Low-frequency Image Research Articles

IVOMFuse: An image fusion method based on infrared-to-visible object mapping

Due to the significant difference in the structure of infrared and visible images, most of the existing algorithms use globally consistent strategy, which may weaken the contrast of the target region. To solve this problem, we propose an infrared and visible image fusion method based on infrared-to-visible object mapping (IVOMFuse) First of all, we use the probability induced intuitionistic fuzzy C-means algorithm (PIIFCM) to process the infrared image, and extract the target region of the infrared image according to the image structure features. However, because of the poor contrast of the target of the visible image, it can not be segmented directly. For this reason, we map the infrared target region to the visible image, and the target region with the same coordinates of the two images can be obtained very quickly. Secondly, due to the significant differences in image structure, we set a hybrid fusion strategies for different regions. We use the fusion strategy based on the Expectation-Maximization (EM) algorithm to iterate and optimize the probability model for the target region. The fourth-order partial differential equation (FPDE) is used for the background region to decompose the source image into high-frequency texture and low-frequency approximation images respectively. Then, the principal component analysis (PCA) and the average fusion strategy are used to obtain the fused image. Finally, we evaluated our algorithm with fourteen other state-of-the-art image fusion methods on three commonly used datasets, namely TNO, CVC14, and RoadScene. Among the ten evaluation metrics, our algorithm achieved the best six times.

Integration- and separation-aware adversarial model for cerebrovascular segmentation from TOF-MRA

PurposeCerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) is important but challenging for the simulation and measurement of cerebrovascular diseases. Recently, deep learning has promoted the rapid development of cerebrovascular segmentation. However, model optimization relies on voxel or regional punishment and lacks global awareness and interpretation from the texture and edge. To overcome the limitations of the existing methods, we propose a new cerebrovascular segmentation method to obtain more refined structures. MethodsIn this paper, we propose a new adversarial model that achieves segmentation using segmentation model and filters the results using discriminator. Considering the sample imbalance in cerebrovascular imaging, we separated the TOF-MRA images and utilized high- and low-frequency images to enhance the texture and edge representation. The encoder weight sharing from the segmentation model not only saves the model parameters, but also strengthens the integration and separation correlation. Diversified discrimination enhances the robustness and regularization of the model. ResultsThe adversarial model was tested using two cerebrovascular datasets. It scored 82.26% and 73.38%, respectively, ranking first on both datasets. The results show that our method not only outperforms the recent cerebrovascular segmentation model, but also surpasses the common adversarial models. ConclusionOur adversarial model focuses on improving the extraction ability of the model on texture and edge, thereby achieving awareness of the global cerebrovascular topology. Therefore, we obtained an accurate and robust cerebrovascular segmentation. This framework has potential applications in many imaging fields, particularly in the application of sample imbalance. Our code is available at the website https://github.com/MontaEllis/ISA-model.

Experimental evaluation of effects of absorption layer on parametric ultrasound

This study investigated the effects of absorption attenuation on parametric ultrasound (US) for application to low-frequency (LF) US measurement and imaging. The effects of an absorption layer on the sound field and pulsed wave of parametric US at several hundred kilohertz in water were experimentally evaluated via a comparison with LF US directly radiated from a transmitter. The results indicate that the variations of the shape and amplitude of the parametric US field and waveform due to the insertion of an absorber were more significant than those for LF US at the same frequency. However, these effects can be mitigated when an absorber is inserted at a far distance from a transmitter. These results suggest that for the application of parametric US in measurement and imaging, a low-absorption medium must be inserted between a transmitter and absorbing material to be evaluated in order to grow secondary waves sufficiently.

Optimal Axial-Probe Design for Foucault-Current Tomography: A Global Optimization Approach Based on Linear Sampling Method

This paper is concerned with the optimal design of axial probes, commonly used in the Non-Destructive Testing (NDT) of tube boiling in steam generators. The goal is to improve the low-frequency Foucault-current imaging of these deposits by designing a novel probe. The approach uses a combination of an inverse problem solver with global optimization to find the optimal probe characteristics by minimizing a function of merit defined using image processing techniques. The evaluation of the function of merit is computationally intensive and a surrogate optimization approach is used, incorporating a multi-particle search algorithm. The proposed design is validated through numerical experiments and aims to improve the accuracy and efficiency of identifying deposits in steam generator tubes.

