DGSD: Dynamical graph self-distillation for EEG-based auditory spatial attention detection.

Auditory Attention Detection (AAD) aims to detect the target speaker from brain signals in a multi-speaker environment. Although EEG-based AAD methods have shown promising results in recent years, current approaches primarily rely on traditional convolutional neural networks designed for processing Euclidean data like images. This makes it challenging to handle EEG signals, which possess non-Euclidean characteristics. In order to address this problem, this paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input. Specifically, to effectively represent the non-Euclidean properties of EEG signals, dynamical graph convolutional networks are applied to represent the graph structure of EEG signals, which can also extract crucial features related to auditory spatial attention in EEG signals. In addition, to further improve AAD detection performance, self-distillation, consisting of feature distillation and hierarchical distillation strategies at each layer, is integrated. These strategies leverage features and classification results from the deepest network layers to guide the learning of shallow layers. Our experiments are conducted on two publicly available datasets, KUL and DTU. Under a 1-second time window, we achieve results of 90.0% and 79.6% accuracy on KUL and DTU, respectively. We compare our DGSD method with competitive baselines, and the experimental results indicate that the detection performance of our proposed DGSD method is not only superior to the best reproducible baseline but also significantly reduces the number of trainable parameters by approximately 100 times.

Enhancing kiwifruit flower pollination detection through frequency domain feature fusion: a novel approach to agricultural monitoring.

The pollination process of kiwifruit flowers plays a crucial role in kiwifruit yield. Achieving accurate and rapid identification of the four stages of kiwifruit flowers is essential for enhancing pollination efficiency. In this study, to improve the efficiency of kiwifruit pollination, we propose a novel full-stage kiwifruit flower pollination detection algorithm named KIWI-YOLO, based on the fusion of frequency-domain features. Our algorithm leverages frequency-domain and spatial-domain information to improve recognition of contour-detailed features and integrates decision-making with contextual information. Additionally, we incorporate the Bi-Level Routing Attention (BRA) mechanism with C3 to enhance the algorithm's focus on critical areas, resulting in accurate, lightweight, and fast detection. The algorithm achieves a m A P 0.5 of 91.6% with only 1.8M parameters, the AP of the Female class and the Male class reaches 95% and 93.5%, which is an improvement of 3.8%, 1.2%, and 6.2% compared with the original algorithm. Furthermore, the Recall and F1-score of the algorithm are enhanced by 5.5% and 3.1%, respectively. Moreover, our model demonstrates significant advantages in detection speed, taking only 0.016s to process an image. The experimental results show that the algorithmic model proposed in this study can better assist the pollination of kiwifruit in the process of precision agriculture production and help the development of the kiwifruit industry.

Multimodal optical imaging of the oculofacial region using a solid tissue-simulating facial phantom.

Significance: Spatial frequency domain imaging (SFDI) applies patterned near-infrared illumination to quantify the optical properties of subsurface tissue. The periocular region is unique due to its complex ocular adnexal anatomy. Although SFDI has been successfully applied to relatively flat in vivo tissues, regions that have significant height variations and curvature may result in optical property inaccuracies. Aim: We characterize the geometric impact of the periocular region on SFDI imaging reliability. Approach: SFDI was employed to measure the reduced scattering coefficient ( µ s ' ) and absorption coefficient ( µ a ) of the periocular region in a cast facial tissue-simulating phantom by capturing images along regions of interest (ROIs): inferior temporal quadrant (ITQ), inferior nasal quadrant (INQ), superior temporal quadrant (STQ), central eyelid margin (CEM), rostral lateral nasal bridge (RLNB), and forehead (FH). The phantom was placed on a chin rest and imaged nine times from an "en face" or "side profile" position, and the flat back of the phantom was measured 15 times. Results: The measured µ a and µ s ' of a cast facial phantom are accurate when comparing the ITQ, INQ, STQ, and FH to its flat posterior surface. Paired t tests of ITQ, INQ, STQ, and FH µ a and µ s ' concluded that there is not enough evidence to suggest that imaging orientation impacted the measurement accuracy. Regions of extreme topographical variation, i.e., CEM and RLNB, did exhibit differences in measured optical properties. Conclusions: We are the first to evaluate the geometric implications of wide-field imaging along the periocular region using a solid tissue-simulating facial phantom. Results suggest that the ITQ, INQ, STQ, and FH of a generalized face have minimal impact on the SFDI measurement accuracy. Areas with heightened topographic variation exhibit measurement variability. Device and facial positioning do not appear to bias measurements. These findings confirm the need to carefully select ROIs when measuring optical properties along the periocular region.

