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

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  • 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.

  • 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.

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