Search Results (521)

Background: Motion is a long-standing problem in cardiac PET/CT. An automated data-driven motion correction (DDMC) algorithm for within-reconstruction motion correction (MC) has been developed and validated in static images from [¹³N]NH₃ and ⁸²Rb PET/CT. This study aims to validate DDMC in dynamic [¹³N]NH₃ PET/CT, and to explore the added value of DDMC in the evaluation of myocardial motion. Methods: Thirty-six PET/CT studies from normal patients and forty-three scans from patients with myocardial ischemia were processed using QPET software without MC (NMC), using manual in-software MC (ISMC), and DDMC. Differences in the mean values of rest-, stress-MBF, and CFR; and differences in effect size related to the use and type of MC method were explored. Moreover, motion vectors provided by DDMC were analyzed to evaluate differences in myocardial motion between scan phases and axes, and to elucidate changes in MBF quantification in relation to the motion extent. Results: In both subgroups, repeated measures ANOVA showed that the use of MC significantly increased regional and global stress-MBF and CFR values (p < 0.05), regardless of the MC method. Paired t-test analysis demonstrated a comparable ES between MC tools, despite minor differences in Cx, RCA and global rest-MBF values. High-intensity motion (>6 mm) proved to be present almost exclusively in the Z (cranio-caudal) direction. In the same axis, motion was significantly higher during stress than rest, regardless of patients’ subgroup. Finally, the Jonckheere trend test showed a significant trend caused by motion in s-MBF values, in which lower stress-MBF values were observed in response to motion extent increments. Conclusions: DDMC is feasible to perform in [¹³N]NH₃ dynamic acquisitions and provides similar MBF/CFR values than manual ISMC. The use of DDMC reduces post-processing times and observer variability, and allows a more extensive evaluation of motion. MC is highly recommended when using QPET, as motion in the Z-axis during stress scans negatively impacts stress-MBF quantification. Full article

Tracking knee joint movement during activities of daily living can have the potential to transform the rehabilitation and functional assessment of patients. The present study evaluated the validity of a low-cost, instrumented knee brace to determine whether it was appropriate for the monitoring and quantification of human knee function during five activity-of-daily-living (ADL) tasks including walking, inclined walking, stepping, sitting, and object manipulation. A sensor platform was designed to acquire sagittal plane knee data from 13 healthy participants across five different tasks and compared to gold-standard motion analysis. The brace showed good-to-excellent validity (RMSE: 4.97–8.65°), with differences in knee joint angles and angular velocities noted during various ADLs, specifically during early and late portions of a given movement. The results for instantaneous knee joint angles and angular velocities were very similar to those of the gold-standard system (mean bias: 0.59–9.52°·s⁻¹), which may be applicable to everyday movement tasks, but may preclude analyses at a clinical level. Although the low-cost sensor platform shows promise an effective monitoring tool, it is not ready yet for a clinical application. Full article

Brain magnetic resonance imaging (MRI) is highly susceptible to motion artifacts that degrade fine structural details and undermine quantitative analysis. Conventional U-Net-based deep learning approaches for motion artifact reduction typically operate only in the image domain and are often trained on data with simplified motion patterns, thereby limiting physical plausibility and generalization. We propose Sim-DDNet, a simulation-data-based dual-domain network that combines k-space-based motion simulation with a joint image-k-space reconstruction architecture. Motion-corrupted data were generated from T2-weighted Alzheimer’s Disease Neuroimaging Initiative brain MR scans using a k-space replacement scheme with three to five random rotational and translational events per volume, yielding 69,283 paired samples (49,852/6969/12,462 for training/validation/testing). Sim-DDNet integrates a real-valued U-Net-like image branch and a complex-valued k-space branch using cross attention, FiLM-based feature modulation, soft data consistency, and composite loss comprising L1, structural similarity index measure (SSIM), perceptual, and k-space-weighted terms. On the independent test set, Sim-DDNet achieved a peak signal-to-noise ratio of 31.05 dB, SSIM of 0.85, and gradient magnitude similarity deviation of 0.077, consistently outperforming U-Net and U-Net++ across all three metrics while producing less blurring, fewer residual ghost/streak artifacts, and reduced hallucination of non-existent structures. These results indicate that dual-domain, data-consistency-aware learning, which explicitly exploits k-space information, is a promising approach for physically plausible motion artifact correction in brain MRI. Full article