Soybean seed pest damage detection method based on spatial frequency domain imaging combined with RL-SVM.

Soybean seeds are susceptible to damage from the Riptortus pedestris, which is a significant factor affecting the quality of soybean seeds. Currently, manual screening methods for soybean seeds are limited to visual inspection, making it difficult to identify seeds that are phenotypically defect-free but have been punctured by stink bugs on the sub-surface. To facilitate the convenient and efficient identification of healthy soybean seeds, this paper proposes a soybean seed pest detection method based on spatial frequency domain imaging combined with RL-SVM. Firstly, soybean optical data is obtained using single integration sphere technique, and the vigor index of soybean seeds is obtained through germination experiments. Then, based on the above two data items using feature extraction algorithms (the successive projections algorithm and the competitive adaptive reweighted sampling algorithm), the characteristic wavelengths of soybeans are identified. Subsequently, the spatial frequency domain imaging technique is used to obtain the sub-surface images of soybean seeds in a forward manner, and the optical coefficients such as the reduced scattering coefficient µ ' s and absorption coefficient µ a of soybean seeds are inverted. Finally, RL-MLR, RL-GRNN, and RL-SVM prediction models are established based on the ratio of the area of insect-damaged sub-surface to the entire seed, soybean varieties, and µ a at three wavelengths (502 nm, 813 nm, and 712 nm) for predicting and identifying soybean the stinging and sucking pest damage levels of soybean seeds. The experimental results show that the spatial frequency domain imaging technique yields small errors in the optical coefficients of soybean seeds, with errors of less than 15% for µ a and less than 10% for µ ' s . After parameter adjustment through reinforcement learning, the Macro-Recall metrics of each model have improved by 10%-15%, and the RL-SVM model achieves a high Macro-Recall value of 0.9635 for classifying the pest damage levels of soybean seeds.

A comprehensive overview of diffuse correlation spectroscopy: Theoretical framework, recent advances in hardware, analysis, and applications.

Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by the already-complex DCS theoretical framework but also by the broad range of component options and system architectures. To facilitate new entry to this exciting field, we present a comprehensive review of DCS hardware architectures (continuous-wave, frequency-domain, and time-domain) and summarize corresponding theoretical models. Further, we discuss new applications of highly integrated silicon single-photon avalanche diode (SPAD) sensors in DCS, compare SPADs with existing sensors, and review other components (lasers, sensors, and correlators), as well as data analysis tools, including deep learning. Potential applications in medical diagnosis are discussed and an outlook for the future directions is provided, to offer effective guidance to embark on DCS research.

Human hand gesture recognition using fast Fourier transform with coot optimization based on deep neural network.

Hand motion detection is particularly important for managing the movement of individuals who have limbs amputated. The existing algorithm is complex, time-consuming and difficult to achieve better accuracy. A DNN is suggested to recognize human hand movements in order to get over these problems.Initially, the raw input EMG signal is captured then the signal is pre-processed using high-pass Butterworth filter and low-pass filter which is utilized to eliminate the noise present in the signal. After that pre-processed EMG signal is segmented using sliding window which is used for solving the issue of overlapping. Then the features are extracted from the segmented signal using Fast Fourier Transform. Then selected the appropriate and optimal number of features from the feature subset using coot optimization algorithm. After that selected features are given as input for deep neural network classifier for recognizing the hand movements of human. The simulation analysis shows that the proposed method obtain 95% accuracy, 0.05% error, precision is 94%, and specificity is 92%.The simulation analysis shows that the developed approach attain better performance compared to other existing approaches. This prediction model helps in controlling the movement of amputee patients suffering from disable hand motion and improve their living standard.

