High-quality Ultrasound Images Research Articles

Fully Complex-Valued Gated Recurrent Neural Network for Ultrasound Imaging.

Ultrasound imaging is widely used in medical diagnosis. It has the advantages of being performed in real time, cost-efficient, noninvasive, and nonionizing. The traditional delay-and-sum (DAS) beamformer has low resolution and contrast. Several adaptive beamformers (ABFs) have been proposed to improve them. Although they improve image quality, they incur high computation cost because of the dependence on data at the expense of real-time performance. Deep-learning methods have been successful in many areas. They train an ultrasound imaging model that can be used to quickly handle ultrasound signals and construct images. Real-valued radio-frequency signals are typically used to train a model, whereas complex-valued ultrasound signals with complex weights enable the fine-tuning of time delay for enhancing image quality. This work, for the first time, proposes a fully complex-valued gated recurrent neural network to train an ultrasound imaging model for improving ultrasound image quality. The model considers the time attributes of ultrasound signals and uses complete complex-number calculation. The model parameter and architecture are analyzed to select the best setup. The effectiveness of complex batch normalization is evaluated in training the model. The effect of analytic signals and complex weights is analyzed, and the results verify that analytic signals with complex weights enhance the model performance to reconstruct high-quality ultrasound images. The proposed model is finally compared with seven state-of-the-art methods. Experimental results reveal its great performance.

Image quality improvement in single plane-wave imaging using deep learning

In ultrasound image diagnosis, single plane-wave imaging (SPWI), which can acquire ultrasound images at more than 1000 fps, has been used to observe detailed tissue and evaluate blood flow. SPWI achieves high temporal resolution by sacrificing the spatial resolution and contrast of ultrasound images. To improve spatial resolution and contrast in SPWI, coherent plane-wave compounding (CPWC) is used to obtain high-quality ultrasound images, i.e., compound images, by coherent addition of radio frequency (RF) signals acquired by transmitting plane waves in different directions. Although CPWC produces high-quality ultrasound images, their temporal resolution is lower than that of SPWI. To address this problem, some methods have been proposed to reconstruct a ultrasound image comparable to a compound image from RF signals obtained by transmitting a small number of plane waves in different directions. These methods do not fully consider the properties of RF signals, resulting in lower image quality compared to a compound image. In this paper, we propose methods to reconstruct high-quality ultrasound images in SPWI by considering the characteristics of RF signal of a single plane wave to obtain ultrasound images with image quality comparable to CPWC. The proposed methods employ encoder–decoder models of 1D U-Net, 2D U-Net, and their combination to generate the high-quality ultrasound images by minimizing the loss that considers the point spread effect of plane waves and frequency spectrum of RF signals in training. We also create a public large-scale SPWI/CPWC dataset for developing and evaluating deep-learning methods. Through a set of experiments using the public dataset and our dataset, we demonstrate that the proposed methods can reconstruct higher-quality ultrasound images from RF signals in SPWI than conventional method.

A review of robot-assisted ultrasound examination: Systems and technology.

At present, the number and overall level of ultrasound (US) doctors cannot meet the medical needs, and the medical ultrasound robots will largely solve the shortage of medical resources. According to the degree of automation, the handheld, semi-automatic and automatic ultrasound examination robot systems are summarised. Ultrasound scanning path planning and robot control are the keys to ensure that the robot systems can obtain high-quality images. Therefore, the ultrasound scanning path planning and control methods are summarised. The research progress and future trends are discussed. A variety of ultrasound robot systems have been applied to various medical works. With the continuous improvement of automation, the systems provide high-quality ultrasound images and image guidance for clinicians. Although the development of medical ultrasound robot still faces challenges, with the continuous progress of robot technology and communication technology, medical ultrasound robot will have great development potential and broad application space.

