/01365) and by the UPV/IIS LA Fe through the (Endoworm 3.0) Project. CIBER-BBN is an initiative funded by the VI National R&D&I Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions and financed by the Instituto de Salud Carlos III with the assistance of the European Regional Development FundTobella, J.; Pons-Beltrán, V.; Santonja, A.; Sánchez-Diaz, C.; Campillo Fernandez, AJ.; Vidaurre, A. (2020). Analysis of the 'Endoworm' prototype's ability to grip the bowel in in vitro and ex vivo models. Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine. 234(5):1-10. https://doi.org/10.1177/09544119209014141102345Iddan, G., Meron, G., Glukhovsky, A., & Swain, P. (2000). Wireless capsule endoscopy. Nature, 405(6785), 417-417. doi:10.1038/35013140Yamamoto, H., Sekine, Y., Sato, Y., Higashizawa, T., Miyata, T., Iino, S., … Sugano, K. (2001). Total enteroscopy with a nonsurgical steerable double-balloon method. Gastrointestinal Endoscopy, 53(2), 216-220. doi:10.1067/mge.2001.112181Arnott, I. D. R., & Lo, S. K. (2004). REVIEW: The Clinical Utility of Wireless Capsule Endoscopy. Digestive Diseases and Sciences, 49(6), 893-901. doi:10.1023/b:ddas.0000034545.58486.e6Hosoe, N., Takabayashi, K., Ogata, H., & Kanai, T. (2019). Capsule endoscopy for small‐intestinal disorders: Current status. Digestive Endoscopy, 31(5), 498-507. doi:10.1111/den.13346Fukumoto, A., Tanaka, S., Shishido, T., Takemura, Y., Oka, S., & Chayama, K. (2009). Comparison of detectability of small-bowel lesions between capsule endoscopy and double-balloon endoscopy for patients with suspected small-bowel disease. Gastrointestinal Endoscopy, 69(4), 857-865. doi:10.1016/j.gie.2008.06.007Akerman, P. A., Agrawal, D., Chen, W., Cantero, D., Avila, J., & Pangtay, J. (2009). Spiral enteroscopy: a novel method of enteroscopy by using the Endo-Ease Discovery SB overtube and a pediatric colonoscope. Gastrointestinal Endoscopy, 69(2), 327-332. doi:10.1016/j.gie.2008.07.042Moreels, T. G. (2017). Update in enteroscopy: New devices and new indications. Digestive Endoscopy, 30(2), 174-181. doi:10.1111/den.12920Pasha, S. F. (2012). Diagnostic yield of deep enteroscopy techniques for small-bowel bleeding and tumors. Techniques in Gastrointestinal Endoscopy, 14(2), 100-105. doi:10.1016/j.tgie.2012.02.001Lenz, P., & Domagk, D. (2012). Double- vs. single-balloon vs. spiral enteroscopy. Best Practice & Research Clinical Gastroenterology, 26(3), 303-313. doi:10.1016/j.bpg.2012.01.021Baniya, R., Upadhaya, S., Subedi, S. C., Khan, J., Sharma, P., Mohammed, T. S., … Jamil, L. H. (2017). Balloon enteroscopy versus spiral enteroscopy for small-bowel disorders: a systematic review and meta-analysis. Gastrointestinal Endoscopy, 86(6), 997-1005. doi:10.1016/j.gie.2017.06.015Menciassi, A., & Dario, P. (2003). Bio-inspired solutions for locomotion in the gastrointestinal tract: background and perspectives. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 361(1811), 2287-2298. doi:10.1098/rsta.2003.1255Zarrouk, D., Sharf, I., & Shoham, M. (2011). Analysis of Wormlike Robotic Locomotion on Compliant Surfaces. IEEE Transactions on Biomedical Engineering, 58(2), 301-309. doi:10.1109/tbme.2010.2066274Poon, C. C. Y., Leung, B., Chan, C. K. W., Lau, J. Y. W., & Chiu, P. W. Y. (2015). Design of wormlike automated robotic endoscope: dynamic interaction between endoscopic balloon and surrounding tissues. Surgical Endoscopy, 30(2), 772-778. doi:10.1007/s00464-015-4224-8Kassim, I., Phee, L., Ng, W. S., Feng Gong, Dario, P., & Mosse, C. A. (2006). Locomotion techniques for robotic colonoscopy. IEEE Engineering in Medicine and Biology Magazine, 25(3), 49-56. doi:10.1109/memb.2006.1636351Kim, Y.-T., & Kim, D.-E. (2010). Novel Propelling Mechanisms Based on Frictional Interaction for Endoscope Robot. Tribology Transactions, 53(2), 203-211. doi:10.1080/10402000903125337Massalou, D., Masson, C., Foti, P., Afquir, S., Baqué, P., Berdah, S.-V., & Bège, T. (2016). Dynamic biomechanical characterization of colon tissue according to anatomical factors. Journal of Biomechanics, 49(16), 3861-3867. doi:10.1016/j.jbiomech.2016.10.023Egorov, V. I., Schastlivtsev, I. V., Prut, E. V., Baranov, A. O., & Turusov, R. A. (2002). Mechanical properties of the human gastrointestinal tract. Journal of Biomechanics, 35(10), 1417-1425. doi:10.1016/s0021-9290(02)00084-2Hoeg, H. D., Slatkin, A. B., Burdick, J. W., & Grundfest, W. S. (s. f.). Biomechanical modeling of the small intestine as required for the design and operation of a robotic endoscope. Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065). doi:10.1109/robot.2000.844825Terry, B. S., Passernig, A. C., Hill, M. L., Schoen, J. A., & Rentschler, M. E. (2012). Small intestine mucosal adhesivity to in vivo capsule robot materials. Journal of the Mechanical Behavior of Biomedical Materials, 15, 24-32. doi:10.1016/j.jmbbm.2012.06.018Kim, J.