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Guiding the Last Centimeter: Novel Anatomy-Aware Probe Servoing for Standardized Imaging Plane Navigation in Robotic Lung Ultrasound

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arXiv 2024年

作者： Ma, Xihan Zeng, Mingjie Hill, Jeffrey C. Hoffmann, Beatrice Zhang, Ziming Zhang, Haichong K. The Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01609 United States The Department of Computer Science Worcester Polytechnic Institute WorcesterMA01609 United States The Department of Diagnostic Medical Sonography School of Medical Imaging and Therapeutics MCPHS University WorcesterMA01608 United States The Department of Emergency Medicine Beth Israel Deaconess Medical Center BostonMA02215 United States The Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States The Department of Biomedical Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

Navigating the ultrasound (US) probe to the standardized imaging plane (SIP) for image acquisition is a critical but operator-dependent task in conventional freehand diagnostic US. Robotic US systems (RUSS) offer the potential to enhance imaging consistency by leveraging real-time US image feedback to optimize the probe pose, thereby reducing reliance on operator expertise. However, determining the proper approach to extracting generalizable features from the US images for probe pose adjustment remains challenging. In this work, we propose a SIP navigation framework for RUSS, exemplified in the context of robotic lung ultrasound (LUS). This framework facilitates automatic probe adjustment when in proximity to the SIP. This is achieved by explicitly extracting multiple anatomical features presented in real-time LUS images and performing non-patient-specific template matching to generate probe motion towards the SIP using image-based visual servoing (IBVS). The framework is further integrated with the active-sensing end-effector (A-SEE), a customized robot end-effector that leverages patient external body geometry to maintain optimal probe alignment with the contact surface, thus preserving US signal quality throughout the navigation. The proposed approach ensures procedural interpretability and inter-patient adaptability. Validation is conducted through anatomy-mimicking phantom and in-vivo evaluations involving five human subjects. The results show the framework's high navigating precision with the probe correctly located at the SIP for all cases, exhibiting positioning error of under 2 mm in translation and under 2 degrees in rotation. These results demonstrate the navigation process's capability to accommodate anatomical variations among patients. Copyright © 2024, The Authors. All rights reserved.

关键词： Probes

Multi Jet Fusion of Nylon-12: A Viable Method to 3D-print Concentric Tube Robots?

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arXiv 2022年

作者： Picho, Kalani Persons, Brandon d'Almeida, Jesse F. Pacheco, Nicholas E. Reynolds, Colin Fichera, Loris Department of Robotics Engineering Worcester Polytechnic Institute United States

In this paper, we present a study on the viability of fabricating Concentric Tube Robots using Multi Jet Fusion (MJF) of Nylon-12, a type of elastic polymer commonly used in additive manufacturing. We note that Nylon-12 was already evaluated for the purpose of building CTRs in prior work, but fabrication was performed with Selective Laser Sintering (SLS), which produced unsatisfactory results. Our study is the first study to evaluate the suitability of MJF to 3Dprint CTRs. Copyright © 2022, The Authors. All rights reserved.

关键词： 3D printers

STREAM: Software Tool for Routing Efficiently Advanced Macrofluidics

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arXiv 2023年

作者： Wang, Lehong Kendre, Savita V. Liu, Haotian Nemitz, Markus P. Robotic Materials Group Department of Robotics Engineering Worcester Polytechnic Institute MA01609 United States Department of Computer Science Worcester Polytechnic Institute MA01609 United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute MA01609 United States

The current fabrication and assembly of fluidic circuits for soft robots relies heavily on manual processes;as the complexity of fluidic circuits increases, manual assembly becomes increasingly arduous, error-prone, and time-consuming. We introduce a software tool that generates printable fluidic networks automatically. We provide a library of fluidic logic elements that are easily 3D printed from thermoplastic polyurethanes using Fused Deposition Modeling only. Our software tool and component library allow the development of arbitrary soft digital circuits. We demonstrate a variable frequency ring oscillator and a full adder. The simplicity of our approach using FDM printers only, democratizes fluidic circuit implementation beyond specialized laboratories. Our software is available on GitHub (https://***/roboticmaterialsgroup/FluidLogic) Copyright © 2023, The Authors. All rights reserved.

