Author: hxwu

We are excited to share our latest work, “PneumaOCT: Pneumatic optical coherence tomography endoscopy for targeted distortion-free imaging in tortuous and narrow internal lumens”, which has been published in Science Advances!!! 🎉🎉🎉

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The complex anatomy of internal luminal organs, like bronchioles, poses challenges for endoscopic optical coherence tomography (OCT). These challenges include limited steerability for targeted imaging and nonuniform rotation distortion (NURD) with proximal scanning. Using rotary micromotors for distal scanning could address NURD but raises concerns about electrical safety and costs.

We present pneumaOCT, the first pneumatic OCT endoscope, comprising a steerable catheter with a soft pneumatic actuator and an imaging probe with a miniature pneumatic turbine. With a diameter of 2.8 mm, pneumaOCT allows for a bending angle of up to 237°, facilitating navigation through narrow turns. The pneumatic turbine enables adjustable imaging speeds from 51 to 446 revolutions per second. We demonstrate the pneumaOCT in vivo imaging of mouse esophagus and colon, as well as targeted and distortion-free imaging of peripheral bronchioles in a bronchial phantom and a porcine lung. This advancement substantially improves endoscopic OCT for navigational imaging in curved and narrow lumens.

In the future, we will further develop an autonomous pneumaOCT endoscopy system by integrating an automated external robotic system, navigation guidance (such as x-ray imaging or MRI), and intelligent sensing and control algorithms. This advancement will have a substantial impact on the development of robotic endoscopy techniques in complex clinical practice.

For more details, please find the paper at https://lnkd.in/gtcM7v7p

This is collaborative work between LabREN (http://www.labren.org/), ABI Lab (https://lnkd.in/gUuzQqDt) and Department of Surgery, CUHK. Congrats to all authors: Tinghua Zhang, Sishen YUAN, Chao Xu, Peng Liu, Hing-Chiu Chang, Sze Hang Calvin Ng, Hongliang Ren, and Wu ‘Scott’ YUAN.

An example application of pneumaOCT for targeted distortion-free imaging in the peripheral lung.
PneumaOCT imaging of ex vivo porcine lung.

Our latest research work titled “A Real-Time Self-Sensing Approach to Sensor Array Configuration Fusing Prior Knowledge for Reconfigurable Magnetic Tracking Systems” has been accepted by IEEE/ASME Transactions on Mechatronics!

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Impact:

The reconfigurability of magnetic tracking systems (MTSs) allows for its application in workspaces of different sizes. For instance, with appropriate configuration adjustments, it can be utilized for tasks such as tongue tracking in the head, tracheal intubation navigation in the neck, and even muscle tracking in the legs and arms. However, the dynamic changes in sensor array configuration, known as deformation, caused by posture changes during long-duration examinations, impose significant challenges on MTSs that heavily rely on magnetometer poses.

Here we propose a real-time self-sensing method based on the sensor array structural model and magnetic dipole model, which simultaneously estimates the magnet pose and the hinge angles on the sensor array. The self-sensing capability opens up a new way for perceiving the morphology of origami robots and measuring the curvature of flexible catheters

Paper: https://lnkd.in/dnmcvusm

Authors: Shijian Su; Xindi Yang; Zhen Li; Hongliang Ren

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We are excited to share our paper “OSSAR: Towards Open-Set Surgical Activity Recognition in Robot-assisted Surgery” which has been accepted for IEEE International Conference on Robotics and Automation (ICRA) 2024!

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In this work, we tackle the challenge of open-set recognition in surgical robotics. Our novel OSSAR framework improves the ability to classify known surgical activities while also detecting unknown activities that weren’t seen during training.

Key contributions:

• A hyperspherical reciprocal point strategy to better separate known and unknown classes

• A calibration technique to reduce overconfident misclassifications 

• New open-set benchmarks on the JIGSAWS dataset and our novel DREAMS dataset for endoscopic procedures

• State-of-the-art performance on open-set surgical activity recognition tasks

This research takes an important step towards more robust and generalizable AI systems for surgical robots. We hope it will help pave the way for safer and more capable robot-assisted surgeries.

Thank all the amazing co-authors Long Bai, Guankun Wang, Jie Wang, Xiaoxiao Yang, Huxin Gao, Xin Liang, An Wang, Mobarakol Islam, and Hongliang Ren

and our institutions (The Chinese University of Hong Kong, Beijing Institute of Technology, Qilu Hospital of Shandong University, Tongji University, University College London, National University of Singapore) for their support.

You can find more details in our paper https://lnkd.in/gDsjVDSP

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We are happy to share our journal paper titled “AMagPoseNet: Real-Time 6-DoF Magnet Pose Estimation by Dual-Domain Few-Shot Learning from Prior Model” published in IEEE Transactions on Industrial Informatics!

