Featured Articles

  • Non-invasive Treatment Efficacy Evaluation for HIFU Therapy Based on Magneto-Acousto-Electrical Tomography

    Non-invasive Treatment Efficacy Evaluation for HIFU Therapy Based on Magneto-Acousto-Electrical Tomography

    To ensure the therapeutic effect of HIFU therapy with minimized damage to surrounding tissues, a monitoring method for thermal ablation using the MAET technology is proposed based on the temperature-conductivity relation of tissues. With the distributions of acoustic pressure, temperature and electrical conductivity for a cylindrical model, real-time simulations of MAET signal demonstrated that two wave clusters can be generated by the sharp conductivity variation of HIFU ablation at >69 ˚C with a minimum axial interval of one wavelength. The favorable results provide a sensitive modality for non-invasive treatment efficacy evaluation during HIFU therapy and suggest potential applications in biomedical engineering.

  • Background Removal and Vessel Filtering of Non-Contrast Ultrasound Images of Microvasculature

    Background Removal and Vessel Filtering of Non-Contrast Ultrasound Images of Microvasculature

    A clutter removal method is proposed that utilizes spatiotemporal coherence of the ultrasound plane wave imaging data to significantly suppress tissue clutter signal obtained over extended ensembles. Nonlinear filtering via morphological operations and Hessian-based analysis are proposed to provide superb background rejection and vessel enhancement. This new imaging method, solely based on ultrasound, enables visualization of the small vessels that may find applications in both preclinical and clinical settings. In clinical applications, this method may provide a versatile tool for monitoring angiogenesis which may provide invaluable diagnostic and prognostic information.

  • Efficient Bronchoscopic Video Summarization

    Efficient Bronchoscopic Video Summarization

    Guided by the bronchoscope’s video stream, a physician can navigate the complex three-dimensional (3-D) airway tree to collect tissue samples or administer a disease treatment. Unfortunately, physicians currently discard procedural video because of the overwhelming amount of data generated. We propose a robust automatic method for summarizing an endobronchial video stream. Overall, the method derives a true hierarchical decomposition from a procedural video, consisting of a shot set and constituent keyframe set. Results show that our method more efficiently covers the observed endobronchial regions than other keyframe-selection approaches and facilitates direct fusion with a patient’s 3-D chest computed-tomography scan.

  • Predicting Athlete Ground Reaction Forces and Moments From Spatio-temporal Driven CNN Models

    Conventional methods to generate biomechanical data, required for traditional inverse dynamics estimation of athlete joint forces and loads, are confined to biomechanics laboratories far removed from the sporting field of play. This study used deep learning to predict 3D ground reaction forces and moments (GRF/M) from legacy marker-based motion capture sidestepping trials, ranking correspondence of multivariate regression from five convolutional neural network (CNN) models against ground truth force plate data. By fine-tuning from CaffeNet, a model derivative of ImageNet, mean predicted GRF/M correlations to ground truth above 0.97 were achieved for complex sport-related movements.

  • Model-based Sparse-to-dense Image Registration for Realtime Respiratory Motion Estimation in Image-guided Interventions

    Respiratory motion is known to be an important problem in non-invasive image-guided tumor interventions that needs to be accounted for to achieve an accurate treatment delivery. To this end, we propose a novel motion estimation method that estimates dense motion fields in realtime for the entire treatment region based on intra-interventional image data acquired by state-of-the-art treatment systems like MRI-Linacs. Our method achieves state-of-the-art tracking accuracy (≈ 1 mm) at high frame rates by combining GPU-accelerated sparse feature point matching and patient-specific regularisation using a learned PCA-based motion model in a unified registration framework.

  • An Intracardiac Flow Based Electromagnetic Energy Harvesting Mechanism for Cardiac Pacing

    Contemporary cardiac implantable electronic devices are powered by batteries. Replacement due to battery depletion may cause complications and is costly. To overcome these limitations, we present an energy harvesting device with a lever which is deflected by blood flow within the right heart. The kinetic energy of the lever is converted by an electromagnetic conversion principle. It generated a mean power of 14.39 / 82.64 µW at 60 / 200 bpm in an experimental setup mimicking flow conditions in the heart at 1 m/s peak flow. Therefore, it presents a viable alternative to batteries to power cardiac pacemakers.

  • A Portable Passive Rehabilitation Robot for Upper-Extremity Functional Resistance Training

    It is very common for individuals to experience a loss of arm function after neurological injury. Robotic devices can assist in recovery; however, current devices are typically too large, bulky, and expensive to be routinely used in the clinic or at home. Here, we developed and validated a low-cost portable planar passive rehabilitation robot (PaRRo) that uses a unique mechanical design and miniature eddy current brakes instead of motors to directly resist the user during reaching motions. Theoretical and experimental results show that this device could potentially serve as a valuable clinical tool to restore arm function after neurological injury.

  • Learning Patterns of Pivoting Neuromuscular Control Training-Towards a Learning Model

    Over the last decades, lower limb therapy using locomotion devices has been used; however, how the learning patterns over the course of a long-term training program change are unknown. The purpose of this study was to investigate the learning patterns in leg pivoting neuromuscular control performance over six-week pivoting neuromuscular control training (POINT) among 20 healthy subjects. Overall, learning patterns could be characterized by learning curve models such as the power law and exponential curve. The findings and models can potentially be used to develop more effective subject-specific therapy scheduling and gait therapy protocol.

  • Model of Impedance Changes in Unmyelinated Nerve Fibres

    Electrical impedance tomography produces cross-sectional images of the compound action potential in peripheral nerve; it has potential to provide images of active fascicles in autonomic nerves. It could be used in electroceuticals for selective stimulation of nerve fascicles and so avoid off-target effects.
    For this, the EIT parameters must be optimised. This has been aided by development of accurate FEM models of unmyelinated nerve fibres bi-directionally coupled with the external space. The models agreed with experiments and could serve as a basis for optimising EIT imaging and consequent selective stimulation.

  • An Automated Method for Multi-DOF Cell Rotational Control Contributing to Orientation-based Cell Surgery Applications

    An autonomous control framework for cell out-of-plane reorientation has been proposed by utilizing a robot-tweezers cell manipulation system. A serious of experiments that include cell in-plane, out-of-plane rotational control and cellular orientation-based enucleation manipulation have been performed, demonstrating the necessity and significance of cell orientation control involved in many cell surgery studies. The outcome of this study represents an advancement in single cell manipulation, and contributes to many cellular orientation-based cell surgery applications, such as cell nucleus biopsy and transplantation, cloning technology, and PGD.

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