Featured Articles

  • Generation of patient-specific cardiac vascular networks: a hybrid image-based and synthetic geometric model

    Generation of Patient-Specific Cardiac Vascular Networks: a Hybrid Image-Based and Synthetic Geometric Model

    The image resolution of CT angiography currently precludes vessel segmentation of coronary arteries smaller than approximately 1 mm in size and affects blood flow analysis. We propose an algorithm for the generation of patient-specific vascular networks starting from segmented epicardial vessels. We extend a tree generation method based on functional principles to account for multiple, competing vascular trees. From segmented vascular tree models of several patients, we generate networks (~50 vascular trees) filling the entire left ventricle myocardium down to the arteriole size level. All vascular models match morphometry properties previously described and enable potential applications for blood flow simulation and disease modeling.

  • A New Modeling Method to Characterize the Stance Control Function of Prosthetic Knee Joints

    A New Modeling Method to Characterize the Stance Control Function of Prosthetic Knee Joints

    A new method is presented to characterize the function of lower-limb prosthetic stance control under mobility conditions associated with activities of daily living. The method is based on a model of the gait modes corresponding to finite stance control states. Empirical data from amputee and simulated gait were acquired using a custom built wearable instrument and input into the model. The modeling approach was shown to be robust, responsive and capable of accurate characterization of controller function under diverse of locomotor and prosthetic setup conditions.

  • Quantification and Analysis of Laryngeal Closure from Endoscopic Videos

    Quantification and Analysis of Laryngeal Closure from Endoscopic Videos

    We propose an automatic method to quantify laryngeal movements from laryngoscopic videos, to facilitate the diagnosis procedure. The proposed method analyses laryngoscopic videos, and delineates glottic opening, vocal folds, and supraglottic structures, using a deep learning-based algorithm. The segmentation results are quantified along the temporal dimension and processed using singular spectrum analysis (SSA), to extract information that can be used by the clinicians in diagnosis. The segmentation was validated on 400 images from 20 videos acquired using different endoscopic systems from different patients. Five clinical cases on patients have also been provided to showcase the final quantitative analysis result.

  • CMOS Based Smart Petridish: A Paradigm Shift in Drug Testing Technology

    Smart Cell Culture Monitoring and Drug Test Platform Using CMOS Capacitive Sensor Array

    Integrated Microfluidic CMOS or imCMOS has recently received significant interest, as a new paradigm in the design and implementation of chemical/biological analysis platforms, for life science applications. Among these applications, this research has focused on developing a novel imCMOS device for monitoring drug cytotoxicity. This device incorporated with polyelectrolyte layers consists of 8×8 capacitive sensors integrated on the same chip. With the potential to perform label free cellular analysis, the proposed platform opens an avenue to transit from traditional to smart cellular analysis techniques suitable for a variety of biological applications, in particular high throughput cell based drug testing.

  • Acousto-optic Catheter Tracking Sensor for Interventional MRI Procedures

    Acousto-optic Catheter Tracking Sensor for Interventional MRI Procedures

    Catheter tracking and guidance in the body is essential for interventional procedures. However, traditional catheters are invisible under magnetic resonance imaging (MRI). We present an acousto-optic sensor for tracking catheter position during interventional MRI. A coil antenna collects local MRI signal. In order to eliminate the RF induced heating, optical fiber is used to carry the MRI signal. An acousto-optic modulator based on fiber Bragg grating (FBG) is developed to convert the electrical signal from the antenna to optical signal to be carried by the fiber. Sensor was successfully tested for position detection in phantom under MRI.

  • 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.

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