Investigation of the vibration isolation effect of composite vibration isolation walls on ground surface vibrations in deep tunnels of suburban railways.

To investigate the vibration isolation effect of composite vibration isolation walls on surface vibrations in suburban railway deep tunnels under various influencing factors, an integrated numerical model of the train was initially developed. This model solved the wheel-rail interaction force and was applied to a three-dimensional volume coupling model of the track soil. Subsequently, the model's reliability was validated through comparison with measured data. Afterward, the vibration isolation effects of various types of EPS material vibration isolation walls were examined, with a focus on exploring the impact of thickness, material proportion, and relative positioning of the materials within the vibration isolation wall composed of EPS material and concrete. Research indicates that with an increase in the burial depth of a single material vibration isolation wall, its effective vibration isolation frequency range gradually widens. When the burial depth of the vibration isolation wall exceeds the tunnel burial depth, the vibration isolation effect is optimal. Composite vibration isolation walls, with thicknesses smaller than single-material vibration isolation walls, exhibit superior vibration isolation effects compared to their single-material counterparts. The effective vibration isolation frequency band of composite vibration isolation walls differs from that of single-material vibration isolation walls. Using the optimal-size vibration isolation wall of a single material as a composite vibration isolation wall enhances the vibration isolation effect of peak acceleration in the frequency domain by 16.58% and peak velocity by 16.95%. Moreover, frequency domain peak displacement experiences a 30.73% improvement in the vibration isolation effect.

Integrative role of attention networks in frequency-dependent modular organization of human brain.

Despite converging evidence of hierarchical organization in the cerebral cortex, with sensory-motor and association regions at opposite ends, the mechanism of such hierarchical interactions remains elusive. This organization was primarily investigated regarding the spatiotemporal dynamics of intrinsic connectivity networks (ICNs). However, more effort is needed to investigate network dynamics in the frequency domain. We aimed to examine the integrative role of brain regions in the frequency domain with graph metrics. Phase-based connectivity estimation was performed in three frequency bands (0.011-0.038, 0.043-0.071, and 0.076-0.103 Hz) in the BOLD signal during rest. We applied modularity analysis to connectivity matrices and investigated those areas, which we called integrative regions, that showed frequency-domain flexibility. Integrative regions, mostly belonging to attention networks, were densely connected to higher-order cognitive ICNs in lower frequency bands but to sensory-motor ICNs in higher frequency bands. We compared the normalized participation coefficient (Pnorm) values of integrative and core regions with respect to their relation to higher-order cognition using a permutation-based t-test for multiple linear regression. Regression parameters of integrative regions in relation to three cognitive scores in executive functions, and working memory were significantly larger than those of core regions (Pfdr < 0.05) for salience ventral attention network. Parameters of integrative regions in relation to intelligence scores were significantly larger than those with core regions (Pfdr < 0.05) in dorsal attention network. Larger parameters of neuropsychological test scores in relation to these flexible parcels further indicate their essential role at an intermediate level in behavior. Results emphasize the importance of frequency-band analysis of brain networks.

Spectral Decomposition and Transformation for Cross-domain Few-shot Learning.

Cross-domain few-shot Learning (CDFSL) is proposed to first pre-train deep models on a source domain dataset where sufficient data is available, and then generalize models to target domains to learn from only limited data. However, the gap between the source and target domains greatly hampers the generalization and target-domain few-shot finetuning. To address this problem, we analyze the domain gap from the aspect of frequency-domain analysis. We find the domain gap could be reflected by the compositions of source-domain spectra, and the lack of compositions in the source datasets limits the generalization. Therefore, we aim to expand the coverage of spectra composition in the source datasets to help the source domain cover a larger range of possible target-domain information, to mitigate the domain gap. To achieve this goal, we propose the Spectral Decomposition and Transformation (SDT) method, which first randomly decomposes the spectrogram of the source datasets into orthogonal bases, and then randomly samples different coordinates in the space formed by these bases. We integrate the above process into a data augmentation module, and further design a two-stream network to handle augmented images and original images respectively. Experimental results show that our method achieves state-of-the-art performance in the CDFSL benchmark dataset.