关键词： Computer software

Realistic Data Generation for 6D Pose Estimation of Surgical Instruments

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arXiv 2024年

作者： Barragan, Juan Antonio Zhang, Jintan Zhou, Haoying Munawar, Adnan Kazanzides, Peter Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01608 United States

Automation in surgical robotics has the potential to improve patient safety and surgical efficiency, but it is difficult to achieve due to the need for robust perception algorithms. In particular, 6D pose estimation of surgical instruments is critical to enable the automatic execution of surgical maneuvers based on visual feedback. In recent years, supervised deep learning algorithms have shown increasingly better performance at 6D pose estimation tasks;yet, their success depends on the availability of large amounts of annotated data. In household and industrial settings, synthetic data, generated with 3D computer graphics software, has been shown as an alternative to minimize annotation costs of 6D pose datasets. However, this strategy does not translate well to surgical domains as commercial graphics software have limited tools to generate images depicting realistic instrument-tissue interactions. To address these limitations, we propose an improved simulation environment for surgical robotics that enables the automatic generation of large and diverse datasets for 6D pose estimation of surgical instruments. Among the improvements, we developed an automated data generation pipeline and an improved surgical scene. To show the applicability of our system, we generated a dataset of 7.5k images with pose annotations of a surgical needle that was used to evaluate a state-of-the-art pose estimation network. The trained model obtained a mean translational error of 2.59 mm on a challenging dataset that presented varying levels of occlusion. These results highlight our pipeline’s success in training and evaluating novel vision algorithms for surgical robotics applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

Demonstration and experimental validation of plastic-encased resonant ultrasonic piezoelectric actuator for magnetic resonance imaging-guided surgical robots

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Journal of engineering and Science in Medical Diagnostics and Therapy 2020年第1期3卷 011002页

作者： Carvalho, Paulo A.W.G. Nycz, Christopher J. Gandomi, Katie Y. Fischer, Gregory S. Robotics Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Mechanical and Robotics Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

Intra-operative medical imaging based on magnetic resonance imaging (MRI) coupled with robotic manipulation of surgical instruments enables precise feedback-driven procedures. Electrically powered nonferromagnetic motors based on piezoelectric elements have shown to be well suited for MRI robots. However, even avoiding ferrous materials, the high metal content on commercially available motors still cause distortions to the magnetic fields. We construct semicustom piezoelectric actuators wherein the quantity of conductive material is minimized and demonstrate that the distortion issues can be partly addressed through substituting several of these components for plastic equivalents, while maintaining motor functionality. Distortion was measured by assessing the root-mean-squared (RMS) change in position of 49 centroid points in a 12.5 mm square grid of a gelatin-filled phantom. The metal motor caused a distortion of up to 4.91 mm versus 0.55 mm for the plastic motor. An additional signal-to-noise-ratio (SNR) drop between motor off and motor spinning of approximately 20% was not statistically different for metal versus plastic (p ¼ 0.36). Copyright © 2020 by ASME

关键词： Magnetic resonance imaging

Suturing Tasks Automation Based on Skills Learned from Demonstrations: A Simulation Study

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Suturing Tasks Automation Based on Skills Learned from Demon...

International Symposium on Medical robotics (ISMR)

作者： Haoying Zhou Yiwei Jiang Shang Gao Shiyue Wang Peter Kazanzides Gregory S. Fischer Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Computer Science Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350377118

ISBN: (纸本)9798350377125

In this work, we develop an open-source surgical simulation environment that includes a realistic model obtained by MRI-scanning a physical phantom, for the purpose of training and evaluating a Learning from Demonstration (LfD) algorithm for autonomous suturing. The LfD algorithm utilizes Dynamic Movement Primitives (DMP) and Locally Weighted Regression (LWR), but focuses on the needle trajectory, rather than the instruments, to obtain better generality with respect to needle grasps. We conduct a user study to collect multiple suturing demonstrations and perform a comprehensive analysis of the ability of the LfD algorithm to generalize from a demonstration at one location in one phantom to different locations in the same phantom and to a different phantom. Our results indicate good generalization, on the order of 91.5%, when learning from more experienced subjects, indicating the need to integrate skill assessment in the future.

