Robotized system for navigated transcranial magnetic stimulation

• Ano de defesa: 2022
• Autor(a) principal: Matsuda, Renan Hiroshi
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Biomedical instrumentation; Collaborative robots; Computer vision; Estimulação magnética transcraniana; Instrumentação biomédica; Neuronavegação; Neuronavigation; Robôs colaborativos; Transcranial magnetic stimulation; Visão computacional
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/59/59135/tde-13122022-171059/

Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique widely used to investigate human brain functions. Neuronavigation systems provide image guidance to TMS targeting procedures, known as navigated TMS (nTMS). nTMS uses a tracking device to monitor the patient\'s head movement. The tracking head marker must remain static during the entire treatment or experimental protocol. Small variations in the head marker or in the TMS coil positioning may cause considerable unintended changes in the physiological responses, compromising the TMS reliability and reproducibility. Moreover, collaborative robots have been used to overcome these TMS targeting limitations. However, robotic TMS coil positioning is not common due to poor portability, high cost, and closed-source software development platforms. Therefore, the aim of this thesis was: 1) to develop an open-source robotized system for nTMS; 2) to develop a markerless head tracker for navigated TMS. In the first part, we developed and characterized a closed-loop control system combining the electronic and physical robotic positioning of the TMS transducer. Our new open-source platform for robotized TMS coil positioning is an important step to increase the accuracy and reliability of TMS procedures, facilitating the development of new tools and methods for brain investigation, such as the automation of motor mappings. Second, we developed and characterized MarLe; a novel strategy of a markerless head tracker for navigated TMS. MarLe uses computer vision techniques combined with a low-cost camera to estimate the head pose for neuronavigation systems. MarLe improves the neuronavigation reliability, simplifying and reducing the time of brain intervention protocols, such as with nTMS.