Feasibility of transabdominal electrohysterography for analysis of uterine activity in nonpregnant women

Purpose: Uterine activity plays a key role in reproduction, and altered patterns of uterine contractility have been associated with important physiopathological conditions, such as subfertility, dysmenorrhea, and endometriosis. However, there is currently no method to objectively quantify uterine contractility outside pregnancy without interfering with the spontaneous contraction pattern. Transabdominal electrohysterography has great potential as a clinical tool to characterize noninvasively uterine activity, but results of this technique in nonpregnant women are poorly documented. The purpose of this study is to investigate the feasibility of transabdominal electrohysterography in nonpregnant women. Methods: Longitudinal measurements were performed on 22 healthy women in 4 representative phases of the menstrual cycle. Twelve electrohysterogram-based indicators previously validated in pregnancy have been estimated and compared in the 4 phases of the cycle. Using the Tukey honest significance test, significant differences were defined for P values below .05. Results: Half of the selected electrohysterogram-based indicators showed significant differences between menses and at least 1 of the other 3 phases, that is the luteal phase. Conclusion: Our results suggest transabdominal electrohysterography to be feasible for analysis of uterine activity in nonpregnant women. Due to the lack of a golden standard, this feasibility study is indirectly validated based on physiological observations. However, these promising results motivate further research aiming at evaluating electrohysterography as a method to improve understanding and management of dysfunctions (possibly) related to altered uterine contractility, such as infertility, endometriosis, and dysmenorrhea

Exploiting flow dynamics for super-resolution in contrast-enhanced ultrasound

Ultrasound localization microscopy offers new radiation-free diagnostic tools for vascular imaging deep within the tissue. Sequential localization of echoes returned from inert microbubbles with low-concentration within the bloodstream reveal the vasculature with capillary resolution. Despite its high spatial resolution, low microbubble concentrations dictate the acquisition of tens of thousands of images, over the course of several seconds to tens of seconds, to produce a single super-resolved image. %since each echo is required to be well separated from adjacent microbubbles. Such long acquisition times and stringent constraints on microbubble concentration are undesirable in many clinical scenarios. To address these restrictions, sparsity-based approaches have recently been developed. These methods reduce the total acquisition time dramatically, while maintaining good spatial resolution in settings with considerable microbubble overlap. %Yet, non of the reported methods exploit the fact that microbubbles actually flow within the bloodstream. % to improve recovery. Here, we further improve sparsity-based super-resolution ultrasound imaging by exploiting the inherent flow of microbubbles and utilize their motion kinematics. While doing so, we also provide quantitative measurements of microbubble velocities. Our method relies on simultaneous tracking and super-localization of individual microbubbles in a frame-by-frame manner, and as such, may be suitable for real-time implementation. We demonstrate the effectiveness of the proposed approach on both simulations and {\it in-vivo} contrast enhanced human prostate scans, acquired with a clinically approved scanner.Comment: 11 pages, 9 figure

Local Density Random Walk Model Preprocessing for Local Shear Wave Viscoelastographic Estimation

Shear wave (SW) elastography is an ultrasound technique that provides quantitative tissue elasticity and viscosity measurements by imaging the tissue response to an applied excitation. In particular, the phase difference method allows for local viscoelasticity estimation through the dispersion curve using the phases from acquired signals at two laterally-spaced pixels in SW elastography. However, this method is sensitive to measurement noise in the SW particle displacement signals. To address this, we propose adopting the local density random walk model to fit the measured noisy SW particle displacement signals. Local elasticity and viscosity are then estimated from the phase difference of the fit signals at two neighboring pixels. Elasticity and viscosity were estimated in six customized phantoms by the phase difference method with and without the proposed preprocessing by model fitting. A decreased interquartile range was obtained by model fitting in both the elasticity and viscosity estimates across all phantoms. In addition, the Levene test indicated a significant difference (p-value < 0.05) between the original and proposed methods in the variance of the estimated elasticity and viscosity. These findings highlight the reduced variability observed in pixel-based viscoelastic estimations with the proposed method, indicating its enhanced robustness

Adaptive multilevel thresholding for SVD-based clutter filtering in ultrafast transthoracic coronary flow imaging