This study investigates the mechanical characteristics of a multi-stage centrifugal pump rotor through fluid–structure interaction (FSI) analysis. A two-stage centrifugal pump equipped with back vanes on the trailing impeller is selected as the research object. Numerical simulations are performed based on the continuity equation and Reynolds-averaged Navier–Stokes (RANS) equations, with experimental data utilized to validate the numerical model’s accuracy. The internal flow field mechanisms are analyzed, and the effectiveness of two axial force calculation methods—formula-based and numerical simulation-based—for the rotor system is comprehensively evaluated. Employing an FSI-based modal analysis approach, the governing differential equations of motion are established and decoupled via Laplace transformation to introduce modal coordinates. Modal analysis of the pump rotor system is conducted, revealing the first six natural frequencies and corresponding vibration modes, along with critical speed calculations. The findings demonstrate that when the flow field near the back vanes exhibits complex characteristics, the formula-based axial force calculation shows reduced accuracy. In contrast, without back vanes, the hydraulic motion in the impeller rear chamber remains relatively stable, resulting in higher accuracy for formula-based axial force predictions. The calculation error between the two conditions (with/without back vanes) reaches 27.6%. Based on vibration mode characteristics and critical speed analysis, the pump is confirmed to operate within a safe region. The rotor system exhibits two similar adjacent natural frequencies differing by less than 1 Hz, with perpendicular vibration mode directions. Additionally, rotational speed fluctuations in the rotor system induce alternating critical speed phenomena when operating in this region. This study establishes a coupled analysis framework of “flow field stability–axial force calculation accuracy–rotor dynamic response”, quantifies the axial force calculation error patterns under different flow field conditions of a special pump type, supplements the basic data on axial force calculation accuracy for complex structure centrifugal pumps, and provides new theoretical insights and reference benchmarks for the study of hydraulic–mechanical coupling characteristics of similar fluid machinery. In engineering applications, it avoids over-design or under-design of thrust bearings to reduce manufacturing costs and operational risks. The revealed rotor modal characteristics, critical speed distribution, and frequency alternation phenomena can provide direct technical support for the optimization of operating parameters, vibration control, and structural improvement of pump units in industrial scenarios, thereby reducing rotor imbalance, bearing wear, and other failures. Full article

Hand exoskeletons are increasingly used to support post-stroke reach-to-grasp, yet most intention-detection strategies trigger assistance from local hand events without considering the synergy between proximal arm transport and distal hand shaping. We evaluated whether proximal arm kinematics, alone or fused with EMG, can predict flexor and extensor digitorum activity for synergy-aligned hand assistance. We trained nine models per participant: linear regression (LINEAR), feedforward neural network (NONLINEAR), and LSTM, each under EMG-only, kinematics-only (KIN), and EMG+KIN inputs. Performance was assessed by RMSE on test trials and by a synergy-retention analysis, comparing synergy weights from original EMG versus a hybrid EMG in which extensor and flexor digitorum measure signals were replaced by model predictions. Results have shown that kinematic information can predict muscle activity even with a simple linear model (average RMSE around 30% of signal amplitude peak during go-to-grasp contractions), and synergy analysis indicated high cosine similarity between original and hybrid synergy weights (on average 0.87 for the LINEAR model). Furthermore, the LINEAR model with kinematics input has been tested in a real-time go-to-grasp motion, developing a high-level control strategy for a hand exoskeleton, to better simulate post-stroke rehabilitation scenarios. These results suggest the intrinsic synergistic motion of go-to-grasp actions, offering a practical path, in hand rehabilitation contexts, for timing hand assistance in synergy with arm transport and with minimal setup burden. Full article