Quality of ultrasound images by midwives implementing ultrasound scanning services during antenatal clinics within selected healthcare facilities of Zambia

BackgroundLimited access to ultrasound services in low and middle-income countries (LMICs) prompts the development of an intensive training program for midwives. The aim of this study was to evaluate the quality of ultrasound images produced by midwives and assess their competency in gestational age assessment using femur length. MethodsA cross-sectional study conducted in 11 healthcare facilities across four districts in Zambia involved the evaluation of 928 images captured during the implementation of the Training in Ultrasound to Determine Gestational Age (TUDA) program in 2021. TUDA, collaboration under the DIPLOMATIC Study, trained 24 midwives intensively for 14 days, followed by eight weeks of supervised scanning. Image assessment utilized the Sarries four-point tool, and statistical analyses included descriptive and inferential statistics. ResultsMidwives achieved an 87.95 % competency level in basic ultrasound scans for gestational age determination using femur length. Assessment by four evaluators showed an average proportion of good-quality images also at 87.95 %. There were no statistically significant differences among assessors, indicating consistency in evaluations. Trainers and radiographers exhibited different average proportions of good-quality images at 91.5 % and 83.9 %, respectively, with no statistically significant differences observed between them.Furthermore, analysis of image quality revealed significant variation among different facilities. Specifically, in Chipata, 24.32 % of scans were classified as poor quality, while in George Clinic, only 2.30 % were considered poor quality. A Chi-square test indicated a substantial correlation between health facility and image quality (χ2 = 15.99, p = 0.025). However, no significant correlations were found between image quality and factors such as work experience, age group, or gender. ConclusionThis study provides critical evidence for successfully training midwives to competent produce high-quality ultrasound images for determining gestational age. These findings are essential for guiding interventions aimed at enhancing ultrasound service delivery and ensuring consistent, high-quality healthcare outcomes, because this is critical for effective prenatal care in Zambia. This has high potential to improve prenatal care. RecommendationZambia needs to train more midwives to provide routine ultrasound services during Antenatal Care (ANC) which may ultimately reduce preterm and still births. We recommend the inclusion of ultrasound scanning in-service and pre-service training and establishment of standardized protocols and guidelines for ultrasound scanning services during antenatal clinics.

Multi-apodization with cross-correlation combined with generalized sidelobe canceller applied to ultrasound imaging.

Beamforming is vital for medical ultrasound imaging systems. The generalized sidelobe canceller (GSC) beamforming can improve the image quality of lateral resolution, but its performance improvement in contrast and robustness is limited. This paper proposes an improved generalized sidelobe canceller algorithm based on multi-apodization with cross-correlation (MAXB-IGSC), which aims to improve the contrast and robustness of ultrasound imaging while maintaining the high image resolution and background speckle quality of GSC. The proposed MAXB-IGSC uses multiple pairs of complementary received apodization functions to process the echo data individually to obtain multiple pairs of data sets. The average of their normalized cross-correlation coefficients is then calculated and utilized to determine the adaptive subarray length of the GSC covariance matrix and weights the output of the improved GSC. The MAXB-IGSC improves the contrast ratio (CR) by 171.18% in anechoic cyst simulation and by 91.23%/130.97%/171.76% in geabr_0 (a dataset from the University of Michigan) experiment compared with GSC, respectively. Furthermore, MAXB-IGSC exhibits significant noise immunity, which greatly improves the robustness of the imaging. The technology also maintains the brightness and uniformity of the background speckle. The proposed MAXB-IGSC has potential for obtaining high-quality ultrasound images in clinical applications.