Structure-transferring edge-enhanced grid dehazing network.

The problem of image dehazing has received a great deal of attention in the computer vision community over the past two decades. Under haze conditions, due to the scattering of water vapor and dust particles in the air, the sharpness of the image is seriously reduced, making it difficult for many computer vision systems, such as those for object detection, object recognition, surveillance, driver assistance, etc. to do further process and operation. However, the previous dehazing methods usually have shortcomings such as poor brightness, color cast, removal of uncleanliness, halos, artifacts, and blurring. To address these problems, we propose a novel Structure-transferring Edge-enhanced Grid Dehazing Network (SEGDNet) in this study. An edge-preserving smoothing operator, a guided filter, is used to efficiently decompose the images into low-frequency image structure and high-frequency edges. The Low-frequency Grid Dehazing Subnetwork (LGDSn) is proposed to effectively preserve the low-frequency structure while dehazing. The High-frequency Edge Enhancement Subnetwork (HEESn) is also proposed to enhance the edges and details while removing the noise. The Low-and-High frequency Fusion Subnetwork (L&HFSn) is used to fuse the low-frequency and high-frequency results to obtain the final dehazed image. The experimental results on synthetic and real-world datasets demonstrate that our method outperforms the state-of-the-art methods in both qualitative and quantitative evaluations.

中国SKA区域中心原型系统——软件平台

The Square Kilometre Array (SKA) radio telescope is designed to revolutionize scientific breakthroughs in a variety of scientific fields, and the SKA software system is one of the key factors influencing scientific products. The software environment for processing SKA science data must be versatile, flexible, and adaptable. The SKA Regional Centre (SRC) serves as a platform for astronomers to analyze SKA data, conduct scientific research, and interact with other academics. To enable automated parallel processing of observational data from various scientific fields, Chinese scientists have developed the China SRC-prototype (CSRC-P), installed a job scheduling system widely used by large supercomputers, installed an astronomical software platform capable of processing observational data from current leading radio telescopes, and deployed multiple scientific data processing pipelines. This paper describes the software platform of the CSRC-P as well as pipelines for processing SKA precursor telescope data, such as the low-frequency continuum imaging pipeline, spectral line imaging pipeline, and very long baseline interferometry data processing pipeline. Users worldwide have successfully conducted scientific research on SKA using this platform. The knowledge gained from the construction and operation of this platform will be useful in constructing a full-scale SRC in the future.

A clustering-based method for estimating pennation angle from B-mode ultrasound images.

B-mode ultrasound (US) is often used to noninvasively measure skeletal muscle architecture, which contains human intent information. Extracted features from B-mode images can help improve closed-loop human-robotic interaction control when using rehabilitation/assistive devices. The traditional manual approach to inferring the muscle structural features from US images is laborious, time-consuming, and subjective among different investigators. This paper proposes a clustering-based detection method that can mimic a well-trained human expert in identifying fascicle and aponeurosis and, therefore, compute the pennation angle. The clustering-based architecture assumes that muscle fibers have tubular characteristics. It is robust for low-frequency image streams. We compared the proposed algorithm to two mature benchmark techniques: UltraTrack and ImageJ. The performance of the proposed approach showed higher accuracy in our dataset (frame frequency is 20Hz), that is, similar to the human expert. The proposed method shows promising potential in automatic muscle fascicle orientation detection to facilitate implementations in biomechanics modeling, rehabilitation robot control design, and neuromuscular disease diagnosis with low-frequency data stream.

Is the X-ray bright z = 5.5 quasar SRGE J170245.3+130104 a blazar?