Accurate monitoring of fish movement is essential for understanding behavioral patterns and group dynamics in aquaculture systems. Underwater scenes—characterized by dense populations, frequent occlusions, non-rigid body motion, and visually similar appearances—present substantial challenges for conventional multi-object tracking methods. We propose an improved CenterTrack-based framework tailored for multi-fish tracking in such environments. The framework integrates three complementary components: a multi-branch feature extractor that enhances discrimination among visually similar individuals, occlusion-aware output heads that estimate visibility states, and a three-stage cascade association module that improves trajectory continuity under abrupt motion and occlusions. To support systematic evaluation, we introduce a self-built dataset named Multi-Fish 25 (MF25), continuous video sequences of 75 individually annotated fish recorded in aquaculture tanks. The experimental results on MF25 show that the proposed method achieves an IDF1 of 82.5%, MOTA of 85.8%, and IDP of 84.7%. Although this study focuses on tracking performance rather than biological analysis, the produced high-quality trajectories form a solid basis for subsequent behavioral studies. The framework’s modular design and computational efficiency make it suitable for practical, online tracking in aquaculture scenarios. Full article

Trajectory clustering is of great significance for identifying behavioral patterns and vessel types of non-cooperative ships. However, existing trajectory clustering methods suffer from limitations in extracting cross-spatiotemporal scale features and modeling the coupling relationship between positional and motion features, which restricts clustering performance. To address this, this study proposes a deep ship trajectory clustering method based on feature embedding representation learning (ERL-DTC). The method designs a Temporal Attention-based Multi-scale feature Aggregation Network (TA-MAN) to achieve dynamic fusion of trajectory features from micro to macro scales. A Dual-feature Self-attention Fusion Encoder (DualSFE) is employed to decouple and jointly represent the spatiotemporal position and motion features of trajectories. A two-stage optimization strategy of “pre-training and joint training” is adopted, combining contrastive loss and clustering loss to jointly constrain the embedding representation learning, ensuring it preserves trajectory similarity relationships while being adapted to the clustering task. Experiments on a public vessel trajectory dataset show that for a four-class task (K = 4), ERL-DTC improves ACC by approximately 14.1% compared to the current best deep clustering method, with NMI and ARI increasing by about 28.9% and 30.2%, respectively. It achieves the highest Silhouette Coefficient (SC) and the lowest Davies-Bouldin Index (DBI), indicating a tighter and more clearly separated cluster structure. Furthermore, its inference efficiency is improved by two orders of magnitude compared to traditional point-matching-based methods, without significantly increasing runtime due to model complexity. Ablation studies and parameter sensitivity analysis further validate the necessity of each module design and the rationality of hyperparameter settings. This research provides an efficient and robust solution for feature learning and clustering of vessel trajectories across spatiotemporal scales. Full article

Estimation of remaining useful life (RUL) of roller bearings is a prevalent problem for predictive maintenance in manufacturing. However, roller bearings are subject to a variety of factors during their operation. As a result, we deal with a slow nonlinear degradation process, which is long-range dependent, self-similar and has non-Gaussian characteristics. Proper data pre-processing enables us to use Pareto’s probability density function (PDF), Generalized Pareto motion (GPm) and its fractional-order extension (fGPm) as the degradation predictive model. Estimation of the Hurst exponent shows that this model has a long-range correlation and self-similarity. Through the analysis of the uncertainty of the end point of the bearing’s RUL and the prediction process, not only did it verify the high adaptability of fGPm in simulating complex degradation processes but also the criteria for judging self-similarity, and LRD characteristics were established. The case study mainly proves the validity of the theory, providing an effective analytical tool for a deeper understanding of the degradation mechanism. Full article

This study investigates the thermal transport behavior of a time-dependent viscoelastic nanofluid moving over a widening cylindrical surface. A steady magnetic influence is introduced along the transverse direction due to photonic heating, thermal sources, or absorbers, and modified Fourier conduction. A mixture of $CoF{e}_2{O}_4$ and $F{e}_3{O}_4$ nanoparticles are uniformly distributed in ethylene glycol to form a hybrid nanofluid. Using a suitable similarity transformation, the governing equations were reformulated into a set of nonlinear ordinary differential equations. The collocation method (CM) is employed as a discretization approach, combined with feedforward neural networks (FNNs) to enhance computational accuracy. Unsteady patterns in both fluid motion and heat distribution were identified, with the localized Nusselt coefficient influenced by relevant scaling parameters. Results are illustrated through plots and structured data formats for various nanoparticle geometries, including spherical, brick, and platelet forms. The analysis revealed that spherical nanoparticles enhance heat transfer by up to 18–22% compared with brick and platelet forms under strong unsteadiness and relaxation effects. As temporal fluctuation indicators intensify, the thermal distribution increases; however, increasing the relaxation coefficient in the heat response leads to diminished energy levels. Full article

