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MRI-guided robot intervention—current state-of-the-art and new challenges

Med-X 2023年第1期1卷 141-181页

作者： Shaoping Huang Chuqian Lou Ying Zhou Zhao He Xuejun Jin Yuan Feng Anzhu Gao Guang-Zhong Yang Institute of Medical Robotics and School of Biomedical Engineering Shanghai Jiao Tong UniversityShanghai 200240China National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy(NERC-AMRT) Shanghai Jiao Tong UniversityShanghai 200240China Materials and Technology Center of Robotics EmpaDübendorf 8600Switzerland State Key Lab of Metal Matrix Composites and School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Institute of Medical Robotics and Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China The Key Laboratory of System Control and Information Processing Ministry of EducationShanghai 200240China

Magnetic Resonance Imaging(MRI)is now a widely used modality for providing multimodal,high-quality soft tissue contrast images with good spatiotemporal resolution but without subjecting patients to ionizing *** addition to its diagnostic potential,its future theranostic value lies in its ability to provide MRI-guided robot intervention with combined structural and functional mapping,as well as integrated instrument localization,target recognition,and in situ,in vivo monitoring of the therapeutic *** of current applications include neurosurgery,breast biopsy,cardiovascular intervention,prostate biopsy and *** applications in targeted drug delivery and MRI-guided chemoembolization are also being *** promising progress has been made in recent years,there are still significant basic science research and engineering *** paper provides a comprehensive review of the current state-of-the-art in MRI-guided robot intervention and allied technologies in actuation,sensing,new materials,interventional instruments,and interactive/real-time *** future research directions and new clinical developments are also discussed.

关键词： MRI-guided robot intervention Actuation Sensing Materials Interventional instruments Interactive/realtime MRI

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Virtual Physical Coupling of Two Lower-Limb Exoskeletons

arXiv

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arXiv 2023年

作者： Küçüktabak, Emek Barış Wen, Yue Short, Matthew Demirbaş, Efe Lynch, Kevin Pons, Jose Legs and Walking Lab Shirley Ryan AbilityLab ChicagoIL United States Center for Robotics and Biosystems of Northwestern University EvanstonIL United States Department of Biomedical Engineering Northwestern University EvanstonIL United States

Physical interaction between individuals plays an important role in human motor learning and performance during shared tasks. Using robotic devices, researchers have studied the effects of dyadic haptic interaction mostly focusing on the upper-limb. Developing infrastructure that enables physical interactions between multiple individuals’ lower limbs can extend the previous work and facilitate investigation of new dyadic lower-limb rehabilitation schemes. We designed a system to render haptic interactions between two users while they walk in multi-joint lower-limb exoskeletons. Specifically, we developed an infrastructure where desired interaction torques are commanded to the individual lower-limb exoskeletons based on the users’ kinematics and the properties of the virtual coupling. In this pilot study, we demonstrated the capacity of the platform to render different haptic properties (e.g., soft and hard), different haptic connection types (e.g., bidirectional and unidirectional), and connections expressed in joint space and in task space. With haptic connection, dyads generated synchronized movement, and the difference between joint angles decreased as the virtual stiffness increased. This is the first study where multi-joint dyadic haptic interactions are created between lower-limb exoskeletons. This platform will be used to investigate effects of haptic interaction on motor learning and task performance during walking, a complex and meaningful task for gait rehabilitation. © 2023, CC BY.

关键词： Exoskeleton (robotics)

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Towards Closed-loop Control of the Modified COAST Guidewire under Fluoroscopic Imaging for Endotracheal and Endovascular Interventions

Towards Closed-loop Control of the Modified COAST Guidewire ...

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International Symposium on Medical robotics (ISMR)

作者： Sharan R. Ravigopal Kirsten M. Williams Jaydev P. Desai Department of Biomedical Engineering Medical Robotics and Automation (RoboMed) Laboratory Georgia Institute of Technology Atlanta GA USA Aflac Cancer and Blood Disorders Center Children's Healthcare of Atlanta Emory University Atlanta GA USA

