Search Results (1,133)

The objective of this study is to measure T2* relaxation time in the triangular fibrocartilage (TFC) disc in asymptomatic volunteers and evaluate its variation with factors such as age, hand dominance, sex, and ulnar variance, using a dedicated MRI sequence. The MRI protocol included anatomical sequences as well as a 3D ultra-short echo time (UTE)-T2* mapping sequence. A linear regression model was used to assess the potential influence of age, sex, and hand dominance on T2* values measured in the TFC disc and to evaluate the correlation between T2* values and ulnar variance. T2* relaxation time was positively correlated with age. The higher T2* relaxation times may reflect early degeneration of the fibrocartilage microstructure, which is associated with both biomechanical factors and the aging process. However, T2* was not significantly influenced by sex or hand dominance, nor was it correlated with ulnar variance (this later being limited by the fact that none of our subject had positive ulnar variance). In conclusion, UTE-T2* is a promising MRI technique showing positive correlation with age in the TFC of healthy subjects. These findings are a first step to establish normative T2* values and will help interpreting deviations observed in patient with suspected pathology in future studies. Full article

Over the last ten years, transcatheter tricuspid valve interventions (TTVIs) have emerged as effective options for symptomatic patients with moderate-to-severe tricuspid regurgitation (TR) who are at prohibitive surgical risk. Successful application of these therapies depends on a patient-tailored, multimodal imaging workflow. Transthoracic and transesophageal echocardiography remain the first-line diagnostic tools, rapidly stratifying TR severity, mechanism, and right ventricular function, and identifying cases requiring further evaluation. Cardiac computed tomography (CT) then provides anatomical detail—quantifying tricuspid annular dimension, leaflet tethering, coronary artery course, and venous access anatomy—to refine candidacy and simulate optimal device sizing and implantation angles. In patients with suboptimal echocardiographic windows or equivocal functional data, cardiovascular magnetic resonance (CMR) offers gold-standard quantification of RV volumes, ejection fraction, regurgitant volume, and tissue characterization to detect fibrosis. Integration of echo-derived parameters, CT anatomical notes, and CMR functional assessment enables the heart team to better select patients, plan procedures, and determine the optimal timing, thereby maximizing procedural success and minimizing complications. This review describes the current strengths, limitations, and future directions of multimodality imaging in comprehensive evaluations of TTVI candidates. Full article

Time series prediction of aerodynamic noise is critical for oscillatory instabilities analyses in fluid systems. Due to the significant dynamical and non-stationary characteristics of aerodynamic noise, it is challenging to precisely predict its temporal behavior. Here, we propose a method combining variational mode decomposition (VMD) and echo state network (ESN) to accurately predict the time series of aerodynamic noise induced by flow around a cylinder. VMD adaptively decomposes the noise signal into multiple modes through a constrained variational optimization framework, effectively separating distinct frequency-scale features between vortex shedding and turbulent fluctuations. ESN then employs a randomly initialized reservoir to map each mode into a high-dimensional dynamical system, and learns their temporal evolution by leveraging the reservoir’s memory of past states to predict their future values. Aerodynamic noise data from cylinder flow at a Reynolds number of 90,000 is generated by numerical simulation and used for model validation. With a rolling prediction strategy, this VMD-ESN method achieves accurate prediction within 150 time steps with a root-mean-square-error of only 3.32 Pa, substantially reducing computational costs compared to conventional approaches. This work enables effective aerodynamic noise prediction and is valuable in fluid dynamics, aeroacoustics, and related areas. Full article

Objective: The aim of this study was to describe the presenting characteristics and outcomes of neonates with respiratory failure referred for extracorporeal membrane oxygenation (ECMO) support, compare those who received ECMO support (ECMO group) to those who did not (non-ECMO group), and evaluate the predictive variables requiring ECMO support. Methods: All neonates (<15 days) with respiratory failure (without congenital diaphragmatic hernia or congenital heart disease) referred to our regional ECMO center from 2014 to 2023 were included in this retrospective study. Patient demographics, birth history, and clinical and outcome variables were analyzed. Oxygenation indices and vasoactive–inotropic scores obtained at PICU arrival and four hours after arrival were compared between the two groups using ROC analysis, with ECMO initiation as an outcome variable. Youden’s index was used for optimal threshold values. Chi-square, Mann–Whitney U, and binary logistic regression were used for comparative analyses. Results: Out of the 147 neonates, 96 (65%) required ECMO support. The two groups significantly differed in the prevalence of pulmonary hypertension (pHTN; systemic or suprasystemic pulmonary pressures), lactate level, and oxygenation indices. Mortality was not different between the two groups. Presence of oxygen saturation index (OSI) ≥ 10 had a sensitivity 96.8% in predicting the need for ECMO support. On regression analysis, OSI and pHTN were independent predictors of ECMO support. Conclusions: Oxygenation indices and echo findings predict the need for ECMO support in neonatal hypoxemic respiratory failure. These findings help non-ECMO centers make appropriate and timely transfers of neonates with respiratory failure to ECMO centers. Full article