Standardization of ultrasound images across various centers: M2O-DiffGAN bridging the gaps among unpaired multi-domain ultrasound images

Domain shift problem is commonplace for ultrasound image analysis due to difference imaging setting and diverse medical centers, which lead to poor generalizability of deep learning-based methods. Multi-Source Domain Transformation (MSDT) provides a promising way to tackle the performance degeneration caused by the domain shift, which is more practical and challenging compared to conventional single-source transformation tasks. An effective unsupervised domain combination strategy is highly required to handle multiple domains without annotations. Fidelity and quality of generated images are also important to ensure the accuracy of computer-aided diagnosis. However, existing MSDT approaches underperform in above two areas. In this paper, an efficient domain transformation model named M2O-DiffGAN is introduced to achieve a unified mapping from multiple unlabeled source domains to the target domain. A cycle-consistent “many-to-one” adversarial learning architecture is introduced to model various unlabeled domains jointly. A condition adversarial diffusion process is employed to generate images with high-fidelity, combining an adversarial projector to capture reverse transition probabilities over large step sizes for accelerating sampling. Considering the limited perceptual information of ultrasound images, an ultrasound-specific content loss helps to capture more perceptual features for synthesizing high-quality ultrasound images. Massive comparisons on six clinical datasets covering thyroid, carotid and breast demonstrate the superiority of the M2O-DiffGAN in the performance of bridging the domain gaps and enlarging the generalization of downstream analysis methods compared to state-of-the-art algorithms. It improves the mean MI, Bhattacharyya Coefficient, dice and IoU assessments by 0.390, 0.120, 0.245 and 0.250, presenting promising clinical applications.

WATUNet: a deep neural network for segmentation of volumetric sweep imaging ultrasound

Limited access to breast cancer diagnosis globally leads to delayed treatment. Ultrasound, an effective yet underutilized method, requires specialized training for sonographers, which hinders its widespread use. Volume sweep imaging (VSI) is an innovative approach that enables untrained operators to capture high-quality ultrasound images. Combined with deep learning, like convolutional neural networks, it can potentially transform breast cancer diagnosis, enhancing accuracy, saving time and costs, and improving patient outcomes. The widely used UNet architecture, known for medical image segmentation, has limitations, such as vanishing gradients and a lack of multi-scale feature extraction and selective region attention. In this study, we present a novel segmentation model known as Wavelet_Attention_UNet (WATUNet). In this model, we incorporate wavelet gates and attention gates between the encoder and decoder instead of a simple connection to overcome the limitations mentioned, thereby improving model performance. Two datasets are utilized for the analysis: the public ‘Breast Ultrasound Images’ dataset of 780 images and a private VSI dataset of 3818 images, captured at the University of Rochester by the authors. Both datasets contained segmented lesions categorized into three types: no mass, benign mass, and malignant mass. Our segmentation results show superior performance compared to other deep networks. The proposed algorithm attained a Dice coefficient of 0.94 and an F1 score of 0.94 on the VSI dataset and scored 0.93 and 0.94 on the public dataset, respectively. Moreover, our model significantly outperformed other models in McNemar’s test with false discovery rate correction on a 381-image VSI set. The experimental findings demonstrate that the proposed WATUNet model achieves precise segmentation of breast lesions in both standard-of-care and VSI images, surpassing state-of-the-art models. Hence, the model holds considerable promise for assisting in lesion identification, an essential step in the clinical diagnosis of breast lesions.

Review of high-quality ultrasound imaging and reconstruction

Review of high-quality ultrasound imaging and reconstruction

Flexible Ultrasound Transducer With Embedded Optical Shape Sensing Fiber for Biomedical Imaging Applications.

Flexible ultrasound transducers (FUTs), capable of conforming to irregular surfaces, have become a research hotspot in the field of medical imaging. With these transducers, high-quality ultrasound images can be obtained only if strict design criteria are fulfilled. Moreover, the relative positions of array elements must be determined, which are important for ultrasound beamforming and image reconstruction. These two major characteristics present great challenges to the design and fabrication of FUTs compared to that for traditional rigid probes. In this study, an optical shape-sensing fiber was embedded into a 128-element flexible linear array transducer to acquire the real-time relative positions of array elements to produce high-quality ultrasound images. Minimum concave and convex bend diameters of approximately 20 and 25 mm, respectively, were achieved. The transducer was flexed 2000 times, and yet no obvious damage was observed. Stable electrical and acoustic responses confirmed its mechanical integrity. The developed FUT exhibited an average center frequency of 6.35 MHz, and average -6-dB bandwidth of 69.2%. The array profile and element positions measured by the optic shape-sensing system were instantly transferred to the imaging system. Phantom experiments for both spatial resolution and contrast-to-noise ratio proved that FUTs can maintain satisfactory imaging capability despite bending to sophisticated geometries. Finally, color Doppler images and Doppler spectra of the peripheral arteries of healthy volunteers were obtained in real time.

