arXiv subjects

Wenzhi Bai

Publications and source records attributed to Wenzhi Bai.

3 recordsLinked to original sources

arXiv2026

SlicerRoboTMS: An Open-Source 3D Slicer Extension for Robot-Assisted Transcranial Magnetic Stimulation

Robot-assisted Transcranial Magnetic Stimulation (Robo-TMS) is an image-guided robotic intervention that enhances the accuracy and reproducibility of conventional Transcranial Magnetic Stimulation (TMS), a widely used non-invasive brain stimulation procedure in clinical treatment and neuroscience research. Despite its potential, the development of Robo-TMS remains challenging due to the need for multidisciplinary expertise spanning medical imaging, computer vision, and robotics. This paper presents SlicerRoboTMS, an open-source 3D Slicer extension that provides a unified interaction infrastructure for Robo-TMS research. By leveraging 3D Slicer's medical image computing and visualisation capabilities, the extension supports Magnetic Resonance Imaging (MRI)-based neuronavigation and interfaces with robotic systems through standardised communication protocols and configurable system descriptions. An example integration is presented to demonstrate how SlicerRoboTMS can be incorporated into a representative Robo-TMS workflow. Designed to support diverse hardware configurations and rapid prototyping, SlicerRoboTMS lowers the barrier to entry and facilitates reproducible and extensible research in Robo-TMS. The extension is available at https://github.com/OpenRoboTMS/SlicerRoboTMS.

cs.RO↗

arXiv2025

Robot-assisted Transcranial Magnetic Stimulation (Robo-TMS): A Review

Transcranial magnetic stimulation (TMS) is a non-invasive and safe brain stimulation procedure with growing applications in clinical treatments and neuroscience research. However, achieving precise stimulation over prolonged sessions poses significant challenges. By integrating advanced robotics with conventional TMS, robot-assisted TMS (Robo-TMS) has emerged as a promising solution to enhance efficacy and streamline procedures. Despite growing interest, a comprehensive review from an engineering perspective has been notably absent. This paper systematically examines four critical aspects of Robo-TMS: hardware and integration, calibration and registration, neuronavigation systems, and control systems. We review state-of-the-art technologies in each area, identify current limitations, and propose future research directions. Our findings suggest that broader clinical adoption of Robo-TMS is currently limited by unverified clinical applicability, high operational complexity, and substantial implementation costs. Emerging technologies, including marker-less tracking, non-rigid registration, learning-based electric field (E-field) modelling, individualised magnetic resonance imaging (MRI) generation, robot-assisted multi-locus TMS (Robo-mTMS), and automated calibration and registration, present promising pathways to address these challenges.

cs.RO↗

arXiv2025

PL-VIWO: A Lightweight and Robust Point-Line Monocular Visual Inertial Wheel Odometry

This paper presents a novel tightly coupled Filter-based monocular visual-inertial-wheel odometry (VIWO) system for ground robots, designed to deliver accurate and robust localization in long-term complex outdoor navigation scenarios. As an external sensor, the camera enhances localization performance by introducing visual constraints. However, obtaining a sufficient number of effective visual features is often challenging, particularly in dynamic or low-texture environments. To address this issue, we incorporate the line features for additional geometric constraints. Unlike traditional approaches that treat point and line features independently, our method exploits the geometric relationships between points and lines in 2D images, enabling fast and robust line matching and triangulation. Additionally, we introduce Motion Consistency Check (MCC) to filter out potential dynamic points, ensuring the effectiveness of point feature updates. The proposed system was evaluated on publicly available datasets and benchmarked against state-of-the-art methods. Experimental results demonstrate superior performance in terms of accuracy, robustness, and efficiency. The source code is publicly available at: https://github.com/Happy-ZZX/PL-VIWO

cs.RO↗