-S., Sung, I.-H., Kim, Y.-T., Kwon, E.-Y., Kim, D.-E., & Jang, Y. H. (2006). Experimental investigation of frictional and viscoelastic properties of intestine for microendoscope application. Tribology Letters, 22(2), 143-149. doi:10.1007/s11249-006-9073-0Lyle, A. B., Luftig, J. T., & Rentschler, M. E. (2013). A tribological investigation of the small bowel lumen surface. Tribology International, 62, 171-176. doi:10.1016/j.triboint.2012.11.018De Simone, A., & Luongo, A. (2013). Nonlinear viscoelastic analysis of a cylindrical balloon squeezed between two rigid moving plates. International Journal of Solids and Structures, 50(14-15), 2213-2223. doi:10.1016/j.ijsolstr.2013.03.028Sliker, L. J., Ciuti, G., Rentschler, M. E., & Menciassi, A. (2016). Frictional resistance model for tissue-capsule endoscope sliding contact in the gastrointestinal tract. Tribology International, 102, 472-484. doi:10.1016/j.triboint.2016.06.003Zhang, C., Liu, H., & Li, H. (2014). Experimental investigation of intestinal frictional resistance in the starting process of the capsule robot. Tribology International, 70, 11-17. doi:10.1016/j.triboint.2013.09.019Zhang, C., Liu, H., & Li, H. (2013). Modeling of Frictional Resistance of a Capsule Robot Moving in the Intestine at a Constant Velocity. Tribology Letters, 53(1), 71-78. doi:10.1007/s11249-013-0244-5Zhang, C., Liu, H., Tan, R., & Li, H. (2012). Modeling of Velocity-dependent Frictional Resistance of a Capsule Robot Inside an Intestine. Tribology Letters, 47(2), 295-301. doi:10.1007/s11249-012-9980-1Woo, S. H., Kim, T. W., Mohy-Ud-Din, Z., Park, I. Y., & Cho, J.-H. (2011). Small intestinal model for electrically propelled capsule endoscopy. BioMedical Engineering OnLine, 10(1), 108. doi:10.1186/1475-925x-10-108Sliker, L. J., & Rentschler, M. E. (2012). The Design and Characterization of a Testing Platform for Quantitative Evaluation of Tread Performance on Multiple Biological Substrates. IEEE Transactions on Biomedical Engineering, 59(9), 2524-2530. doi:10.1109/tbme.2012.2205688Sánchez-Diaz, C., Senent-Cardona, E., Pons-Beltran, V., Santonja-Gimeno, A., & Vidaurre, A. (2018). Endoworm: A new semi-autonomous enteroscopy device. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 232(11), 1137-1143. doi:10.1177/0954411918806330Persson, B. N. J., & Spencer, N. D. (1999). Sliding Friction: Physical Principles and Applications. Physics Today, 52(1), 66-68. doi:10.1063/1.882557Gerson, L. B., Flodin, J. T., & Miyabayashi, K. (2008). Balloon-assisted enteroscopy: technology and troubleshooting. Gastrointestinal Endoscopy, 68(6), 1158-1167. doi:10.1016/j.gie.2008.08.012Glozman, D., Hassidov, N., Senesh, M., & Shoham, M. (2010). A Self-Propelled Inflatable Earthworm-Like Endoscope Actuated by Single Supply Line. IEEE Transactions on Biomedical Engineering, 57(6), 1264-1272. doi:10.1109/tbme.2010.2040617Baek, N.-K., Sung, I.-H., & Kim, D.-E. (2004). Frictional resistance characteristics of a capsule inside the intestine for microendoscope design. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 218(3), 193-201. doi:10.1243/095441104323118914Kwon, J., Cheung, E., Park, S., & Sitti, M. (2006). Friction enhancement via micro-patterned wet elastomer adhesives on small intestinal surfaces. Biomedical Materials, 1(4), 216-220. doi:10.1088/1748-6041/1/4/007Kim, B., Lee, S., Park, J. H., & Park, J.-O. (2005). Design and Fabrication of a Locomotive Mechanism for Capsule-Type Endoscopes Using Shape Memory Alloys (SMAs). IEEE/ASME Transactions on Mechatronics, 10(1), 77-86. doi:10.1109/tmech.2004.842222Terry, B. S., Lyle, A. B., Schoen, J. A., & Rentschler, M. E. (2011). Preliminary Mechanical Characterization of the Small Bowel for In Vivo Robotic Mobility. Journal of Biomechanical Engineering, 133(9). doi:10.1115/1.400516

Paresthesia thresholds in spinal cord stimulation: a comparison of theoretical results with clinical data

The potential distributions produced in the spinal cord and surrounding tissues by dorsal epidural stimulation at the midcervical, midthoracic, and low thoracic levels were calculated with the use of a volume conductor model. Stimulus thresholds of myelinated dorsal column fibers and dorsal root fibers were calculated at each level in models in which the thickness of the dorsal cerebrospinal fluid (CSF) layer was varied. Calculated stimulus thresholds were compared with paresthesia thresholds obtained from measurements at the corresponding spinal levels in patients. The influences of the CSF layer thickness, the contact separation in bipolar stimulation and the laterality of the electrodes on the calculated thresholds were in general agreement with the clinical dat

Model estimation of cerebral hemodynamics between blood flow and volume changes: a data-based modeling approach

It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV