Author: IEEE Transactions on Biomedical Engineering - new TOC

Objective: Rheumatoid arthritis (RA) is a chronic inflammatory syndrome that features painful and destructive joint disease. Aggressive disease-modifying treatment can result in reduced symptoms and protection from irreversible joint damage; however, assessment of treatment efficacy is currently based largely on subjective measures of patient and physician impressions. In this work, we address this compelling need to provide an accurate and quantitative capability for monitoring joint health in patients with RA. Methods: Joint acoustic emissions (JAEs), electrical bioimpedance (EBI), and kinematics were measured noninvasively from 11 patients with RA over the course of three weeks using a custom multimodal sensing brace, resulting in 49 visits with JAE recordings and 43 with EBI recordings. Features derived from all sensing modalities were fed into a linear discriminant analysis (LDA) model to predict disease activity according to the validated disease activity index (the DAS28-ESR). Erythrocyte sedimentation rate (ESR) was predicted using ridge regression and classified into a high or low class using LDA. Results: DAS28-ESR level was predicted with an area under the receiver operating characteristic curve (AUC) of 0.82. With JAEs alone, we were able to track intrasubject differences in the disease activity score as well as classify ESR level with an AUC of 0.93. The majority of patients reported both an interest and ability to use the brace at home for longitudinal monitoring. Conclusion: This work demonstrates the ability to detect RA disease activity using noninvasive sensing. Significance: This system has the potential to improve RA disease activity monitoring by giving treating clinicians objective data that can be acquired independent of a face-to-face clinic visit.

Fluorescence lifetime imaging is a valuable technique for probing characteristics of wide ranging samples and sensing of the molecular environment. However, the desire to measure faster and reduce effects such as photo bleaching in optical photon-count measurements for lifetime estimation lead to inevitable effects of convolution with the instrument response functions and noise, causing a degradation of the lifetime accuracy and precision. To tackle the problem, this paper presents a robust and computationally efficient framework for recovering fluorophore sample decay from the histogram of photon-count arrivals modelled as a decaying single-exponential function. In the proposed approach, the temporal histogram data is first decomposed into multiple bins via an adaptive multi-bin signal representation. Then, at each level of the multi-resolution temporal space, decay information including both the amplitude and the lifetime of a single-exponential function is rapidly decoded based on a novel statistical estimator. Ultimately, a game-theoretic model consisting of two players in an “amplitude-lifetime” game is constructed to be able to robustly recover optimal fluorescence decay signal from a set of fused multi-bin estimates. In addition to theoretical demonstrations, the efficiency of the proposed framework is experimentally shown on both synthesised and real data in different imaging circumstances. On a challenging low photon-count regime, our approach achieves about 28% improvement in bias than the best competing method. On real images, the proposed method processes data on average around 63 times faster than the gold standard least squares fit. Implementation codes are available to researchers.

Embryo vitrification is a fundamental technology utilized in assisted reproduction and fertility preservation. Vitrification involves sequential loading and unloading of cryoprotectants (CPAs) with strict time control, and transferring the embryo in a minimum CPA droplet to the vitrification straw. However, manual operation still cannot effectively avoid embryo loss, and the existing automatic vitrification systems have insufficient system reliability, and operate differently from clinical vitrification protocol. Through collaboration with in vitro fertilization (IVF) clinics, we are in the process realizing a robotic system that can automatically conduct the embryo vitrification process, including the pretreatment with CPAs, transfer of embryo to the vitrification straw, and cryopreservation with liquid nitrogen ($rm LN_{2}$). An open microfluidic chip (OMC) was designed to accommodate the embryo during the automatic CPAs pretreatment process. The design of two chambers connected by a capillary gap facilitated solution exchange around the embryo, and simultaneously reduced the risk of embryo loss in the flow field. In accordance to the well-accepted procedure and medical devices in manual operation, we designed the entire vitrification protocol, as well as the robotic prototype. In a practical experiment using mouse embryos, our robotic system showed a 100$%$ success rate in transferring and vitrifying the embryos, achieved comparable embryo survival rates (90.9$%$ versus 94.4$%$) and development rates (90.0$%$ versus 94.1$%$), when compared with the manual group conducted by the senior embryologist. With this study, we aim to facilitate the standardization of clinical vitrification from manual operation to a more efficient and reliable automated process.