Visual Perception and Convolutional Neural Network-Based Robotic Autonomous Lung Ultrasound Scanning Localization System.

Under the situation of severe COVID-19 epidemic, lung ultrasound (LUS) has been proved to be an effective and convenient method to diagnose and evaluate the extent of respiratory disease. However, the traditional clinical ultrasound (US) scanning requires doctors not only to be in close contact with patients but also to have rich experience. In order to alleviate the shortage of medical resources and reduce the work stress and risk of infection for doctors, we propose a visual perception and convolutional neural network (CNN)-based robotic autonomous LUS scanning localization system to realize scanned target recognition, probe pose solution and movement, and the acquisition of US images. The LUS scanned targets are identified through the target segmentation and localization algorithm based on the improved CNN, which is using the depth camera to collect the image information; furthermore, the method based on multiscale compensation normal vector is used to solve the attitude of the probe; finally, a position control strategy based on force feedback is designed to optimize the position and attitude of the probe, which can not only obtain high-quality US images but also ensure the safety of patients and the system. The results of human LUS scanning experiment verify the accuracy and feasibility of the system. The positioning accuracy of the scanned targets is 15.63 ± 0.18 mm, and the distance accuracy and rotation angle accuracy of the probe position calculation are 6.38 ± 0.25 mm and 8.60° ±2.29° , respectively. More importantly, the obtained high-quality US images can clearly capture the main pathological features of the lung. The system is expected to be applied in clinical practice.

GSDA: Generative adversarial network-based semi-supervised data augmentation for ultrasound image classification

Medical Ultrasound (US) is one of the most widely used imaging modalities in clinical practice, but its usage presents unique challenges such as variable imaging quality. Deep learning models can serve as advanced medical US image analysis tools, but their performance is greatly limited by the scarcity of large datasets. To solve the common data shortage, we develop GSDA, a Generative Adversarial Network (GAN)-based semi-supervised data augmentation method. GSDA consists of the GAN and Convolutional Neural Network (CNN). The GAN synthesizes and pseudo-labels high-resolution, high-quality US images, and both real and synthesized images are then leveraged to train the CNN. To address the training challenges of both GAN and CNN with limited data, we employ transfer learning techniques during their training. We also introduce a novel evaluation standard that balances classification accuracy with computational time. We evaluate our method on the BUSI dataset and GSDA outperforms existing state-of-the-art methods. With the high-resolution and high-quality images synthesized, GSDA achieves a 97.9% accuracy using merely 780 images. Given these promising results, we believe that GSDA holds potential as an auxiliary tool for medical US analysis.

Ultrasound Imaging with Flexible Array Transducer for Pancreatic Cancer Radiation Therapy