Deep learning-enabled high-speed, multi-parameter diffuse optical tomography.

Significance: Frequency-domain diffuse optical tomography (FD-DOT) could enhance clinical breast tumor characterization. However, conventional diffuse optical tomography (DOT) image reconstruction algorithms require case-by-case expert tuning and are too computationally intensive to provide feedback during a scan. Deep learning (DL) algorithms front-load computational and tuning costs, enabling high-speed, high-fidelity FD-DOT. Aim: We aim to demonstrate a simultaneous reconstruction of three-dimensional absorption and reduced scattering coefficients using DL-FD-DOT, with a view toward real-time imaging with a handheld probe. Approach: A DL model was trained to solve the DOT inverse problem using a realistically simulated FD-DOT dataset emulating a handheld probe for human breast imaging and tested using both synthetic and experimental data. Results: Over a test set of 300 simulated tissue phantoms for absorption and scattering reconstructions, the DL-DOT model reduced the root mean square error by 12 % ± 40 % and 23 % ± 40 % , increased the spatial similarity by 17 % ± 17 % and 9 % ± 15 % , increased the anomaly contrast accuracy by 9 % ± 9 % ( µ a ), and reduced the crosstalk by 5 % ± 18 % and 7 % ± 11 % , respectively, compared with model-based tomography. The average reconstruction time was reduced from 3.8 min to 0.02 s for a single reconstruction. The model was successfully verified using two tumor-emulating optical phantoms. Conclusions: There is clinical potential for real-time functional imaging of human breast tissue using DL and FD-DOT.

Recent advances in the metamaterial and metasurface-based biosensor in the gigahertz, terahertz, and optical frequency domains.

Recently, metamaterials and metasurface have gained rapidly increasing attention from researchers due to their extraordinary optical and electrical properties. Metamaterials are described as artificially defined periodic structures exhibiting negative permittivity and permeability simultaneously. Whereas metasurfaces are the 2D analogue of metamaterials in the sense that they have a small but not insignificant depth. Because of their high optical confinement and adjustable optical resonances, these artificially engineered materials appear as a viable photonic platform for biosensing applications. This review paper discusses the recent development of metamaterial and metasurface in biosensing applications based on the gigahertz, terahertz, and optical frequency domains encompassing the whole electromagnetic spectrum. Overlapping features such as material selection, structure, and physical mechanisms were considered during the classification of our biosensing applications. Metamaterials and metasurfaces working in the GHz range provide prospects for better sensing of biological samples, THz frequencies, falling between GHz and optical frequencies, provide unique characteristics for biosensing permitting the exact characterization of molecular vibrations, with an emphasis on molecular identification, label-free analysis, and imaging of biological materials. Optical frequencies on the other hand cover the visible and near-infrared regions, allowing fine regulation of light-matter interactions enabling metamaterials and metasurfaces to offer excellent sensitivity and specificity in biosensing. The outcome of the sensor's sensitivity to an electric or magnetic field and the resonance frequency are, in theory, determined by the frequency domain and features. Finally, the challenges and possible future perspectives in biosensing application areas have been presented that use metamaterials and metasurfaces across diverse frequency domains to improve sensitivity, specificity, and selectivity in biosensing applications.

An Underwater Image Denoising Method Based on High-Frequency Abrupt Signal Separation and Hybrid Attention Mechanism.