ABSTRACT Jets may have contributed to promoting the growth of seed black holes in the early Universe, and thus observations of radio-loud high-redshift quasars are crucial to understanding the growth and evolution of the early supermassive black holes. Here we report the radio properties of an X-ray bright z = 5.5 quasar, SRGE J170245.3+130104 (J1702+1301). Our high-resolution radio images reveal the radio counterpart at the optical position of J1702+1301, while another radio component is also detected at ∼23.5 arcsec to the south-west. Our analysis suggests that this south-west component is associated with a foreground galaxy at z ≈ 0.677, which is mixed with J1702+1301 in low-frequency low-resolution radio images. After removing the contamination from this foreground source, we recalculated the radio loudness of J1702+1301 to be R >1100, consistent with those of blazars. J1702+1301 exhibits a flat radio spectrum (α = −0.17 ± 0.05, S ∝ να) between 0.15 and 5 GHz; above 5 GHz, it shows a rising spectrum shape, and the spectral index $\alpha ^{8.2}_{4.7}$ appears to be correlated with the variation of the flux density: in burst states, $\alpha ^{8.2}_{4.7}$ becomes larger. J1702+1301 displays distinct radio variability on time-scales from weeks to years in the source’s rest frame. These radio properties, including high radio loudness, rising spectrum, and rapid variability, tend to support it as a blazar.

Pedestrian detection using a translation-invariant wavelet residual dense super-resolution.

Pedestrian detection is an important research area and technology for car driving, gait recognition, and other applications. Although a lot of pedestrian detection techniques have been introduced, low-resolution imaging devices still exist in real life, so detection in low-resolution images remains a challenging problem. To address this issue, we propose a novel end-to-end Translation-invariant Wavelet Residual Dense Super-Resolution (TiWRD-SR) method to upscale LR images to SR images and then use Yolov4 for detection to address the low detection problem performance on low-resolution images. To make the enlarged SR image not only effectively distinguish the foreground and background of images but also highlight the characteristic structure of pedestrians, we decompose the image into low-frequency and high-frequency parts by stationary wavelet transform (SWT). The high- and low-frequency sub-images are trained through different network structures so that the network can reconstruct the high-frequency image edge information and the low-frequency image structure in a more detailed manner. In addition, a high-to-low branch information transmission (H2LBIT) is proposed to import high-frequency image edge information into the low-frequency network to make the reconstructed low-frequency structure more detailed. In addition, we also propose a novel loss function, which enables the SR network to focus on the reconstruction of image structure in the network by the characteristics of wavelet decomposition, thereby improving its detection performance. The experimental results indicate that the proposed TiWRD-SR can effectively improve detection performance.

Multi-scale cascaded networks for synthesis of mammogram to decrease intensity distortion and increase model-based perceptual similarity.

Synthetic digital mammogram (SDM) is a 2D image generated from digital breast tomosynthesis (DBT) and used as a substitute for a full-field digital mammogram (FFDM) to reduce the radiation dose for breast cancer screening. The previous deep learning-based method used FFDM images as the ground truth, and trained a single neural network to directly generate SDM images with similar appearances (e.g., intensity distribution, textures) to the FFDM images. However, the FFDM image has a different texture pattern from DBT. The difference in texture pattern might make the training of the neural network unstable and result in high-intensity distortion, which makes it hard to decrease intensity distortion and increase perceptual similarity (e.g., generate similar textures) at the same time. Clinically, radiologists want to have a 2D synthesized image that feels like an FFDM image in vision and preserves local structures such as both mass and microcalcifications (MCs) in DBT because radiologists have been trained on reading FFDM images for a long time, while local structures are important for diagnosis. In this study, we proposed to use a deep convolutional neural network to learn the transformation to generate SDM from DBT. To decrease intensity distortion and increase perceptual similarity, a multi-scale cascaded network (MSCN) is proposed to generate low-frequency structures (e.g., intensity distribution) and high-frequency structures (e.g., textures) separately. The MSCN consist of two cascaded sub-networks: the first sub-network is used to predict the low-frequency part of the FFDM image; the second sub-network is used to generate a full SDM image with textures similar to the FFDM image based on the prediction of the first sub-network. The mean-squared error (MSE) objective function is used to train the first sub-network, termed low-frequency network, to generate a low-frequency SDM image. The gradient-guided generative adversarial network's objective function is to train the second sub-network, termed high-frequency network, to generate a full SDM image with textures similar to the FFDM image. 1646 cases with FFDM and DBT were retrospectively collected from the Hologic Selenia system for training and validation dataset, and 145 cases with masses or MC clusters were independently collected from the Hologic Selenia system for testing dataset. For comparison, the baseline network has the same architecture as the high-frequency network and directly generates a full SDM image. Compared to the baseline method, the proposed MSCN improves the peak-to-noise ratio from 25.3 to 27.9 dB and improves the structural similarity from 0.703 to 0.724, and significantly increases the perceptual similarity. The proposed method can stabilize the training and generate SDM images with lower intensity distortion and higher perceptual similarity.