We discuss different quench protocols for Ising and XY spin chains in a transverse magnetic field. With a sudden local magnetic field quench as a starting point, we generalize our approach to a large class of local non-sudden quenches. Using finite time path field theory (FTPFT) perturbative methods, we show that the difference between the sudden quench and a class of quenches with non-sudden switching on the perturbation vanishes exponentially with time, apart from non-substantial modifications that are systematically accounted for. As the consequence of causality and analytic properties of functions describing the discussed class of quenches, this is true at any order of perturbation expansion and thus for the resummed perturbation series. The only requirements on functions describing the perturbation strength switched on at a finite time $t=0$ are as follows: (1) their Fourier transform $f\left(p\right)$ is a function that is analytic everywhere in the lower complex semiplane, except at the simple pole at $p=0$ and possibly others with $\Im \left(p\right)<0$; and (2) $f\left(p\right)/p$ converges to zero at infinity in the lower complex semiplane. A prototypical function of this class is $tanh\left(\eta t\right)$, to which the perturbation strength is proportional after the switching at time $t=0$. In the limit of large $\eta$, such a perturbation approaches the case of a sudden quench. It is shown that, because of this new type of universal behavior of Loschmidt echo (LE) that emerges in an exponentially short time scale, our previous results for the sudden local magnetic field quench of Ising and XY chains, obtained by the resummation of the perturbative expansion, extend in the long-time limit to all non-sudden quench protocols in this class, with non-substantial modifications systematically taken into account. We also show that analogous universal behavior exists in disorder quenches, and ultimately global ones. LE is directly connected to the work probability distribution, and the described universal behavior is therefore appropriate in potential concepts of quantum technology related to spin chains. Full article

Phase images from gradient echo MRI sequences reflect underlying magnetic field inhomogeneities but are inherently wrapped within the range of −π to π, requiring phase unwrapping to recover the true phase. In this study, we present DIP-UP (Deep Image Prior for Unwrapping Phase), a framework designed to refine two pre-trained deep learning models for phase unwrapping: PHUnet3D and PhaseNet3D. We compared the DIP-refined models to their original versions, as well as to the conventional PRELUDE algorithm from FSL, using both simulated and in vivo brain data. Results demonstrate that DIP refinement improves unwrapping accuracy (achieving ~99%) and robustness to noise, surpassing the original networks and offering comparable performance to PRELUDE while being over three times faster. This framework shows strong potential for enhancing downstream MRI phase-based analyses. Full article

Radar echo extrapolation is a critical forecasting tool in the field of meteorology, playing an especially vital role in nowcasting and weather modification operations. In recent years, spatiotemporal sequence prediction models based on deep learning have garnered significant attention and achieved notable progress in radar echo extrapolation. However, most of these extrapolation network architectures are built upon convolutional neural networks, using radar echo images as input. Typically, radar echo intensity values ranging from −5 to 70 dBZ with a resolution of 5 dBZ are converted into 0–255 grayscale images from pseudo-color representations, which inevitably results in the loss of important echo details. Furthermore, as the extrapolation time increases, the smoothing effect inherent to convolution operations leads to increasingly blurred predictions. To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. These variables are encoded jointly with high-resolution (0.5 dB) radar mosaic data to form multiple radar cells as input. A multi-channel radar echo extrapolation network architecture (MR-DCGAN) is then designed based on the DCGAN framework; (3) Since radar echo decay becomes more prominent over longer extrapolation horizons, this study departs from previous approaches that use a single model to extrapolate 120 min. Instead, it customizes time-specific loss functions for spatiotemporal attenuation correction and independently trains 20 separate models to achieve the full 120 min extrapolation. The dataset consists of radar composite reflectivity mosaics over North China within the range of 116.10–117.50°E and 37.77–38.77°N, collected from June to September during 2018–2022. A total of 39,000 data samples were matched with the initial zero-hour fields from RMAPS-NOW, with 80% (31,200 samples) used for training and 20% (7800 samples) for testing. Based on the ConvLSTM and the proposed MR-DCGAN architecture, 20 extrapolation models were trained using four different input encoding strategies. The models were evaluated using the Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR). Compared to the baseline ConvLSTM-based extrapolation model without physical variables, the models trained with the MR-DCGAN architecture achieved, on average, 18.59%, 8.76%, and 11.28% higher CSI values, 19.46%, 19.21%, and 19.18% higher POD values, and 19.85%, 11.48%, and 9.88% lower FAR values under the 20 dBZ, 30 dBZ, and 35 dBZ reflectivity thresholds, respectively. Among all tested configurations, the model that incorporated three physical variables—relative humidity (rh), u-wind, and v-wind—demonstrated the best overall performance across various thresholds, with CSI and POD values improving by an average of 16.75% and 24.75%, respectively, and FAR reduced by 15.36%. Moreover, the SSIM of the MR-DCGAN models demonstrates a more gradual decline and maintains higher overall values, indicating superior capability in preserving echo structural features. Meanwhile, the comparative experiments demonstrate that the MR-DCGAN (u, v + rh) model outperforms the MR-ConvLSTM (u, v + rh) model in terms of evaluation metrics. In summary, the model trained with the MR-DCGAN architecture effectively enhances the accuracy of radar echo extrapolation. Full article