Pancreatic cancer with less than 10% 3-year survival rate is one of deadliest cancer types and greatly benefits from enhanced radiotherapy. Organ motion monitoring helps spare the normal tissue from high radiation and, in turn, enables the dose escalation to the target that has been shown to improve the effectiveness of RT by doubling and tripling post-RT survival rate. The flexible array transducer is a novel and promising solution to address the limitation of conventional US probes. We proposed a novel shape estimation for flexible array transducer using two sequential algorithms: (i) an optical tracking-based system that uses the optical markers coordinates attached to the probe at specific positions to estimate the array shape in real-time and (ii) a fully automatic shape optimization algorithm that automatically searches for the optimal array shape that results in the highest quality reconstructed image. We conducted phantom and in vivo experiments to evaluate the estimated array shapes and the accuracy of reconstructed US images. The proposed method reconstructed US images with low full-width-at-half-maximum (FWHM) of the point scatters, correct aspect ratio of the cyst, and high-matching score with the ground truth. Our results demonstrated that the proposed methods reconstruct high-quality ultrasound images with significantly less defocusing and distortion compared with those without any correction. Specifically, the automatic optimization method reduced the array shape estimation error to less than half-wavelength of transmitted wave, resulting in a high-quality reconstructed image.

An adaptive beamformer based on dynamic phase coherence factor for pixel-based medical ultrasound imaging.

Pixel-based beamforming realizes dynamic focusing at the pixel level with a focused beam by assuming that the received signals are composed of spherical pulses. Far-focused pixel-based (FPB) imaging was proposed to avoid artifacts around the focal depth. However, the contrast improvement is limited. We propose an adaptive weighting method based on dynamic phase coherence factor (DPCF) to improve the image contrast while preserving the speckle pattern. The phase variation is dynamically estimated based on the noise energy proportion of echo signals and it is used to calculate phase coherence weights for suppressing interference and preserving desired signals. A depth-dependent parameter is designed for DPCF to enhance the performance of noise and clutter suppression in the far-field region. We further use the subarray averaging technique to smooth the speckle texture. The proposed method was evaluated on simulated, phantom experimental, and in vivo data. Results show that, compared with the phase coherence factor (PCF) based method, DPCF respectively leads to average CR improvements by more than 60% and 24% in simulation and experiment, while obtaining an improved speckle signal-to-noise ratio. The proposed method is a potentially valuable approach to obtaining high-quality ultrasound images in clinical applications.

Application of a 2D interpolation technique to reduce the number of steering angles required for high-quality ultrasound images in plane-wave compounding

Plane wave imaging (PWI) can reach frame rates in the kHz range. However, PWI suffers from reduced image quality due to the lack of focusing. To tackle this limitation, plane wave compounding (PWC) was introduced. PWC is based on recombining multiple PWI images generated using different steering angles. However, the number of steering angles required to achieve reasonable image quality significantly reduces the frame rate. In this study, we propose a two-dimensional (2D) interpolation technique, based on radial basis functions, which allows reducing the number of steering angles in PWC without degrading image quality. The idea is to reduce the number of steering angles and then apply 2D interpolation, along the angle dimension and directly to the RF data, to reconstruct the data related to those angles for which data were not collected. The full dataset is then utilized to generate an image. To compare this technique with standard PWC, we utilized the dataset from the Plane-wave Imaging Challenge in Medical UltraSound (PICMUS). The results show that the number of steering angles can be reduced by factor 3, thus tripling the frame rate, while achieving comparable performance with standard PWC (with 75 angles) in terms of contrast and spatialresolution.

Inter-transducer variability of ultrasound image quality in obese adults: Qualitative and quantitative comparisons

PurposeAcquiring high-quality ultrasound images of deep abdominal organs and vasculatures in obese adults (BMI >30 kg/cm2) is considered challenging. The aim of the study was to assess the inter-transducer variability in B-mode and color Doppler image quality from four commercial ultrasound transducers through qualitative and quantitative analyses. MethodsFour curvilinear transducers on three ultrasound scanners were used to acquire B-mode and color Doppler images of deep abdominal structures in 15 obesity ≥ class II (BMI >35 kg/cm2) adults. Using visual-qualitative assessment and an offline image processing software, visual-qualitative score and quantitative mean pixel values of B-mode images, and color area ratios of color Doppler images were calculated. Differences in these values among the transducers were analyzed using one-way ANOVA. The intra- and inter-observer reliability of visual-qualitative assessment and offline image processing was tested using the intraclass correlation coefficient (ICC). ResultsDifferences in visual-qualitative score, mean pixel value of B-mode images, and color area ratio of color Doppler images among the four transducers were significant (p < 0.001). Transducer −4 produced the highest quality of B-mode (45–53% improvement) and color Doppler (22–73% improvement) images among the transducers. Intra-observer repeatability and inter-observer reproducibility were higher with performing offline image processing than visual-qualitative assessment (ICC: 0.97–0.99 versus ICC: 0.76–0.97). ConclusionThere was significant image quality variability between different transducers. Transducer −4, a transducer designed specifically for high BMI patients, had the highest quality B-mode and color Doppler images compared to the other transducers lending to improved ultrasonographic visualization in obese patients.