Low-Frequency Magnetic Resonance Imaging Identifies Hand Joint Subclinical Inflammation in Systemic Sclerosis.

Objectives: The aim of this work was to determine hand joint inflammation in systemic sclerosis (SSc); patients with rheumatoid arthritis (RA) with hand joint involvement were used as controls. Our investigation also aimed at examining the relationship between these subclinical inflammatory changes in the hands, verified by low-frequency MRI, and clinical (especially cardiopulmonary) manifestations, disease activity, and functional capacity in patients with diffuse cutaneous (dcSSc) and limited cutaneous SSc (lcSSc). Methods: Out of 250 SSc patients, the selection included 82 patients with signs and symptoms of joint involvement, and 35 consecutive RA patients. These patients underwent clinical and laboratory investigations, and hand X-ray and MRI of the dominant hand. Synovitis/tenosynovitis, bone edema, and erosions were investigated, and the bone changes were quantified and scored using the RAMRIS method. HAQ index, modified Rodnan skin score, examination of internal organ involvement, and serological markers for SSc, as well as rheumatoid factor (RF) and cyclic citrullinated peptides antibodies (ACPA), were performed on all experimental group subjects. Results: MRI of the dominant hand showed a significantly higher number of cases with synovitis (78%) than the number of patients with clinically swollen joints (17.1%; p < 0.001); bone edema was found in 62 (75.6%) SSc patients. MRI also showed a higher number of erosions (52; 63.4%) compared to those (22; 27.5%) detected with X-ray (p < 0.001). The average values of the total MRI score of synovitis/edema and erosions in the wrist (p < 0.001) and MCP joints (p < 0.001) were statistically higher in RA than in SSc patients (p < 0.001). The probability of the MRI-detected inflammatory changes was considerably higher in SSc patients who had vascular complications (digital ulceration, OR = 4.68; 95% IP: 1.002–22.25; p < 0.05), in patients with more severe functional impairment (OR = 8.22; 95% IP: 1.74–38.89; p < 0.01), and in patients with active disease (OR = 3.132; 95% IP: 1.027–9.551; p < 0.05). In our investigation, patients with a limited form of the disease and with inflammatory changes on MR more often had higher functional impairment compared to the other group without MRI inflammation. Conclusions: Our data show that in SSc MRI can detect a significant subclinical joint inflammation. RAMRIS confirmed the high degree of joint inflammation in RA, but also revealed a great deal of joint inflammation in SSc. That inflammation is associated with systemic inflammation (disease activity), vascular complications, and more severe forms of the disease, as synovitis cannot be precisely diagnosed by the clinical examination of joints. These results suggest that a careful joint investigation is necessary in SSc, and that in symptomatic patients, MRI may identify joint inflammation. In clinical practice, this evidence might drive to an early targeted therapy, thus preventing joint erosions.

