Author: IEEE Transactions on Biomedical Engineering - new TOC

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.

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.

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.

Objective: This study aimed to develop a robust and data driven automatic motion artifacts (MA) removal technique from electrodermal activity (EDA) signal. Methods: we proposed a deep convolutional autoencoder (DCAE) approach for automatic MA removal in EDA signals. Our model was trained using several publicly available datasets that were collected using a wide variety of stimuli to cause EDA reactions; the sample size was large ($mathbf{N} = 385 text{subjects}$). We trained and validated our DCAE network using both Gaussian white noise (GWN) and realistic MA data records collected using a novel circuitry in our lab. We further evaluated and compared the performance of our DCAE model with the existing methods on two independent and unseen datasets called Chon lab motion artifact dataset II (CMAD II) and central nervous system oxygen toxicity dataset (CNS-OT). Results: Our DCAE model showed significantly higher signal-to-noise-power-ratio improvement ($mathbf{SN}{mathbf{R}_{mathbf{imp}}}$) and lower mean squared error ($mathbf{MSE}$) when compared with that of the three previous methods (averaged $boldsymbol{S!N!R}_text{imp} = 35.25,{text{dB}}$, and ${boldsymbol{M!S!E }} = 0.028$ on the MA-corrupted data). Moreover, the reconstructed EDAs from the CMAD II dataset had a mean correlation value of 0.78 (statistically significantly higher when compared with other methods) with the reference clean data from the motionless hand, whereas the raw MA-corrupted data had a correlation value of only 0.68. Conclusion: The results pr- sented in the paper indicates that our DCAE can remove MAs with higher intensity where the existing methods fails. Significance: Proposed DCAE model can be used to recover a significant amount of otherwise discarded EDA data.

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.

Objective: Current inertial-based models were mostly limited to gait assessment of straight walking, which may not be efficient for detecting subtle gait disorders at an early stage of Parkinson’s disease (PD). As PD patients exhibit more severe gait impairments during turns even before the appearance of gait disorders, gait characteristics during turning can provide promise in the identification of early-stage PD. Methods: We proposed a novel spatiotemporal gait model using inertial measurement units that can assess gait performance in both straight walking and turning. Ten healthy young, ten healthy elderly subjects and ten early-stage PD patients were enrolled in the validation experiment. All participants performed a 7-meter walk test consisting of a straight walking path and turns at a self-selected speed. Spatiotemporal gait parameters from the proposed model were compared with the Vicon motion capture system. Results: A strong correlation of all spatiotemporal parameters (Pearson’s R between 0.82 $sim$ 0.99) between the inertial-based model and the reference was observed. Most measurement differences were within the mean $pm$1.96 standard deviation lines. The absolute bias was below 6.21 ms for all temporal gait parameters, 2.19 cm for stride length and 0.02 m/s for walking speed. We show that the proposed model does not only achieve a highly accurate and reliable spatiotemporal gait measurement but also enable the detection of significantly decreased stride length and reduced walking speed in early-stage PD patients at turns compared to the control groups. Significance: Our model offers a potential approach for early-stage PD detection.

Objective: This paper presents a force control scheme for brief isotonic holds in an isometrically contracted muscle tissue, with minimal overshoot and settling time to measure its shortening velocity, a key parameter of muscle function. Methods: A two-degree-of-freedom control configuration, formed by a feedback controller and a feedforward controller, is explored. The feedback controller is a proportional-integral controller and the feedforward controller is designed using the inverse of a control-oriented model of muscle tissue. A generalized linear model and a nonlinear model of muscle tissue are explored using input-output data and system identification techniques. The force control scheme is tested on equine airway smooth muscle and its robustness confirmed with murine flexor digitorum brevis muscle. Results: Performance and repeatability of the force control scheme as well as the number of inputs and level of supervision required from the user were assessed with a series of experiments. The force control scheme was able to fulfill the stated control objectives in most cases, including the requirements for settling time and overshoot. Conclusion: The proposed control scheme is shown to enable automation of force control for characterizing muscle mechanics with minimal user input required. Significance: This paper leverages an inversion-based feedforward controller based on a nonlinear physiological model in a system identification context that is superior to classic linear system identification. The control scheme can be used as a steppingstone for generalized control of nonlinear, viscoelastic materials.

Objective: Preterm birth is the leading cause of morbidity and mortality involving over 10% of infants. Tools for timely diagnosis of preterm birth are lacking and the underlying physiological mechanisms are unclear. The aim of the present study is to improve early assessment of pregnancy progression by combining and optimizing a large number of electrohysterography (EHG) features with a dedicated machine learning framework. Methods: A set of reported EHG features are extracted. In addition, novel cross and multichannel entropy and mutual information are employed. The optimal feature set is selected using a wrapper method according to the accuracy of the leave-one-out cross validation. An annotated database of 74 EHG recordings in women with preterm contractions was employed to test the ability of the proposed method to recognize the onset of labor and the risk of preterm birth. Difference between using the contractile segments only and the whole EHG signal was compared. Results: The proposed method produces an accuracy of 96.4% and 90.5% for labor and preterm prediction, respectively, much higher than that reported in previous studies. The best labor prediction was observed with the contraction segments and the best preterm prediction achieved with the whole EHG signal. Entropy features, particularly the newly-employed cross entropy contribute significantly to the optimal feature set for both labor and preterm prediction. Significance: Our results suggest that changes in the EHG, particularly the regularity, might manifest early in pregnancy. Single-channel and cross entropy may therefore provide relevant prognostic opportunities for pregnancy monitoring.