Highly compressible hydrogel reinforced with cellulose nanocrystals for ultrasound scanning via microwave-assisted synthesis

In this study, a rapid fabrication method was developed to prepare hydrogel structures with high mechanical strength and low attenuation coefficient for ultrasound scanning. Poly acrylic acid (PAA) hydrogel was first prepared via a free radical polymerization approach. To shorten the process time (~ 30 min in water bath), microwave heating was applied to facilitate the reaction and reduce the reaction time down to 80 s. The produced hydrogels showed excellent elasticity but had a low compressive strength of 100 kPa. To further enhance the mechanical strengths of PAA hydrogels, cellulosenanocrystals (CNCs) were added to the precursor solution. After the microwave assisted crosslinking process, the compressive strength of the hydrogel increased to 350 kPa. Moreover, the ultimate compressive strain was enhanced from 60 to 80% with great recoverability. The PAA/CNC hydrogel has a great ultrasound trnansimission for high-quality ultrasound images comparable to conventional liquid hydrogels. To demonstrate the feasibility of the PAA/CNC hydrogel in ultrasonic medical applications, a customized ultrasound probe coat was created with a 3D printed mold and practically used in the ultrasound scanning process.

Deep reconstruction of high-quality ultrasound images from raw plane-wave data: A simulation and in vivo study

This paper presents a novel beamforming approach based on deep learning to get closer to the ideal Point Spread Function (PSF) in Plane-Wave Imaging (PWI). The proposed approach is designed to reconstruct a high-quality version of Tissue Reflectivity Function (TRF) from echo traces acquired by transducer elements using only a single plane-wave transmission. In this approach, first, a model for the TRF is introduced by setting the imaging PSF as an isotropic (i.e., circularly symmetric) 2D Gaussian kernel convolved with a cosine function. Then, a mapping function between the pre-beamformed Radio-Frequency (RF) channel data and the proposed output is constructed using deep learning. Network architecture contains multi-resolution decomposition and reconstruction using wavelet transform for effective recovery of high-frequency content of the desired output. We exploit step by step training from coarse (mean square error) to fine (ℓ0.2) loss functions. The proposed method is trained on 1174 simulation ultrasound data with the ground-truth echogenicity map extracted from real photographic images. The performance of the trained network is evaluated on the publicly available simulation and in vivo test data without any further fine-tuning. Simulation test results show an improvement of 37.5% and 65.8% in terms of axial and lateral resolution as compared to Delay-And-Sum (DAS) results, respectively. The contrast is also improved by 33.7% in comparison to DAS. Furthermore, the reconstructed in vivo images confirm that the trained mapping function does not need any fine-tuning in the new domain. Therefore, the proposed approach maintains high resolution, contrast, and framerate simultaneously.