Short-term precipitation forecasting is a core task in meteorological science, aiming to achieve accurate predictions by modeling the spatiotemporal evolution of radar echo sequences, thereby supporting meteorological services and disaster warning systems. However, existing spatiotemporal sequence prediction methods still struggle to disentangle complex spatiotemporal dependencies effectively and fail to capture the nonlinear chaotic characteristics of precipitation systems. This often results in ambiguous predictions, attenuation of echo intensity, and spatial localization errors. To address these challenges, this paper proposes a unified spatiotemporal sequence prediction framework based on spatiotemporal masking, which comprises two stages: self-supervised pre-training and task-oriented fine-tuning. During pre-training, the model learns global structural features of meteorological systems from sparse contexts by randomly masking local spatiotemporal regions of radar images. In the fine-tuning stage, considering the importance of the temporal dimension in short-term precipitation forecasting and the complex long-range dependencies in spatiotemporal evolution of precipitation systems, we design an RNN-based cyclic temporal mask self-encoder model (MAE-RNN) and a transformer-based spatiotemporal attention model (STMT). The former focuses on capturing short-term temporal dynamics, while the latter simultaneously models long-range dependencies across space and time via a self-attention mechanism, thereby avoiding the smoothing effects on high-frequency details that are typical of conventional convolutional or recurrent structures. The experimental results show that STMT improves 3.73% and 2.39% in CSI and HSS key indexes compared with the existing advanced models, and generates radar echo sequences that are closer to the real data in terms of air mass morphology evolution and reflection intensity grading. Full article

Background/Objectives: Two-dimensional echocardiography (2D echo) is widely used for assessing left ventricular ejection fraction (LVEF). This single-center comparative study aims to evaluate the accuracy of LVEF measurements obtained using the AI-assisted handheld ultrasound device Kosmos against cardiac magnetic resonance (CMR), the current gold standard. Methods: A total of 49 adult patients undergoing clinically indicated CMR were prospectively enrolled. AI-based LVEF measurements were compared with CMR using the Wilcoxon signed-rank test, Pearson correlation, multivariable linear regression, and Bland–Altman analysis. All analyses were performed using STATA v18.0. Results: Median LVEF was 57% (CMR) vs. 55% (AI-Echo), with no significant difference (p = 0.51). Strong correlation (r = 0.99) and minimal bias (1.1%) were observed. Conclusions: The Kosmos AI-based autoEF algorithm demonstrated excellent agreement with CMR-derived LVEF values. Its speed and automation make it promising for bedside assessment in emergency departments, intensive care units, and outpatient clinics. This study aims to fill the gap in current clinical evidence by evaluating, for the first time, the agreement between LVEF measurements obtained via Kosmos’ AI-assisted autoEF and those from cardiac MRI (CMR), the gold standard for ventricular function assessment. This comparison is critical for validating the reliability of portable AI-driven echocardiographic tools in real-world clinical practice. However, these findings derive from a selected population at a single Italian center and should be validated in larger, diverse cohorts before assuming global generalizability. Full article