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Impact: Traditional magnetic tracking approaches based on mathematical models and optimization algorithms are computationally intensive, depend on initial guesses, and do not guarantee convergence to a global optimum. Here we propose an annular magnet pose estimation network (called AMagPoseNet) based on dual-domain few-shot learning from a prior mathematical model, featuring the following advantages:

1) Higher localization accuracy (1.87±1.14 mm, 1.89±0.81°), especially in the near field;

2) Enhanced robustness, as AMagPoseNet is just a single feed-forward neural network that does not rely on initial guesses and avoids the risk of falling into local optima;

3) Lower computational latency (2.08±0.02 ms) since the magnet pose is directly regressed from a single feed-forward network rather than iterative optimization;

4) Real-time estimation of 6-DoF pose if discriminative magnetic field features are provided.

Paper: https://lnkd.in/gXBSQ-hG

Dataset: https://lnkd.in/gXEFfwfA

Authors: Shijian Su; Sishen YUAN; Mengya Xu; Huxin Gao; Xiaoxiao Yang; and Prof Hongliang Ren

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Excited to share our journal paper entitled “Magnetic Tracking With Real-Time Geomagnetic Vector Separation for Robotic Dockable Charging” published in IEEE Transactions on Intelligent Transportation Systems! 🎉

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Great collaboration between the Chinese University of Hong Kong and the Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences. 🤝

The superposition of the geomagnetic vector and the magnetic field vector generated by the permanent magnet (PM) leads to the degrading of magnetic tracking performance. Here we present a real-time geomagnetic-vector-separation method to estimate the PM pose and geomagnetic vector simultaneously. This advancement promises to revolutionize autonomous robotic operations, offering a robust solution for seamless and reliable self-charging mechanisms, with far-reaching implications for various industries.

Paper: https://lnkd.in/gAbr82Dp

Authors: Shijian Su; Houde Dai; Yuanchao Zhang; Sishen YUAN; Prof Shuang Song and Prof Hongliang Ren.

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Our latest research work titled “A Wearable, Reconfigurable, and Modular Magnetic Tracking System for Wireless Capsule Robots” has been accepted by IEEE Transactions on Industrial Informatics! 🎉

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Existing wearable MTSs are fixed in size and cannot accommodate patients with diverse abdominal circumferences. Here we propose a wearable and reconfigurable MTS. First, we design a reconfigurable sensor array inspired by the structure of bamboo slips, allowing it to conform to the abdominal surface and accommodate individuals with different abdominal circumferences. Next, we formulate a magnetic tracking optimization problem based on the magnetic dipole model and our established kinematic model of the reconfigurable sensor array.

Our proposed system is portable, reconfigurable and adaptable to different abdominal circumferences, offering valuable technological means for diagnosing and treating gastrointestinal disorders.

Paper: https://lnkd.in/g7HqPARr

Authors: Shijian Su; Sishen YUAN; Zhen Li; Yan Ma; Miaomiao Ma and Prof Hongliang Ren

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We’re thrilled to share our work, “Inconstant curvature kinematics of parallel continuum robot without static model”, which has been accepted at the IEEE International Conference on Robotics and Automation (ICRA 2024)!

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In the study of minimally invasive surgical robots, a mini parallel continuum robot (PCR) has shown motion advantage after passing through a long and winding working channel. However, due to the interaction force between the elastic wires of the parallel robots during motion generation processes, the constant curvature assumption has shown modeling errors. This causes the current geometric kinematic model to become unreliable. This paper aims to solve this issue. The simulation in ANSYS is carried out, and the shape of one of the driving wires, when bending, is fitted by a two-segment polynomial curve. Then, the position of the distal wrist tip can be calculated based on the curve shape. To verify the accuracy of the proposed model, bending simulation and experiment are carried out. The accuracy of the proposed model is compared with that of the kinematic model based on constant curvature assumption. The result shows that the proposed model can get more accurate results, especially when the driving wire displacement increases.

Main contributions:

1. A two-segment polynomial curve was used to model the deformation of the parallel wrist joint. Compared with the kinematic model based on constant curvature assumption, the proposed curve has shown higher accuracy. 

2. The fitting results of the chosen NiTi wire with the proposed curve were verified by finite element simulation (Fig. 3).

3. The modeling accuracy was verified when the deflection angle was 0°, 30° and 45° respectively. The experimental results show that the accuracy of this method is improved significantly than that of the constant curvature model. Especially, when the bending angle is increased, its accuracy does not decrease significantly.

Stay tuned to more of our research on parallel continuum robot!!!

Co-authors: Tao Zhang, Huxin Gao and Prof Hongliang Ren.

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We are happy to share our work entitled “Chained Flexible Capsule Endoscope: Unraveling the Conundrum of Size Limitations and Functional Integration for Gastrointestinal Transitivity”, will be presented in 2024 IEEE International Conference on Robotics and Automation (ICRA2024).

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Capsule endoscopes, predominantly serving diagnostic functions, provide lucid internal imagery but are devoid of surgical or therapeutic capabilities. Consequently, despite lesion detection, physicians frequently resort to traditional endoscopic or open surgical procedures for treatment, resulting in more complex, potentially risky interventions.