Working Principle of the Calibration Algorithm for High Dynamic Range Solar Imaging with the Square Kilometre Array Precursor

Imaging the low-frequency radio Sun is an intrinsically challenging problem. Meter-wavelength solar emission spans angular scales from a few arcminutes to a few degrees. These emissions show temporal and spectral variability on sub-second and sub-MHz scales. The brightness temperature of these emissions also varies by many orders of magnitude, which requires high-dynamic-range spectroscopic snapshot imaging. With the unique array configuration of the Murchison Widefield Array (MWA) and the robust calibration and imaging pipeline, Automated Imaging Routine for the Compact Arrays for the Radio Sun (AIRCARS) produces the best spectroscopic snapshot solar images available to date. The working principle and the strength of this algorithm are demonstrated using statistical analysis and simulation. AIRCARS uses the partial phase stability of the MWA, which has a compact core with many antenna elements distributed over a small array footprint. The strength of this algorithm makes it a state-of-the-art calibration and imaging pipeline for low-frequency solar imaging, which is expected to be highly suitable for the upcoming Square Kilometre Array (SKA) and other future radio interferometers for producing high-dynamic-range and high-fidelity images of the Sun.

Applying a Wavelet Transform Technique to Optimize General Fitting Models for SM Analysis: A Case Study in Downscaling over the Qinghai–Tibet Plateau

Soil moisture (SM) is an important land-surface parameter. Although microwave remote sensing is recognized as one of the most appropriate methods for retrieving SM, such retrievals often cannot meet the requirements of specific applications because of their coarse spatial resolution and spatiotemporal data gaps. A range of general models (GMs) for SM analysis topics (e.g., gap-filling, forecasting, and downscaling) have been introduced to address these shortcomings. This work presents a novel strategy (i.e., optimized wavelet-coupled fitting method (OWCM)) to enhance the fitting accuracy of GMs by introducing a wavelet transform (WT) technique. Four separate GMs are selected, i.e., elastic network regression, area-to-area regression kriging, random forest regression, and neural network regression. The fitting procedures are then tested within a downscaling analysis implemented between aggregated Global Land Surface Satellite products (i.e., LAI, FVC, albedo), Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST, and Random Forest Soil Moisture (RFSM) datasets in both the WT space and the regular space. Then, eight fine-resolution SM datasets mapped from the trained GMs and OWCMs are analyzed using direct comparisons with in situ SM measurements and indirect intercomparisons between the aggregated OWCM-/GM-derived SM and RFSM. The results demonstrate that OWCM-derived SM products are generally closer to the in situ SM observations, and better capture in situ SM dynamics during the unfrozen season, compared to the corresponding GM-derived SM product, which shows fewer time changes and more stable trends. Moreover, OWCM-derived SM products represent a significant improvement over corresponding GM-derived SM products in terms of their ability to spatially and temporally match RFSM. Although spatial heterogeneity still substantially impacts the fitting accuracies of both GM and OWCM SM products, the improvements of OWCMs over GMs are significant. This improvement can likely be attributed to the fitting procedure of OWCMs implemented in the WT space, which better captures high- and low-frequency image features than in the regular space.

Recaptured images detection algorithm based on frequency domain transformation1

With the raising popularity of digital devices in the current society, the present image detection system is becoming a great threaten. Especially the appearance of the recaptured images. It can be used in traditional invalid digital image detection algorithm. There is a new algorithm in this paper is presented to detect the recaptured and real image. The algorithm obtains low-frequency images, directional filtering images and high-frequency images by multiple application frequency domain filtering. Then the proposed algorithm analyzes the directional filtering images and high-frequency images by means of LBP algorithm to extract features. At last, the recaptured images were classified by the SVM. The experimental results demonstrated the algorithm in this paper could be effectively identify in the recaptured images.