The integration of intelligent voice-control systems represents a critical pathway for enhancing driver comfort and reducing cognitive distraction in modern vehicles. Currently, voice assistants capable of accessing real-time vehicular data (e.g., engine parameters) or controlling actuators (e.g., door locks) remain exclusive to premium brands. While aftermarket solutions like Amazon’s Echo Auto provide multimedia functionality, they lack access to critical vehicle systems. To address this gap, we develop a novel architecture leveraging the OBD-II port to enable voice-controlled telematics and actuation in mass-production vehicles. Our system interfaces with a Toyota Hilux (2020) and Mazda CX-3 SUV (2021), utilizing an MCP2515 CAN controller for engine control unit (ECU) communication, an Arduino Nano for data processing, and an ESP01 Wi-Fi module for cloud transmission. The Blynk IoT platform orchestrates data flow and provides user interfaces, while a Voiceflow-programmed Alexa skill enables natural language commands (e.g., “unlock doors”) via Alexa Auto. Experimental validation confirms the successful real-time monitoring of engine variables (coolant temperature, air–fuel ratio, ignition timing) and secure door-lock control. This work demonstrates that high-end vehicle capabilities—previously restricted to luxury segments—can be effectively implemented in series-production automobiles through standardized OBD-II protocols and IoT integration, establishing a scalable framework for next-generation in-vehicle assistants. Full article

Background: Most of the existing hyperpolarized (HP) ¹³C MRI analyses use univariate rate maps of pyruvate-to-lactate conversion (k_PL), and radiomic-style multiparametric models extracting complex, higher-order features remain unexplored. Purpose: To establish a multivariate framework based on whole abdomen/pelvis HP ¹³C-pyruvate MRI and evaluate the association between multiparametric features of metabolism (MFM) and clinical outcome measures in advanced and metastatic prostate cancer. Methods: Retrospective statistical analysis was performed on 16 participants with metastatic or local-regionally advanced prostate cancer prospectively enrolled in a tertiary center who underwent HP-pyruvate MRI of abdomen or pelvis between November 2020 and May 2023. Five patients were hormone-sensitive and eleven were castration-resistant. GMP-grade [1-¹³C]pyruvate was polarized using a 5T clinical-research DNP polarizer, and HP MRI used a set of flexible vest-transmit, array-receive coils, and echo-planar imaging sequences. Three basic metabolic maps (k_PL, pyruvate summed-over-time, and mean pyruvate time) were created by semi-automatic segmentation, from which 316 MFMs were extracted using an open-source, radiomic-compliant software package. Univariate risk classifier was constructed using a biologically meaningful feature (k_PL,median), and the multivariate classifier used a two-step feature selection process (ranking and clustering). Both were correlated with progression-free survival (PFS) and overall survival (OS) (median follow-up = 22.0 months) using Cox proportional hazards model. Results: In the univariate analysis, patients harboring tumors with lower-k_PL,median had longer PFS (11.2 vs. 0.5 months, p < 0.01) and OS (NR vs. 18.4 months, p < 0.05) than their higher-k_PL,median counterparts. Using a hypothesis-generating, age-adjusted multivariate risk classifier, the lower-risk subgroup also had longer PFS (NR vs. 2.4 months, p < 0.002) and OS (NR vs. 18.4 months, p < 0.05). By contrast, established laboratory markers, including PSA, lactate dehydrogenase, and alkaline phosphatase, were not significantly associated with PFS or OS (p > 0.05). Key limitations of this study include small sample size, retrospective study design, and referral bias. Conclusions: Risk classifiers derived from select multiparametric HP features were significantly associated with clinically meaningful outcome measures in this small, heterogeneous patient cohort, strongly supporting further investigation into their prognostic values. Full article

Bistatic active sonar enables robust and precise target position and tracking, making it a key technology for autonomous underwater vehicles (AUVs) in underwater surveillance. This paper proposes a multi-information-assisted target tracking algorithm for bistatic active sonar, leveraging spatial and temporal echo signal structures to address the challenges of AUVs in shallow water. First, broadened cluster formations in sonar echoes are analyzed, leading to the integration of a spatial clustering-based data association. This paper departs from conventional methods by fusing target position, echo amplitude, and Doppler information during the movement of AUVs, which can improve the efficiency of association probability computation. The re-derived multi-information-assisted association probability calculation method and algorithmic workflow are explicitly designed for real-time implementation in AUV systems. Simulation experiments verify the feasibility of integrating Doppler and amplitude information. The sea trial data from simulated AUV-deployed bistatic sonar contained only amplitude information due to experimental limitations. By utilizing this amplitude information, the algorithm proposed in this paper demonstrates a 23.95% performance improvement over the traditional probabilistic data association algorithm. The proposed algorithm provides AUVs with enhanced tracking autonomy, significantly advancing their capability in ocean engineering applications. Full article