Lung infections in children remain challenging to diagnose despite the use of bronchoalveolar lavage (BAL), in which sterile saline flushes a lung segment and a sample is tested for pathogens. The yield of BAL is low as less fluid can be inserted since the size of the children's lungs is small, and the site of infection is often far distal to the position of the bronchoscope. In addition, it poses additional risks or complications as the saline is also absorbed into the lung parenchyma. A smaller, robotic device could traverse further along the bronchial tree and guide to the site of infection, increasing the successful identification of the pathogen to decrease complications of the BAL procedure. Likewise, treatment of endovascular disease also often involves catheterization, which requires a highly experienced interventional cardiologist to place guidewires at the target location, typically using fluoroscopic imaging to localize the guidewire during the procedure. However, inaccurate placement of these robotic guidewires can lead to unsuccessful procedural outcomes. Robotic manipulation of guidewires could provide better control of the device. We propose an automated approach for closed-loop control of the modified COaxially Aligned STeerable (COAST) guidewire under fluoroscopic imaging in phantoms. Deep learning architectures are used to determine the workspace, position, and orientation of the guidewire in two dimensions. This information is used for path planning and traversal in a phantom. From the centerline of the guidewire, the parameters for the modified COAST guidewire are determined to generate estimated joint actuations, which are compared to the actual joint actuations to autonomously correct for the error in the guidewire tip position for closed-loop control.

关键词： Pathogens Pediatrics Navigation Lung Imaging Phantoms Imaging phantoms

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View-Robust Neural Networks for Unseen Human Action Recognition in Videos

View-Robust Neural Networks for Unseen Human Action Recognit...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Yu, Jiahui Ma, Tianyu Ju, Zhaojie Chen, Hang Xu, Yingke Zhejiang University Department of Biomedical Engineering Hangzhou China Binjiang Institute of Zhejiang University Innovation Center for Smart Medical Technologies & Devices Hangzhou China Shenyang Ligong University School of Automation and Electrical Engineering Shenyang China Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Portsmouth School of Computing Portsmouth United Kingdom

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Data-driven deep learning achieved excellent performance for human action recognition. However, unseen action recognition remains a challenge for most existing neural networks. Because the action categories, collection perspectives, and scenarios considered during data collection are limited. Compared with class-unseen action recognition, view-unseen action recognition in videos is under-explored. This paper proposes view-robust neural networks (VR-Net) to recognize unseen actions in videos. The VR-Net consists of a 3D pose estimation module, skeleton adaptive transformation neural networks, and classification modules. We first extract 3D skeleton models from the video sequence based on existing pose estimation methods. Next, we propose a skeleton representation transformation scheme and achieve it based on Convolutional Neural Networks (VR-CNN) and Graph Neural Networks (VR-GCN), resulting in the optimal skeleton representations. Futhermore, we explore an associate optimization scheme and a fused output method. We evaluate the proposed neural networks on three challenging benchmarks, i.e., NTU RGB-D dataset (NTU), Kinetics-400 dataset, and Human3.6M dataset (H3.6M). The experimental results show that view robust neural networks achieve the top performance compared to state-of-the-art RGB-based and skeleton-based works, such as 93.6% on the NTU (CV) and 94.6% on the Kinetics-400 dataset (Top-5). The proposed neural networks significantly improve the recognition performance for unseen action recognition, such as 86.8% on the H3.6M (View 2). © 2022 IEEE.

关键词： Convolutional neural networks

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Bringing sensation to prosthetic hands-chronic assessment of implanted thin-film electrodes in humans

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npj Flexible Electronics 2023年第1期7卷 32-45页

作者： PaulČvančara Giacomo Valle Matthias Müller Inga Bartels Thomas Guiho Arthur Hiairrassary Francesco Petrini Stanisa Raspopovic Ivo Strauss Giuseppe Granata Eduardo Fernandez Paolo M.Rossini Massimo Barbaro Ken Yoshida Winnie Jensen Jean-Louis Divoux David Guiraud Silvestro Micera Thomas Stieglitz Laboratory for Biomedical Microtechnology Department of Microsystems Engineering-IMTEKAlbert-Ludwig-University FreiburgFreiburgGermany Center for Neuroprosthetics and Institute of Bioengineering School of EngineeringÉcole Polytechnique Fédérale de Lausanne(EPFL)LausanneSwitzerland The Biorobotics Institute Scuola Superiore Sant’AnnaPisaItaly Camin Team INRIAUniversity of MontpellierMontpellierFrance Institute of Neurology Catholic University of the Sacred HeartRomaItaly Institute of Neurosurgery Catholic University of the Sacred HeartRomaItaly Department of Neuroscience&Neurorehabilitation IRCCS San Raffaele Roma00166 RomeItaly Department of Electrical and Electronic Engineering University of CagliariCagliariItaly Biomedical Engineering Department Indiana University-Purdue UniversityIndianapolisINUSA SMI Dept.Health Science and TechnologyAalborg UniversityAalborgDenmark AXONIC/Groupe MXM VallaurisFrance BrainLinks-BrainTools Albert-Ludwig-University FreiburgFreiburgGermany Bernstein Center Freiburg Albert-Ludwig-University FreiburgFreiburgGermany Present address:Laboratory for Neuroengineering Department of Health Sciences and TechnologyInstitute for Robotics and Intelligent SystemsETH ZürichZürichSwitzerland