During underwater image processing, image quality is affected by the absorption and scattering of light in water, thus causing problems such as blurring and noise. As a result, poor image quality is unavoidable. To achieve overall satisfying research results, underwater image denoising is vital. This paper presents an underwater image denoising method, named HHDNet, designed to address noise issues arising from environmental interference and technical limitations during underwater robot photography. The method leverages a dual-branch network architecture to handle both high and low frequencies, incorporating a hybrid attention module specifically designed for the removal of high-frequency abrupt noise in underwater images. Input images are decomposed into high-frequency and low-frequency components using a Gaussian kernel. For the high-frequency part, a Global Context Extractor (GCE) module with a hybrid attention mechanism focuses on removing high-frequency abrupt signals by capturing local details and global dependencies simultaneously. For the low-frequency part, efficient residual convolutional units are used in consideration of less noise information. Experimental results demonstrate that HHDNet effectively achieves underwater image denoising tasks, surpassing other existing methods not only in denoising effectiveness but also in maintaining computational efficiency, and thus HHDNet provides more flexibility in underwater image noise removal.

Gearbox Fault Diagnosis Method in Noisy Environments Based on Deep Residual Shrinkage Networks.

Gearbox fault diagnosis is essential in the maintenance and preventive repair of industrial systems. However, in actual working environments, noise frequently interferes with fault signals, consequently reducing the accuracy of fault diagnosis. To effectively address this issue, this paper incorporates the noise attenuation of the DRSN-CW model. A compound fault detection method for gearboxes, integrated with a cross-attention module, is proposed to enhance fault diagnosis performance in noisy environments. First, frequency domain features are extracted from the public dataset by using the fast Fourier transform (FFT). Furthermore, the cross-attention mechanism model is inserted in the optimal position to improve the extraction and recognition rate of global and local fault features. Finally, noise-related features are filtered through soft thresholds within the network structure to efficiently mitigate noise interference. The experimental results show that, compared to existing network models, the proposed model exhibits superior noise immunity and high-precision fault diagnosis performance.

Acute Response of Different High-Intensity Interval Training Protocols on Cardiac Auto-Regulation Using Wearable Device.

The purpose of this study was to compare different high-intensity interval training (HIIT) protocols with different lengths of work and rest times for a single session (all three had identical work-to-rest ratios and exercise intensities) for cardiac auto-regulation using a wearable device. With a randomized counter-balanced crossover, 13 physically active young male adults (age: 19.4 years, BMI: 21.9 kg/m2) were included. The HIIT included a warm-up of at least 5 min and three protocols of 10 s/50 s (20 sets), 20 s/100 s (10 sets), and 40 s/200 s (5 sets), with intensities ranging from 115 to 130% Wattmax. Cardiac auto-regulation was measured using a non-invasive method and a wearable device, including HRV and vascular function. Immediately after the HIIT session, the 40 s/200 s protocol produced the most intense stimulation in R-R interval (Δ-33.5%), ln low-frequency domain (Δ-42.6%), ln high-frequency domain (Δ-73.4%), and ln LF/HF ratio (Δ416.7%, all p < 0.05) compared to other protocols of 10 s/50 s and 20 s/100 s. The post-exercise hypotension in the bilateral ankle area was observed in the 40 s/200 s protocol only at 5 min after HIIT (right: Δ-12.2%, left: Δ-12.6%, all p < 0.05). This study confirmed that a longer work time might be more effective in stimulating cardiac auto-regulation using a wearable device, despite identical work-to-rest ratios and exercise intensity. Additional studies with 24 h measurements of cardiac autoregulation using wearable devices in response to various HIIT protocols are warranted.

Influence of Immersion Time on the Frequency Domain Characteristics of Acoustic Emission Signals in Clayey Mineral Rocks.