Background and Objective:The integration of ultrafast Doppler imaging with singular value decomposition clutter filtering has demonstrated notable enhancements in flow measurement and Doppler sensitivity, surpassing conventional Doppler techniques. However, in the context of transthoracic coronary flow imaging, additional challenges arise due to factors such as the utilization of unfocused diverging waves, constraints in spatial and temporal resolution for achieving deep penetration, and rapid tissue motion. These challenges pose difficulties for ultrafast Doppler imaging and singular value decomposition in determining optimal tissue-blood (TB) and blood-noise (BN) thresholds, thereby limiting their ability to deliver high-contrast Doppler images.Methods:This study introduces a novel local blood subspace detection method that utilizes multilevel thresholding by the valley-emphasized Otsu’s method to estimate the TB and BN thresholds on a pixel-based level, operating under the assumption that the magnitude of the spatial singular vector curve of each pixel resembles the shape of a trimodal Gaussian. Upon obtaining the local TB and BN thresholds, a weighted mask (WM) is generated to assess the blood content in each pixel. To enhance the computational efficiency of this pixel-based algorithm, a dedicated tree-structure -means clustering approach, further enhanced by noise rejection (NR) at each singular vector order, is proposed to group pixels with similar spatial singular vector curves, subsequently applying local thresholding (LT) on a cluster-based (CB) level.Results:The effectiveness of the proposed method was evaluated using an ex-vivo setup featuring a Langendorff swine heart. Comparative analysis with power Doppler images filtered using the conventional global thresholding method, which uniformly applies TB and BN thresholds to all pixels, revealed noteworthy enhancements. Specifically, our proposed CBLT+NR+WM approach demonstrated an average 10.8-dB and 11.2-dB increase in Contrast-to-Noise ratio and Contrast in suppressing the tissue signal, paralleled by an average 5-dB (Contrast-to-Noise ratio) and 9-dB (Contrast) increase in suppressing the noise signal.Conclusions:These results clearly indicate the capability of our method to attenuate residual tissue and noise signals compared to the global thresholding method, suggesting its promising utility in challenging transthoracic settings for coronary flow measurement

Feasibility of 3D Ultrasound Strain Analysis in the Non-Pregnant Uterus

Uterine mechanical behavior in the form of uterine contraction plays a key role in reproduction. Irregular patterns of uterine contractility associate with dysfunctions of the uterus. Current quantitative assessments of uterine motion primarily rely on two-dimensional transvaginal ultrasound (2D TVUS). However, 2D TVUS offers a limited view of the uterus, and the quantification is further affected by out-of-plane (OOP) motion problems. In this study, our objective is to compare the efficacy of three-dimensional (3D) TVUS against 2D TVUS in detecting uterine contraction propagation. To achieve this, we utilized 3D speckle tracking and radial strain analysis to track uterine movements and deformation. Following this, we proposed a dedicated propagation detection method to quantify and visualize the 3D propagation patterns. Both 2D and 3D ultrasound scans were performed on a healthy volunteer during the late follicular phase of the menstrual cycle and a patient undergoing in-vitro fertilization (IVF). Our preliminary findings indicate that 3D TVUS detects more complete and consistent propagation compared to 2D TVUS, suggesting the potential utility of 3D TVUS for quantitatively measuring uterine contraction and enhancing our understanding of its underlying mechanisms

Safety of contrast-enhanced ultrasound using microbubbles in human pregnancy:A scoping review

Introduction Successful placentation is crucial for fetal development and maintaining a healthy pregnancy. Placental insufficiency can cause a variety of obstetric complications. Despite the many efforts to enhance diagnosing placental insufficiency, no imaging technique has proven satisfactory. A promising imaging technique is contrast-enhanced ultrasound (CEUS) using microbubbles which has proven capable of (micro)vascular imaging. Its use for placental vascularization assessment in human pregnancies remains constrained by limited evidence and safety concerns. This scoping review aims to demonstrate the safety of CEUS used in human pregnancy in the published literature to date. Material and Methods A systematic search using PubMed, Medline, Embase, and Cochrane databases was performed. All studies where contrast-enhanced ultrasound was used in pregnant humans were included. Studies, where there was a planned termination of pregnancy, were excluded. To assess the safety of CEUS during pregnancy, relevant outcomes were divided into the following 3 categories; fetal outcome, maternal outcome, and pregnancy and neonatal outcomes. Results A total of 13 articles were included, in which 256 women underwent CEUS during pregnancy. No clinically significant maternal or fetal adverse events or negative pregnancy or neonatal outcomes associated with CEUS were described. Conclusion Based on our findings, we consider expanding the knowledge of this promising diagnostic technique in future larger clinical studies to be safe and relevant.</p

Longitudinally Tracking Maternal Autonomic Modulation During Normal Pregnancy With Comprehensive Heart Rate Variability Analyses

Changes in the maternal autonomic nervous system are essential in facilitating the physiological changes that pregnancy necessitates. Insufficient autonomic adaptation is linked to complications such as hypertensive diseases of pregnancy. Consequently, tracking autonomic modulation during progressing pregnancy could allow for the early detection of emerging deteriorations in maternal health. Autonomic modulation can be longitudinally and unobtrusively monitored by assessing heart rate variability (HRV). Yet, changes in maternal HRV (mHRV) throughout pregnancy remain poorly understood. In previous studies, mHRV is typically assessed only once per trimester with standard HRV features. However, since gestational changes are complex and dynamic, assessing mHRV comprehensively and more frequently may better showcase the changing autonomic modulation over pregnancy. Subsequently, we longitudinally (median sessions = 8) assess mHRV in 29 healthy pregnancies with features that assess sympathetic and parasympathetic activity, as well as heart rate (HR) complexity, HR responsiveness and HR fragmentation. We find that vagal activity, HR complexity, HR responsiveness, and HR fragmentation significantly decrease. Their associated effect sizes are small, suggesting that the increasing demands of advancing gestation are well tolerated. Furthermore, we find a notable change in autonomic activity during the transition from the second to third trimester, highlighting the dynamic nature of changes in pregnancy. Lastly, while we saw the expected rise in mean HR with gestational age, we also observed increased autonomic deceleration activity, seemingly to counter this rising mean HR. These results are an important step towards gaining insights into gestational physiology as well as tracking maternal health via mHRV