Direct stimulation of peripheral nerves with implantable electrodes successfully provided sensory feedback to amputees while using hand *** of the electrodes is key to success,which we have improved for the polyimide-based transverse intrafascicular multichannel electrode(TIME).The TIMEs were implanted in the median and ulnar nerves of three trans-radial amputees for up to six *** present a comprehensive assessment of the electrical properties of the thin-film metallization as well as material status post *** TIMEs stayed within the electrochemical safe limits while enabling consistent and precise amplitude *** lead to a reliable performance in terms of eliciting *** signs of corrosion or morphological change to the thin-film metallization of the probes was observed by means of electrochemical and optical *** presented longevity demonstrates that thin-film electrodes are applicable in permanent implant systems.

关键词： film electrodes sensation

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Advances in sensor developments for cell culture monitoring

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BMEMat(biomedical engineering Materials) 2023年第4期1卷 4-28页

作者： Ka Ram Kim Woon-Hong Yeo George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGeorgiaUSA IEN Center for Human-Centric Interfaces and Engineering Institute for Electronics and NanotechnologyGeorgia Institute of TechnologyAtlantaGeorgiaUSA Wallace H.Coulter Department of Biomedical Engineering Georgia Tech and Emory University School of MedicineAtlantaGeorgiaUSA Institute for Materials Institute for Robotics and Intelligent MachinesNeural Engineering CenterGeorgia Institute of TechnologyAtlantaGeorgiaUSA

Cell culture encompasses procedures for extracting cells from their natural tissue and cultivating them under controlled artificial conditions. During this process, various factors, including cell physiological/morphological properties, culture environments, metabolites, and contaminants, have to be precisely controlled and monitored for the survival of cells and the pursuit of the desired properties of the cells. This review summarizes recent advances in sensor technologies and manufacturing strategies for various cell culture platforms using traditional plastics, microfluidic chips, and scalable bioreactors. We share the details of newly developed biological sensors, chemical sensors, optical sensors, electronic chip technologies, and material integration methods. The precise control of parameters based on the feedback by these sensors and electronics enhances cell culture quality and throughput.

关键词： biosensor cell culture monitoring cell physiology monitoring electrochemical sensing metabolite optical sensing

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Predicting RTMS Treatment Effects Using Open-Loop Control and Neural Manifold

Predicting RTMS Treatment Effects Using Open-Loop Control an...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hongyu Shi Kaizhong Zheng Huaning Wang Baojuan Li Badong Chen National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xijing Hospital Fourth Military Medical University Xi’an China School of Biomedical Engineering Fourth Military Medical University Xi’an China

Repetitive transcranial magnetic stimulation (rTMS) is a common non-invasive treatment for medication-resistant major depressive disorder (MDD). It utilizes continuous and adjustable magnetic stimulation to modulate neural circuits implicated in the pathogenesis of depression. Nevertheless, constructing a universal and effective predictive factor for forecasting treatment outcomes remains challenging. To address this, we first collect neuroimaging data and five depression scales from 26 medication-resistant MDD patients before and after rTMS treatment. Then we propose a novel framework for predicting treatment effects precisely, which combines open-loop control and neural manifold estimation. This framework utilizes the geometric information of the manifold as a biomarker to predict the therapeutic efficacy of rTMS. Experiments based on the clinical dataset demonstrate the effectiveness and robustness of our framework.

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Mapping Cognitive Engagement: EEG and Graph Theory Analysis of Brain Region Involvement in Supernumerary Robotic Finger Utilization

Mapping Cognitive Engagement: EEG and Graph Theory Analysis ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Rateb Katmah Mohammad I. Awad Aamna Al Shehhi Mohd Khalil Abu Hantash Feryal A. Alskafi Abdul Aziz Hulleck Herbert F. Jelinek Irfan Hussain Kinda Khalaf Department of Biomedical Engineering & Biotechnology Khalifa University AbuDhabi United Arab Emirates Center of Autonomous Robotics Systems (KUCARS) Khalifa University Abu Dhabi United Arab Emirates Healthcare Engineering and Innovation Center Khalifa University AbuDhabi United Arab Emirates Department of Mechanical Engineering Khalifa University AbuDhabi United Arab Emirates Department of Medical Sciences Khalifa University AbuDhabi United Arab Emirates