The frequency domain characteristics of acoustic emission can reflect issues such as rock structure and stress conditions that are difficult to analyze in time domain parameters. Studying the influence of immersion time on the mechanical properties and acoustic emission frequency domain characteristics of muddy mineral rocks is of great significance for comprehensively analyzing rock changes under water-rock coupling conditions. In this study, uniaxial compression tests and acoustic emission tests were conducted on sandstones containing montmorillonite under dry, saturated, and different immersion time conditions, with a focus on analyzing the effect of immersion time on the dominant frequency of rock acoustic emission. The results indicated that immersion time had varying degrees of influence on compressive strength, the distribution characteristics of dominant acoustic emission frequencies, the frequency range of dominant frequencies, and precursor information of instability failure for sandstones. After initial saturation, the strength of the rock sample decreased from 53.52 MPa in the dry state to 49.51 MPa, and it stabilized after 30 days of immersion. Both dry and initially saturated rock samples exhibited three dominant frequency bands. After different immersion days, a dominant frequency band appeared between 95 kHz and 110 kHz. After 5 days of immersion, the dominant frequency band near 0 kHz gradually disappeared. After 60 days of immersion, the dominant frequency band between 35 kHz and 40 kHz gradually disappeared, and with increasing immersion time, the dominant frequency of the acoustic emission signals increased. During the loading process of dry rock samples, the dominant frequency of acoustic emission signals was mainly concentrated between 0 kHz and 310 kHz, while after saturation, the dominant frequencies were all below 180 kHz. The most significant feature before the rupture of dry rock samples was the frequent occurrence of high frequencies and sudden changes in dominant frequencies. Before rupture, the characteristics of precursor events for initially saturated and immersed samples for 5, 10, and 30 days were the appearance and rapid increase in sudden changes in dominant frequencies, as well as an enlargement of the frequency range of dominant frequencies. After 60 days of immersion, the precursor characteristics of rock sample rupture gradually disappeared, and sudden changes in dominant frequencies frequently occurred at various stages of sample loading, making it difficult to accurately predict the rupture of specimens based on these sudden changes.

Numerical analysis for temporal and spectral responses of electromagnetic waves in spatially homogeneous time varying medium.

This paper presents simple numerical solutions for electromagnetic plane waves in spatially homogenous time varying medium. The solution is based on converting the resulting second order differential equation into two combined ordinary differential equations which are solved numerically by using the built-in ode113 function in Matlab. By using this method, the time domain responses of the electric and magnetic fields at fixed point in space are obtained. The proposed method is applied on two cases: linearly time varying medium and sinusoidally time varying medium. The corresponding frequency domain response is obtained by using inverse Fourier transformation of the obtained time domain response. The proposed method is compared with FDTD solution. It is found that the proposed method has the same accuracy of FDTD with much less computational time.

The wavenumber linearisation without calibration device for spectral-domain optical coherence tomography.

The wavenumber nonlinearity leads to blurred reconstructed images in spectral-domain optical coherence tomography (SDOCT). In this work, a wavenumber-linearisation method without calibration devices is presented, based on the fact that the difference between the phases of adjacent peak and valley points is equal to π $\pi $ . The theoretical model is derived, and the efficacy of the method was proven by acquiring SDOCT data from TiO2 phantom and zebrafish. The results exhibit the superior performance of our method. Compared with the linear phase-based method, the resolution could be improved at least a factor of 2. Compared with the polynomial fitting method, the resolution could also be improved by nearly half.

Towards accurate monitoring of water content in woody tissue across tropical forests and other biomes.

Forest ecosystems face increasing drought exposure due to climate change, necessitating accurate measurements of vegetation water content to assess drought stress and tree mortality risks. Although Frequency Domain Reflectometry offers a viable method for monitoring stem water content by measuring dielectric permittivity, challenges arise from uncertainties in sensor calibration linked to wood properties and species variability, impeding its wider usage. We sampled tropical forest trees and palms in eastern Amazônia to evaluate how sensor output differences are controlled by wood density, temperature and taxonomic identity. Three individuals per species were felled and cut into segments within a diverse dataset comprising five dicotyledonous tree and three monocotyledonous palm species on a wide range of wood densities. Water content was estimated gravimetrically for each segment using a temporally explicit wet-up/dry-down approach and the relationship with the dielectric permittivity was examined. Woody tissue density had no significant impact on the calibration, but species identity and temperature significantly affected sensor readings. The temperature artefact was quantitatively important at large temperature differences, which may have led to significant bias of daily and seasonal water content dynamics in previous studies. We established the first tropical tree and palm calibration equation which performed well for estimating water content. Notably, we demonstrated that the sensitivity remained consistent across species, enabling the creation of a simplified one-slope calibration for accurate, species-independent measurements of relative water content. Our one-slope calibration serves as a general, species-independent standard calibration for assessing relative water content in woody tissue, offering a valuable tool for quantifying drought responses and stress in trees and forest ecosystems.