Perioperative Nursing Management of Patients Undergoing Laparoscopic Ovarian Cystectomy Guided by Ultrasound Imaging under Intelligent Algorithm

This study was aimed at exploring the application value of ultrasonic imaging-guided laparoscopic ovarian cystectomy after denoising by intelligent algorithms in perioperative nursing intervention of patients. In this study, convolutional downsampling was introduced to the UNet model, based on which the residual structure and Recon module were added to improve the UNet denoising model, which was applied to 100 patients who underwent ultrasound imaging-guided laparoscopic ovarian cystectomy. The patients were grouped into a control group receiving conventional nursing and an experimental group receiving perioperative nursing management. The various experimental indicators were comprehensively evaluated. The results revealed that after denoising using the improved UNet model, the ultrasound image showed no unnecessary interference noise, and the image clarity was significantly improved. In the experimental group, the operation time was 55.45 ± 6.13 days, the intraoperative blood loss was 71.52 ± 9.87 days, the postoperative exhaust time was 1.9 ± 0.73 days, the time to get out of bed was 1.2 ± 0.85 days, the complication rate was 8%, the hospitalization time was 7.3 ± 2.6 days, and the nursing satisfaction rate reached 98%. All above aspects were significantly better than those of the control group, and the differences were statistically significant (P < 0.05). In short, the improved UNet denoising model can effectively eliminate the interference noise in ultrasound and restore high-quality ultrasound images. Perioperative nursing intervention can accelerate the recovery speed of patients, reduce the complication rate, and shorten the length of stay in hospital. Therefore, it was worthy of being widely used in clinical nursing.

Deep-Learning Based Adaptive Ultrasound Imaging From Sub-Nyquist Channel Data.

Traditional beamforming of medical ultrasound images relies on sampling rates significantly higher than the actual Nyquist rate of the received signals. This results in large amounts of data to store and process, imposing hardware and software challenges on the development of ultrasound machinery and algorithms, and impacting the resulting performance. In light of the capabilities demonstrated by deep learning methods over the past years across a variety of fields, including medical imaging, it is natural to consider their ability to recover high-quality ultrasound images from partial data. Here, we propose an approach for deep-learning-based reconstruction of B-mode images from temporally and spatially sub-sampled channel data. We begin by considering sub-Nyquist sampled data, time-aligned in the frequency domain and transformed back to the time domain. The data are further sampled spatially so that only a subset of the received signals is acquired. The partial data is used to train an encoder-decoder convolutional neural network (CNN), using as targets minimum-variance (MV) beamformed signals that were generated from the original, fully-sampled data. Our approach yields high-quality B-mode images, with up to two times higher resolution than previously proposed reconstruction approaches (NESTA) from compressed data as well as delay-and-sum (DAS) beamforming of the fully-sampled data. In terms of contrast-to- noise ratio (CNR), our results are comparable to MV beamforming of the fully-sampled data, and provide up to 2 dB higher CNR values than DAS and NESTA, thus enabling better and more efficient imaging than what is used in clinical practice today.

A high frequency endoscopic ultrasound imaging method combining chirp coded excitation and compressed sensing

Insufficient imaging penetration and large data acquisition are two of the major challenges of high-frequency ultrasound imaging. Based on the good autocorrelation properties of chirp signal and the feasibility of using compressed sensing theory to reconstruct high-quality ultrasound images with low sampling requirements, this paper proposed a chirp coded excitation combined with compressed sensing (CCE-CS) technique for high-frequency endoscopic ultrasound (HFEUS) imaging. The feasibility of the method was verified by a brief theoretical analysis, and the relevant parameters were selected and analyzed according to the actual engineering situation. Simulated phantoms and in-vitro tissue experiments were used to evaluate the performance of the CCE-CS. Simulation results demonstrate that CCE-CS is capable of reducing the impact of reconstruction errors and improving imaging quality through comparison with conventional methods. The reduction of reconstruction data had less impact on penetration depth, resolution and general contrast general contrast-to-noise ratio (gCNR), and the reconstructed image was closer to the original image with a maximum improvement of 37% in peak signal-to-noise ratio (PSNR). Moreover, comparisons were conducted on the digestive tract of swine, and the results show that CCE-CS is also feasible in the in-vitro environment. These results demonstrated that CCE-CS method has good potential for application to improve the imaging quality of HFEUS while reducing the sampling rate.