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

As the worldwide incidence of stroke increases, supernumerary robotic limbs (SRLs), more specifically supernumerary robotic fingers (SRFs), present a potentially effective solution for enhancing the task related functionality of the upper-limbs of stroke survivors. This study investigated the impact of an SRF use on cognitive function, employing both electroencephalography (EEG) and graph theory analysis (GTA). Three tasks were undertaken by 30 subjects with and without the SRF. The results revealed notable changes during driving, as well as SRF-assisted activities. Increased clustering coefficients in emotional expression regions during SRF-assisted driving contrasted with reduced connectivity strength between frontal brain regions, suggesting altered attention control. Local efficiency (LE) analysis indicated disruptions in neuronal communication during SRF-assisted tasks, impacting attention, motor planning, and judgment actions. Likewise, statistical analyses demonstrated noteworthy shifts in mental states between phases, emphasizing improved communication efficiency with negative LE values. These findings increased our understanding of SRF influence on brain dynamics, which may help guide future development of assistive tools and technology to assist stroke survivors with their day-to-day functional activities.

关键词： Statistical analysis Precision medicine Fingers User centered design Stroke (medical condition) Motors Electroencephalography Graph theory Planning Robots

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Deep Learning Based Neovascularization Detection in Color Fundus Photography Image 23

Deep Learning Based Neovascularization Detection in Color Fu...

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9th International Conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Guo, Tianjiao Yang, Jie Yu, Qi Institute of Medical Robotics Institute of Image Processing and Pattern Recognition Department of Automation School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Shanghai General Hospital Shanghai Jiao Tong University School of Medicine Shanghai China National Clinical Research Center for Eye Diseases Shanghai China

ISBN: (纸本)9781450399029

Diabetic retinopathy (DR) has already been one of the leading causes of vision loss. A large number of researches about deep learning-based DR screening using color retinal photography images have been proposed in recent years. However, existing works mainly concentrated on non-proliferative diabetic retinopathy (NPDR) lesions. The exploration of proliferative diabetic retinopathy (PDR) lesion, which is more serious, is still insufficient. In this paper, we explored into one typical PDR lesion named neovascularization (NV). We collected and annotated retinal images from public and private datasets to construct and release a new dataset for NV detection. Three tasks including segmentation, detection, and grading were conducted on our dataset by using a set of state-of-the-art deep learning models. The results show that introducing NV detection can benefit DR grading. However, the segmentation and detection of NV are still challenging and have considerable room for improvement. © 2023 Copyright held by the owner/author(s).

关键词： Color

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Squat Quality User Assessment Through Artificial Intelligence: A User-Specific Approach

Squat Quality User Assessment Through Artificial Intelligenc...

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International Symposium on Medical robotics (ISMR)

作者： Brett J. Glenn David Williams Kevin W. Sexton Joseph Sanford Sean Wilson Anthony L. Gunderman U of A for Medical Sciences Little Rock Arkansas USA Institute of Robotics and Intelligent Machines Georgia Institute of Technology/Emory Atlanta GA USA Department of Surgery and Biomedical Informatics Vanderbilt University Medical Center Nashville Tennessee USA Mechanical Engineering Department U of A Fayetteville Arkansas USA

ISBN: (数字)9798331599003

ISBN: (纸本)9798331599010

Strength training is a method of guided exercise that improves physical well-being through repeated loaded movement patterns. One such movement pattern that is core to lower extremity development is the barbell squat. The barbell squat is a movement pattern that requires experienced scrutiny to prevent severe injuries induced by high-risk squat movement patterns. However, professional resources are limited, particularly in rural areas. In this paper, we present a machine learning framework that is user-specific for Squat Quality User Assessment Through Artificial Intelligence (SQUAT-AI). The contributions of this work include the following: (i) a data processing pipeline and structure, (ii) a variational autoencoder for anomaly detection (AD-VAE) that identifies high-risk squat movement patterns (HR-SMP) with score-based feedback, and (iii) a VAE for generating a data set with HR-SMPs, eliminating the need for a participant to ever perform HR-SMPs for training a classification algorithm. This system is meant to provide a method of user assessment without a large population.

关键词： Training Medical robotics Tracking Instruments Autoencoders Pipelines Machine learning Data processing Injuries Extremities

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