To surmount these limitations, this study introduces a chained flexible capsule endoscope (FCE) design concept, specifically conceived to navigate the inherent volume constraints of capsule endoscopes whilst augmenting their therapeutic functionalities. The FCE’s distinctive flexibility originates from a conventional rotating joint design and the incision pattern in the flexible material. In vitro experiments validated the passive navigation ability of the FCE in rugged intestinal tracts. Further, the FCE demonstrates consistent reptile-like peristalsis under the influence of an external magnetic field, and possesses the capability for film expansion and disintegration under high-frequency electromagnetic stimulation. These findings illuminate a promising path toward amplifying the therapeutic capacities of capsule endoscopes without necessitating a size compromise.

Author team: Sishen YUAN, Guang Li, Baijia Liang, Lailu Li, Qingzhuo Zheng, and Prof Hongliang Ren from the Chinese University of Hong Kong, Prof Shuang Song from Harbin Institute of Technology, Shenzhen, and Dr Zhen Li from Qilu Hospital of Shandong University.

For details, please check the paper at https://lnkd.in/gcq2HB9H

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We are excited to share our recent work by Sishen Yuan et al., entitled “Magnetic-Guided Flexible Origami Robot toward Long-Term Phototherapy of H. pylori in the Stomach” presented in 2024 IEEE International Conference on Robotics and Automation (ICRA2024).

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Helicobacter pylori, a pervasive bacterial infection associated with gastrointestinal disorders such as gastritis, peptic ulcer disease, and gastric cancer, impacts approximately 50% of the global population. The efficacy of standard clinical eradication therapies is diminishing due to the rise of antibiotic-resistant strains, necessitating alternative treatment strategies. Photodynamic therapy (PDT) emerges as a promising prospect in this context.

This study presents the development and implementation of a magnetically-guided origami robot, incorporating flexible printed circuit units for sustained and stable phototherapy of Helicobacter pylori. Each integrated unit is equipped with wireless charging capabilities, producing an optimal power output that can concurrently illuminate up to 15 LEDs at their maximum intensity. Crucially, these units can be remotely manipulated via a magnetic field, facilitating both translational and rotational movements.

We propose an open-loop manual control sequence that allows the formation of a stable, compliant triangular structure through the interaction of internal magnets. This adaptable configuration is uniquely designed to withstand the dynamic squeezing environment prevalent in real-world gastric applications. The research herein represents a significant stride in leveraging technology for innovative medical solutions, particularly in the management of antibiotic-resistant Helicobacter pylori infections.

This is a collabrative work by Sishen YUAN, Baijia Liang, Po Wa Wong, Mingjing Xu, Chi Hsuan Li and Prof Hongliang Ren from The Chinese University of Hong Kong, and Dr. Zhen Li from Qilu Hospital of Shandong University.

For details, please check the paper at https://lnkd.in/g3VGZaA3

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🌟 Exciting News! Our latest research work, entitled “RASEC: Rescaling Acquisition Strategy With Energy Constraints Under Fusion Kernel for Active Incision Recommendation in Tracheotomy”, has been accepted by IEEE Transactions on Automation Science and Engineering (T-ASE).

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🔍 In this paper, we unveil an innovative autonomous palpation-based acquisition strategy – RASEC, designed for the tracheal region. RASEC predicts the next acquisition point interactively, maximizing expected information and minimizing palpation procedure costs. By leveraging a Gaussian Process (GP) to model tissue hardness distribution and anatomical information as a guiding input for medical robots, RASEC revolutionizes robot-assisted subtasks in tracheotomy.

💡 We introduce a dynamic tactile sensor based on resonant frequency to measure tissue hardness at millimeter-scale precision, ensuring secure interactions. By exploring kernel fusion techniques blending Squared Exponential (SE) and Ornstein-Uhlenbeck (OU) kernels, and optimizing Bayesian search with larynx anatomical data, we enhance exploration efficiency and accuracy.

🔬 Our research considers new factors like tactile sensor movement and robotic base rotation in the acquisition strategy. Simulation and physical phantom experiments demonstrate a remarkable 53.1% reduction in sensor movement and 75.2% reduction in base rotation, with superior algorithmic performance metrics (average precision 0.932, average recall 0.973, average F1 score 0.952) and minimal distance errors (0.423 mm) at a high resolution of 1 mm.

🚀 The results showcase RASEC’s excellence in exploration efficiency, cost-effectiveness, and incision localization accuracy in real robot-assisted tracheotomy procedures.

This collaborative work is achieved by WENCHAO YUE, Fan Bai, Jianbang Liu, and Prof Hongliang Ren from The Chinese University of Hong Kong, Prof Feng Ju from Nanjing University of Aeronautics and Astronautics, Prof Max Q.-H. Meng from Southern University of Science and Technology, and Dr. Chwee Ming Lim from Singapore General Hospital.

Paper is available at https://lnkd.in/gEgmaDVj