A Novel Image Denoising and Segmentation Using Machine Learning with SRM Strategy

Objectives: In the last decade, artificial intelligence (AI) and machine learning (ML) have a significant impact on image analysis and segmentation. The most demanding aspect in digital image processing is efficient segmentation to extract the desired features or recognize the hidden patterns of the digital images, which are one of the major challenges. Even in existing segmentation techniques, due to under segmentation issues, particularly in remote or arial image analysis, necessary object features and residual details are not properly segmented. Therefore, the study presents a new method for image segmentation using machine learning, which addresses these limitations. Methods: This article presents a novel approach for arial image denoising and segmentation using unsupervised learning. The Discrete Wavelet Transform (DWT) is primarily used as a pre-processing tool, while edge details of the decomposed image are automatically preserved with the help of machine understanding. The use of a fast Non-Local (NL)-means filter provides a better visual effect for low-frequency image features, and the filtered image is partitioned into the number of clusters by Minimum- Spanning Tree (MST) clustering algorithm. Further, Statistical Region Merging (SRM) is used to eliminate the unwanted regions of the clustered image to give meaningful image details. Finally, the segmented image is projected at the wavelet restoration level. Here, the standard images are extracted from the SIPI database, and the system does not require training prototypes but works in an ”unsupervised” way. Findings: The proposed system segment 1014 regions in the first phase and the segmented image features efficiently recognize the hidden patterns of the arial image (7.1.06). However, to track the required object features, statistical Region Merging (SRM) is utilized, a drastic variation in the merging process eliminates the annoying details and brings effective outcomes, which involves 29 essential features with retained residual details at level-2 decomposition and is compared with leading segmentation techniques. The denoising performance of the suggested study provides a 21.73 Peak Signal to Noise Ratio (PSNR) value in 17.96 elapsed time. simulation results have proven to produce better segmentation and denoising with less iteration time. A qualitative evaluation through visual examination justifies the proposed study and is superior to some of the popular methods. Novelty/Applications: The utilization of wavelet transform to preserve edge features during segmentation is one of the key features of this method, and it significantly overcomes the under segmentation patterns. The combination of minimum-spanning tree clustering (MST) and statistical region merging (SRM) is the major strategic step in segmenting and detecting the essential object features of arial images. Keywords: DWT; Fast NLmeans filter; Wavelet denoising; MST; SRM

Searching for pulsars associated with polarised point sources using LOFAR: Initial discoveries from the TULIPP project

Discovering radio pulsars, particularly millisecond pulsars (MSPs), is important for a range of astrophysical applications, such as testing theories of gravity or probing the magneto-ionic interstellar medium. We aim to discover pulsars that may have been missed in previous pulsar searches by leveraging known pulsar observables (primarily polarisation) in the sensitive, low-frequency radio images from the Low-Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS), and have commenced the Targeted search, using LoTSS images, for polarised pulsars (TULIPP) survey. For this survey, we identified linearly and circularly polarised point sources with flux densities brighter than 2 mJy in LoTSS images at a centre frequency of 144 MHz with a 48 MHz bandwidth. Over 40 known pulsars, half of which are MSPs, were detected as polarised sources in the LoTSS images and excluded from the survey. We have obtained beam-formed LOFAR observations of 30 candidates, which were searched for pulsations using coherent de-dispersion. Here, we present the results of the first year of the TULIPP survey. We discovered two pulsars, PSRs J1049+5822 and J1602+3901, with rotational periods ofP = 0.73 s and 3.7 ms, respectively. We also detected a further five known pulsars (two slowly-rotating pulsars and three MSPs) for which accurate sky positions were not available to allow a unique cross-match with LoTSS sources. This targeted survey presents a relatively efficient method by which pulsars, particularly MSPs, may be discovered using the flexible observing modes of sensitive radio telescopes such as the Square Kilometre Array and its pathfinders/precursors, particularly since wide-area all-sky surveys using coherent de-dispersion are currently computationally infeasible.

A single image dehazing method based on decomposition strategy

Outdoor haze has adverse impact on outdoor image quality, including contrast loss and poor visibility. In this paper, a novel dehazing algorithm based on the decomposition strategy is proposed. It combines the advantages of the two-dimensional variational mode decomposition (2DVMD) algorithm and dark channel prior. The original hazy image is adaptively decomposed into low-frequency and high-frequency images according to the image frequency band by using the 2DVMD algorithm. The low-frequency image is dehazed by using the improved dark channel prior, and then fused with the high-frequency image. Furthermore, we optimize the atmospheric light and transmittance estimation method to obtain a defogging effect with richer details and stronger contrast. The proposed algorithm is compared with the existing advanced algorithms. Experiment results show that the proposed algorithm has better performance in comparison with the state-of-the-art algorithms.