关键词： Training Medical robotics Automation Heuristic algorithms Instruments Phantoms Needles

Autonomous Search of Semantic Objects in Unknown Environments

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arXiv 2023年

作者： Qian, Zhentian Fu, Jie Xiao, Jing Robotics Engineering Department Worcester Polytechnic Institute WorcesterMA United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States

This paper addresses the problem of enabling a robot to search for a semantic object, i.e., an object with a semantic label, in an unknown and GPS-denied environment. For the robot in the unknown environment to detect and find the target semantic object, it must perform simultaneous localization and mapping (SLAM) at both geometric and semantic levels using its onboard sensors while planning and executing its motion based on the ever-updated SLAM results. In other words, the robot must be able to conduct simultaneous localization, semantic mapping, motion planning, and execution in real-time in the presence of sensing and motion uncertainty. This is an open problem as it combines semantic SLAM based on perception and real-time motion planning and execution under uncertainty. Moreover, the goals of the robot motion change on the fly depending on whether and how the robot can detect the target object. We propose a novel approach to tackle the problem, leveraging semantic SLAM, Bayesian Networks, Markov Decision Process, and Real-Time Dynamic programming. The results in simulation and real experiments demonstrate the effectiveness and efficiency of our approach. © 2023, CC BY.

关键词： Semantics

Human Following Robot Based on Object Detection for Aerospace Application 1

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Human Following Robot Based on Object Detection for Aerospac...

1st International Conference of Adisutjipto on Aerospace Electrical engineering and Informatics, ICAAEEI 2024

作者： Sumari, Arwin Datumava Wahvudi Aryandika, Ahnaf Bagus Purnami, Ndaru Atmi Dermawan, Denny Syamsiana, Ika Noer State Polytechnic of Malang Cognitive Artificial Intelligence Research Group Department of Electrical Engineering Malang Indonesia Adisutjipto Institute of Aerospace Technology Electrical Engineering Study Program Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Aerospace Navigation Instrumentation Control Automation and Robotics System Research Group Electrical Engineering Study Program Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Electrical Engineering Study Program Aerosp. Digit. Syst. Microprocessor Microcontroller and Programmable Logic Devices Research Group Yogyakarta Indonesia

ISBN: (纸本)9798331507930

In this research, the Human Following Robot (HFR) prototype has been designed and implemented using YOLO v3-Tiny and Tensor Flow Lite on Raspberry Pi hardware named Rewang. The HFR Rewang is designed to assist aircraft technicians by bringing maintenance tool kits to aircraft shelters. Rewang's movement is based on a camera as a vision sensor following the movement of human entities. Image input is processed by YOLO v3-Tiny through the determination of the bounding box, the calculation of Euclidean Distance, and the determination of the pixel value of the camera as the basis of Rewang's movement which is regulated by an L298N driver motor that controls two Direct Current (DC) motors for the left and right wheels. The test results show that Rewang can move at an average speed of 0.18 m/s at 2 m with a travel time of 11 seconds with a light intensity of more than 250 lux. The minimum and maximum distance of object detection is as far as 120 cm and 400 cm, with an object detection accuracy of 95.8%. Rewang allows the HFR to have a larger carrying capacity to bring more complete aircraft maintenance tools and is prospective for other aerospace and general applications. © 2024 IEEE.

关键词： Travel time

Cross-Modality Registration using Bone Surface Pointcloud for Robotic Ultrasound-Guided Spine Surgery

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Journal of Medical robotics Research 2025年第0期

作者： Ma, Xihan Zhang, Xiao Wang, Yang Nycz, Christopher J. Sungarian, Arno Ji, Songbai Huang, Xinming Zhang, Haichong K. Department of Robotics Engineering Worcester Polytechnic Institute 100 Institute Rd. Worcester 01609 MA United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute 100 Institute Rd. Worcester 01609 MA United States Memorial Medical Center University of Massachusetts 55 N Lake Ave. Worcester 01655 MA United States Department of Biomedical Engineering Worcester Polytechnic Institute 100 Institute Rd. Worcester 01609 MA United States

Image guidance using preoperative magnetic resonance imaging (MRI) and intraoperative ultrasound (US) can improve the outcome of spine surgery. Employing a robotic US system (RUSS) allows the automated acquisition of large 3D US volumes, facilitating accurate registration. However, such registration remains challenging due to the cross-modality discrepancy. To address this issue, we present a pipeline that extracts spine pointclouds from MRI and 3D US to perform per-vertebra registration. Experiments showed a registration accuracy of 1.82 mm in terms of residual root mean square error and 7.02 mm in terms of Chamfer distance. The pipeline exhibits superior robustness to suboptimal initial conditions compared with the two baseline methods. It also demonstrated good time efficiency under real-time conditions, demonstrating the potential applicability in RUSS-guided spine surgeries. © World Scientific Publishing Company.

关键词： cross-modality registration image-guided intervention robotic ultrasound Spine surgery ultrasound