Objective: This study demonstrates intravascular micro-agent visualization by utilizing robotic ultrasound-based tracking and visual servoing in clinically-relevant scenarios. Methods: Visual servoing path is planned intraoperatively using a body surface point cloud acquired with a 3D camera and the vessel reconstructed from ultrasound (US) images, where both the camera and the US probe are attached to the robot end-effector. Developed machine vision algorithms are used for detection of micro-agents from minimal size of 250$boldsymbol{mu }$m inside the vessel contour and tracking with error recovery. Finally, real-time positions of the micro-agents are used for servoing of the robot with the attached US probe. Constant contact between the US probe and the surface of the body is accomplished by means of impedance control. Results: Breathing motion is compensated to keep constant contact between the US probe and the body surface, with minimal measured force of 2.02 N. Anthropomorphic phantom vessels are segmented with an Intersection-Over-Union (IOU) score of 0.93 $pm$ 0.05, while micro-agent tracking is performed with up to 99.8% success rate at 28–36 frames per second. Path planning, tracking and visual servoing are realized over 80 mm and 120 mm long surface paths. Conclusion: Experiments performed using anthropomorphic surfaces, biological tissue, simulation of physiological movement and simulation of fluid flow through the vessels indicate that robust visualization and tracking of micro-agents involving human patients is an achievable goal.

Dynamic positron emission tomography (dPET) is currently a widely used medical imaging technique for the clinical diagnosis, staging and therapy guidance of all kinds of human cancers. Higher temporal imaging resolution for the early stage of radiotracer metabolism is desired; however, in this case, the reconstructed images with short frame durations always suffer from a limited image signal-to-noise ratio (SNR) and unsatisfactory image spatial resolution. The appearance of uEXPLORER (United Imaging Healthcare, Inc.) with higher PET imaging sensitivity and resolution may help solving this problem. In this work, based on dynamic PET data acquired by uEXPLORER, we proposed a dPET processing method that denoises images with short frame durations via pixel-level time-activity curve (TAC) correction based on third-order Hermite interpolation (Pitch-In). The proposed method was validated and compared to several state-of-the-art methods to demonstrate its superior performance in terms of high temporal resolution dPET image noise reduction and imaging accuracy. Higher stability and feasibility of the proposed Pitch-In method for future clinical application with high temporal resolution (HTR) dPET imaging can be expected.

Objective: ECG recordings often suffer from a set of artifacts with varying types, severities, and durations, and this makes an accurate diagnosis by machines or medical doctors difficult and unreliable. Numerous studies have proposed ECG denoising; however, they naturally fail to restore the actual ECG signal corrupted with such artifacts due to their simple and naive noise model. In this pilot study, we propose a novel approach for blind ECG restoration using cycle-consistent generative adversarial networks (Cycle-GANs) where the quality of the signal can be improved to a clinical level ECG regardless of the type and severity of the artifacts corrupting the signal. Methods: To further boost the restoration performance, we propose 1D operational Cycle-GANs with the generative neuron model. Results: The proposed approach has been evaluated extensively using one of the largest benchmark ECG datasets from the China Physiological Signal Challenge (CPSC-2020) with more than one million beats. Besides the quantitative and qualitative evaluations, a group of cardiologists performed medical evaluations to validate the quality and usability of the restored ECG, especially for an accurate arrhythmia diagnosis. Significance: As a pioneer study in ECG restoration, the corrupted ECG signals can be restored to clinical level quality. Conclusion: By means of the proposed ECG restoration, the ECG diagnosis accuracy and performance can significantly improve.