This study evaluates the morphological performance of the CardioBAN wearable electrocardiogram (ECG) device by comparing its beat-level waveform accuracy against a clinically certified reference system (GE Vivid E9). A cycle-by-cycle Dynamic Time Warping (DTW) analysis was employed to assess beat-level waveform similarity between both devices in 17 healthy participants under controlled conditions. Each cardiac cycle from CardioBAN was aligned to its reference counterpart, enabling a fine-grained comparison of waveform shape. The resulting DTW distances (mean 0.493 ± 0.166) demonstrated overall high morphological agreement, with lower values occurring in recordings with stable beat morphology and higher values primarily reflecting normal variability related to minor motion artifacts or electrode–skin impedance fluctuations. A complementary Bland–Altman analysis of point-wise amplitude differences after DTW alignment showed minimal bias (0.079) and narrow limits of agreement (−0.897–1.055), confirming strong amplitude concordance between systems. These findings indicate that the CardioBAN wearable reliably reproduces key ECG morphological features under controlled, short-term recording conditions. Further studies encompassing ambulatory environments and clinical populations are needed to evaluate its suitability for real-world and pathological scenarios. Full article

Given the undoubtable similarities between the dynamic behavior of the vehicular traffic flow in terms of its response to boundary condition alterations dictated in the form of obstacles, and the specific case of supersonic compressible fluid flow fields, the current manuscript addresses developing a target trajectory for the overtaking maneuver of autonomous vehicles. The path-planning is pursued in analogy to the governing principles of the supersonic compressible fluid flow fields, with the specific definition of a physically meaningful dimensionless group, namely the Traffic Mach number (M_T), which grants the initial access point to the said set of fundamental equations. This practical application is a follow-up to the primarily established proof-of-concept level introduction and analysis of the more general case of collision avoidance for autonomously driven vehicles in accordance with the supersonic compressible fluid flow field, where the Traffic Mach number was first introduced. The proposed trajectory is then taken to the next block of the investigation, namely the tracking and control aspects of the maneuvering vehicle’s dynamics. The path tracking controller is designed based on sliding mode control technique and the algorithm is applied on a 7-DOF simulation model, used for validation and discussion of results. The proposed method is shown to be suitable for overtaking maneuvers of autonomous vehicles, whilst meeting the criteria for a relative velocity from the constant-velocity vehicle ahead of the road in the supersonic regime based on the defined Traffic Mach number. The results are then presented, first, in the scope of the aerodynamics field configuration and their verifications, followed by the vehicle dynamics remarks showing the practicality of the proposed method in terms of vehicle motion. It is observed that the distance corresponding to the delayed maneuver maximizes at highest velocities of the ego vehicle, consistent with the highest M_T values, yet in all simulated cases, the control system of the vehicle model was capable of performing the maneuver based on the assigned trajectories through the present model. Full article

Movement artifact direction and magnitude are critical parameters in noise detection and image analysis, especially for single-frame images where temporal information is unavailable. This paper introduces the Movement Artifact Direction Estimation (MADE) algorithm, a signal processing-based approach that performs 3D geometric analysis to estimate both the direction (in degrees) and weighted quantity (in pixels) of movement artifacts. Motivated by computational challenges in medical image quality assessment systems such as LUIAS, this work investigates directional multiplicative noise characterization using controlled experimental conditions with optical camera imaging. The MADE algorithm operates on multi-directional quantification outputs from a preprocessing pipeline—MAPE, ROPE, and MAQ. The methodology is designed for computational efficiency and instantaneous processing, providing interpretable outputs. Experimental results using precision-controlled apparatus demonstrate robust estimation of movement artifact direction and magnitude across a range of image shapes and velocities, with principal outputs aligning closely to ground truth parameters. The proposed MADE algorithm offers a methodological proof of concept for movement artifact analysis in single-frame images, emphasizing both directional accuracy and quantitative assessment under controlled imaging conditions. Full article