Exploring the Circular Polarisation of Low\u2013Frequency Solar Radio Bursts with LOFAR

The Sun is an active star that often produces numerous bursts of electromagnetic radiation at radio wavelengths. Low frequency radio bursts have recently been brought back to light with the advancement of novel radio interferometers. However, their polarisation properties have not yet been explored in detail, especially with the Low Frequency Array (LOFAR), due to difficulties in calibrating the data and accounting for instrumental leakage. Here, using a unique method to correct the polarisation observations, we explore the circular polarisation of different sub-types of solar type III radio bursts and a type I noise storm observed with LOFAR, which occurred during March–April 2019. We analysed six individual radio bursts from two different dates. We present the first Stokes V low frequency images of the Sun with LOFAR in tied-array mode observations. We find that the degree of circular polarisation for each of the selected bursts increases with frequency for fundamental emission, while this trend is either not clear or absent for harmonic emission. The type III bursts studied, that are part of a long–lasting type III storm, can have different senses of circular polarisation, occur at different locations and have different propagation directions. This indicates that the type III bursts forming a classical type III storm do not necessarily have a common origin, but instead they indicate the existence of multiple, possibly unrelated acceleration processes originating from solar minimum active regions.

Super-resolution acoustic imaging

This work reports a super-resolution acoustic imaging method that inverses source distribution, strength, and structure in three-dimensional space. The nonlinear coupling process between a low-frequency sound field and a high-frequency plane wave is endowed to break the resolution limit. The reconstructed results for different source strengths and frequencies demonstrate the capability of the proposed method. The results show that the proposed method is very suitable for low-frequency source imaging, which could strengthen the super-resolution analysis capability of acoustic imaging tests and, consequently, lead to deepened physical insights into various propulsion systems in underwater and aerospace systems.

Very Low Frequency Radial Modulation for Deep Penetration Contrast-Enhanced Ultrasound Imaging.

Contrast-enhanced ultrasound imaging allows vascular imaging in a variety of diseases. Radial modulation imaging is a contrast agent-specific imaging approach for improving microbubble detection at high imaging frequencies (≥7.5 MHz), with imaging depth limited to a few centimeters. To provide high-sensitivity contrast-enhanced ultrasound imaging at high penetration depths, a new radial modulation imaging strategy using a very low frequency (100 kHz) ultrasound modulation wave in combination with imaging pulses ≤5 MHz is proposed. Microbubbles driven at 100 kHz were imaged in 10 successive oscillation states by manipulating the pulse repetition frequency to unlock the frame rate from the number of oscillation states. Tissue background was suppressed using frequency domain radial modulation imaging (F-RMI) and singular value decomposition-based radial modulation imaging (S-RMI). One hundred-kilohertz modulation resulted in significantly higher microbubble signal magnitude (63-88 dB) at the modulation frequency relative to that without 100-kHz modulation (51-59 dB). F-RMI produced images with high contrast-to-tissue ratios (CTRs) of 15 to 22 dB in a stationary tissue phantom, while S-RMI further improved the CTR (19-26 dB). These CTR values were significantly higher than that of amplitude modulation pulse inversion images (11.9 dB). In the presence of tissue motion (1 and 10 mm/s), S-RMI produced high-contrast images with CTR up to 18 dB; however, F-RMI resulted in minimal contrast enhancement in the presence of tissue motion. Finally, in transcranial ultrasound imaging studies through a highly attenuating ex vivo cranial bone, CTR values with S-RMI were as high as 23 dB. The proposed technique demonstrates successful modulation of microbubble response at 100 kHz for the first time. The presented S-RMI low-frequency radial modulation imaging strategy represents the first demonstration of real-time (20 frames/s), high-penetration-depth radial modulation imaging for contrast-enhanced ultrasound imaging.