DF-GAM: Cross-Domain Ultrasound Image High-Quality Reconstruction Using a Dual Frequency-Domain Guided Adaptation Model.

OBJECTIVE: To enhance the quality of low-resolution (LR) ultrasound images and mitigate artifacts and speckle noise, which can impede accurate medical diagnosis, a novel method called the dual frequency-domain guided adaptation model (DF-GAM) is proposed. The method aims to achieve high-quality image reconstruction across diverse domains, including different ultrasound machines, diseases and phantom images. METHODS: DF-GAM utilizes a dual-branch network architecture combined with frequency-domain self-adaptation and self-supervised edge regression. This approach enables cross-domain enhancement by focusing on the reconstruction of clear tissue structures and speckle patterns. The model is designed to adapt to various ultrasound imaging (USI) scenarios, ensuring its applicability in real-world clinical settings. RESULTS: Experimental evaluations of DF-GAM were conducted using five different datasets. The results demonstrated the method's effectiveness, with DF-GAM outperforming existing enhancement techniques. The average peak signal-to-noise ratio (PSNR) achieved was 34.62, and the structural similarity index (SSIM) was 0.91, indicating a significant improvement in image quality compared to other methods. CONCLUSION: DF-GAM shows great potential in improving medical image diagnosis and interpretation. Its ability to enhance LR ultrasound images across various domains without the need for extensive training data makes it a valuable tool for clinical use. The high PSNR and SSIM scores validate the method's effectiveness, suggesting that DF-GAM could significantly contribute to the field of USI diagnostics.

Near-field detection and peak frequency metric for substrate and activation mapping of ventricular tachycardias in two- and three-dimensional circuits.

AIMS: Successful ventricular arrhythmia (VA) ablation requires identification of functionally critical sites during contact mapping. Estimation of the peak frequency (PF) component of the electrogram (EGM) may improve correct near-field (NF) annotation to identify circuit segments on the mapped surface. In turn, assessment of NF and far-field (FF) EGMs may delineate the three-dimensional path of a ventricular tachycardia (VT) circuit. METHODS AND RESULTS: A proprietary NF detection algorithm was applied retrospectively to scar-related re-entry VT maps and compared with manually reviewed maps employing first deflection (FDcorr) for VT activation maps and last deflection (LD) for substrate maps. Ventricular tachycardia isthmus location and characteristics mapped with FDcorr vs. NF were compared. Omnipolar low-voltage areas, late activating areas, and deceleration zones (DZ) in LD vs. NF substrate maps were compared. On substrate maps, PF estimation was compared between isthmus and bystander sites. Activation mapping with entrainment and/or VT termination with radiofrequency (RF) ablation confirmed critical sites. Eighteen patients with high-density VT activation and substrate maps (55.6% ischaemic) were included. Near-field detection correctly located critical parts of the circuit in 77.7% of the cases compared with manually reviewed VT maps as reference. In substrate maps, NF detection identified deceleration zones in 88.8% of cases, which overlapped with FDcorr VT isthmus in 72.2% compared with 83.3% overlap of DZ assessed by LD. Applied to substrate maps, PF as a stand-alone feature did not differentiate VT isthmus sites from low-voltage bystander sites. Omnipolar voltage was significantly higher at isthmus sites with longer EGM durations compared with low-voltage bystander sites. CONCLUSION: The NF algorithm may enable rapid high-density activation mapping of VT circuits in the NF of the mapped surface. Integrated assessment and combined analysis of NF and FF EGM-components could support characterization of three-dimensional VT circuits with intramural segments. For scar-related substrate mapping, PF as a stand-alone EGM feature did not enable the differentiation of functionally critical sites of the dominant VT from low-voltage bystander sites in this cohort.