Botulinum neurotoxin type A (BoNT-A) is widely used to reduce spasticity in children with cerebral palsy. Despite its therapeutic benefits, incomplete muscle recovery has been observed post-treatment. This study evaluated longitudinal BoNT-A effects on muscle morphology over one year in children with CP (n = 26, mean age: 5.19 years ± 3.26). Three-dimensional freehand ultrasound assessed medial gastrocnemius muscle volume (MV), muscle belly length (ML), cross-sectional area (CSA), and echo intensity (EI) at baseline and at 3, 6, and 12 months post-BoNT-A. Z-score normalization accounted for natural muscle growth. Linear mixed models analyzed muscular changes over time, and repeated-measures ANOVA compared muscle parameters to an age- and severity-matched control group (n = 26, mean age: 4.98 ± 2.15) at one-year follow-up. MV exhibited a declining trend at 3 (p = 0.005), 6 (p = 0.003), and 12 months (p = 0.007), while ML remained unchanged throughout follow-up (p = 0.95). The initially reduced CSA at 6 months (p = 0.0005) recovered at one year, and EI increased only at 3 months post-BoNT-A (p < 0.0001). At one-year follow-up, there was a trend for reduced growth rate (MV/month) (p = 0.035) in the intervention group, whereas the control group exhibited an increased muscle growth (p = 0.029). These findings suggest distinct recovery timelines for CSA and ML, which may explain the incomplete MV recovery and highlight substantial interindividual variation in recovery processes. Full article

Background/Objectives: T2 mapping is a quantitative magnetic resonance imaging (MRI) technique that provides information about tissue water content and molecular mobility. This study aimed to evaluate the diagnostic utility of T2 mapping in assessing sacroiliitis associated with spondyloarthropathy (SpA). Methods: A prospective study examined a total of 56 participants, comprising 31 SpA patients (n = 31) and 25 healthy controls (n = 25), who underwent sacroiliac joint MRI between August 2018 and August 2020. T2 mapping images were generated using multi-echo turbo spin echo (TSE) sequence, and quantitative T2 relaxation times were measured from bone and cartilage regions. Statistical analysis employed appropriate parametric and non-parametric tests with significance set at p < 0.05. Results: The mean T2 relaxation time measured from the areas with osteitis of SpA patients (100.23 ± 7.41 ms; 95% CI: 97.51–102.95) was significantly higher than that of the control group in normal bone (69.44 ± 4.37 ms; 95% CI: 67.64–71.24), and this difference was found to be statistically significant (p < 0.001). No significant difference was observed between cartilage T2 relaxation times in SpA patients and controls (p > 0.05). Conclusions: T2 mapping serves as a valuable quantitative imaging biomarker for diagnosing sacroiliitis associated with SpA, particularly by detecting bone marrow edema. The technique shows promise for objective disease assessment, though larger studies are needed to establish standardized reference values for T2 relaxation times in osteitis to enhance diagnostic accuracy and facilitate treatment monitoring. Full article

Aluminum–Magnesium (Al–Mg) alloys undergo sensitization, i.e., the precipitations of β-phase (Al₂Mg₃) at the grain boundaries, when exposed to elevated temperature. This microstructural change increases the susceptibility of Al–Mg alloys to intergranular corrosion, exfoliation, and stress corrosion cracking. This study introduces a time-frequency analysis (TFA) technique to determine the frequency-dependent ultrasonic attenuation parameter and correlate the frequency-attenuation slope to the Degree of Sensitization (DoS) developed in heat-treated Al–Mg alloy samples. Broadband pitch-catch signal was generated using a laser ultrasonic testing (LUT) system, from which the narrowband pitch-catch signal at different frequencies can be digitally generated. The attenuation parameters of sensitized Al–Mg samples were determined from these narrowband pitch-catch signals using the primary pulse-first echo (PP-FE) method. By identifying the frequency range within which the attenuation parameter is linearly proportional to the frequency, the slopes of the frequency-attenuation relationship were determined and correlated with the DoS values of the sample plates. The experimental results validate that the frequency-attenuation slope has a higher sensitivity and lower scattering as compared to other conventional ultrasonic attenuation measurement techniques. Full article