Adaptive higher-order singular value decomposition clutter filter for ultrafast Doppler imaging of coronary flow under non-negligible tissue motion

Background and Objective: With the development of advanced clutter-filtering techniques by singular value decomposition (SVD) and leveraging favorable acquisition settings such as open-chest imaging by a linear high-frequency probe and plane waves, several studies have shown the feasibility of cardiac flow measurements during the entire cardiac cycle, ranging from coronary flow to myocardial perfusion. When applying these techniques in a routine clinical setting, using transthoracic ultrasound imaging, new challenges emerge. Firstly, a smaller aperture is needed that can fit between ribs. Consequently, diverging waves are employed instead of plane waves to achieve an adequate field of view. Secondly, to ensure imaging at a larger depth, the maximum pulse repetition frequency has to be reduced. Lastly, in comparison to the open-chest scenario, tissue motion induced by the heartbeat is significantly stronger. The latter complicates substantially the distinction between clutter and blood signals. Methods: This study investigates a strategy to overcome these challenges by diverging wave imaging with an optimal number of tilt angles, in combination with dedicated clutter-filtering techniques. In particular, a novel, adaptive, higher-order SVD (HOSVD) clutter filter, which utilizes spatial, temporal, and angular information of the received ultrasound signals, is proposed to enhance clutter and blood separation. Results: When non-negligible tissue motion is present, using fewer tilt angles not only reduces the decorrelation between the received waveforms but also allows for collecting more temporal samples at a given ensemble duration, contributing to improved Doppler performance. The addition of a third angular dimension enables the application of HOSVD, providing greater flexibility in selecting blood separation thresholds from a 3-D tensor. This differs from the conventional threshold selection method in a 2-D spatiotemporal space using SVD. Exhaustive threshold search has shown a significant improvement in Contrast and Contrast-to-Noise ratio for Power Doppler images filtered with HOSVD compared to the SVD-based clutter filter. Conclusion: With the improved settings, the obtained Power Doppler images show the feasibility of measuring coronary flow under the influence of non-negligible tissue motion in both in vitro and ex vivo.</p

Deep Proximal Learning for High-Resolution Plane Wave Compounding

Plane Wave imaging enables many applications that require high frame rates, including localisation microscopy, shear wave elastography, and ultra-sensitive Doppler. To alleviate the degradation of image quality with respect to conventional focused acquisition, typically, multiple acquisitions from distinctly steered plane waves are coherently (i.e. after time-of-flight correction) compounded into a single image. This poses a trade-off between image quality and achievable frame-rate. To that end, we propose a new deep learning approach, derived by formulating plane wave compounding as a linear inverse problem, that attains high resolution, high-contrast images from just 3 plane wave transmissions. Our solution unfolds the iterations of a proximal gradient descent algorithm as a deep network, thereby directly exploiting the physics-based generative acquisition model into the neural network design. We train our network in a greedy manner, i.e. layer-by-layer, using a combination of pixel, temporal, and distribution (adversarial) losses to achieve both perceptual fidelity and data consistency. Through the strong model-based inductive bias, the proposed architecture outperforms several standard benchmark architectures in terms of image quality, with a low computational and memory footprint

A spatiotemporal deep-learning model for force estimation from surface electromyography

The noninvasive estimation of muscle force from muscular activations is of particular interest for different clinical applications, such as prosthesis control and neurorehabilitation. Surface electromyography (sEMG) enables to measure the electrical activity of a muscle and has been shown in previous works to be associated with force production. However, a general model mapping sEMG to force in real-life situations is yet to be established, hampering the clinical translation of sEMG-based force estimation methods. In this study, we aim at estimating force from sEMG during dynamically changing force-levels achieved with isometric muscular contractions. 64 High Density sEMG (HD-sEMG) channels were acquired from the biceps brachii of 50 healthy subjects in order to record both spatial and temporal distribution of the muscle's electrical activity. The participants performed isometric contractions ranging between 0 and 80% of their maximum voluntary contraction. A deep-learning strategy was adopted in this work. The normalized bipolar HD-sEMG signals were used as input to an adapted three-dimensional version of the temporal convolution network (TCN) which jointly extracts spatiotemporal features. The obtained result supports that force can be estimated with a normalized root mean squared error of 29.2 ± 13.1% and an R2 value of 64.9 ± 25.7%. This shows that deep-learning holds promise for noninvasive estimation of varying force from sEMG signals.</p