To achieve early mobilization of patients with unstable pelvic fractures, the osteosynthesis methods used must withstand the loads in the pelvis during everyday movements. There is currently no predictive tool to assess how suitable these methods are for this purpose. The development of such a tool requires an understanding of the effects of joint and muscle loads on the structural behavior of the pelvis during movement. To further this cause, the stress–strain state of the pelvis during a sit-to-stand transfer of a healthy adult male was analyzed. Muscle and joint reaction forces during the motion were predicted using a rigid-body musculoskeletal model. These loads were then utilized in the first-ever dynamic structural analysis of the pelvis during a sit-to-stand transfer using the finite element method. Several similarities in stress distributions during sit-to-stand transfer, gait, and standing were identified by comparing the finite element analysis results with literature. The common areas of increased stress between the three motions are the acetabular notch, the superior edge of the obturator foramen, the attachments of the gluteus maximus on the ilium, and the lesser sciatic notch. The results also provide important insights into global behavior of the pelvic ring and indicate the locations of concentrated stress during sit-to-stand transfer. Full article

Lagrangian Coherent Structures (LCSs) in the mesoscale circulation of the Western Equatorial Atlantic (WEA), a region governed by the North Brazil Current (NBC) and its retroflection, are analyzed. Observations from 63 surface drifters deployed between 2018 and 2019 were combined with ocean analysis/forecast fields. The Finite-Time Lyapunov Exponent (FTLE) was computed using 15- and 90-day integrations to identify transport barriers and persistent structures. FTLE ridges showed strong seasonal correspondence with drifter trajectories, with 34–74% of drifter positions lying within 0.25° of attracting or repelling LCSs. Characteristic FTLE magnitudes reached ~0.3 d⁻¹, implying particle separation e-folding times of approximately 3.3 days. Spatial agreement between drifter-derived and model-based FTLE fields exhibited similar variability across seasons, with the highest correspondence during periods of intensified frontal activity. These results indicate that a substantial portion of the observed drifter motion follows or remains close to FTLE-defined pathways, supporting the robustness of the Lagrangian structures identified in the WEA. Overall, the study provides the first quantitative LCS-based characterization of mesoscale transport in this region, revealing recurrent eddies, instability zones, and flow boundaries associated with the NBC system and its interaction with the North Equatorial Countercurrent. Full article

Mild traumatic brain injury (mTBI) frequently causes subtle brain changes that are difficult to detect with conventional diagnostic approaches. In this exploratory pilot study, we combined tri-exponential intravoxel incoherent motion (IVIM) and pseudocontinuous arterial spin labeling (pCASL) MRI with Multimodal Canonical Correlation Analysis and joint independent component analysis (mCCA+jICA) to identify imaging signatures distinguishing mTBI patients from healthy controls (HCs) and their associations with clinical function. Cerebral blood flow (CBF) and IVIM-derived metrics were extracted from 90 brain regions in 19 mTBI patients and 24 HCs, and multivariate components were identified using mCCA+jICA. Two independent components (IC2, IC15) showed group differences at the uncorrected level (p < 0.05) but did not survive false discovery rate (FDR) correction. IC2 correlated positively with CBF and perfusion fraction (F_p) and negatively with tissue diffusion fraction (F_s), consistent with reduced vascular integrity in mTBI, while IC15 showed similar trends. One component correlated with Glasgow Outcome Scale–Extended (GOS-E) scores (uncorrected p = 0.046). Although this study is preliminary and limited by a small sample size, our findings suggest that mTBI is associated with perfusion and microstructural alterations, particularly in subcortical regions, and demonstrate the potential value of combining IVIM and ASL within multivariate fusion frameworks to reveal patterns not captured by single-modality approaches. Full article