The Application Value of Ultrasound Imaging in the Differential Diagnosis of Benign and Malignant Breast Nodules of BI-RADS 3 and Above

Objective:To explore the diagnostic value of ultrasound imaging for breast nodules of breast imaging-reporting and data system(BI-RADS)category 3 and above.Methods:From June 2021 to July 2022,163 patients with breast nodules of BI-RADS 3 or above were selected as the research subjects.After pathological diagnosis,24 cases were malignant breast nodules of BI-RADS 3 or above,while 139 cases were benign breast nodules of BI-RADS 3 or above.The diagnosis rate of malignant and benign breast nodules of BI-RADS 3 or above,including 95%CI,was observed and analyzed.Results:The malignant and benign detection rates of conventional ultrasound were 88.63%and 75.00%,respectively,and the malignant and benign detection rates of ultrasound imaging were 93.18%and 87.50%,respectively,with 95%CIs greater than 0.7.Conclusion:Ultrasound imaging can help improve the diagnostic accuracy of benign and malignant breast nodules of BI-RADS 3 and above and reduce the misdiagnosis rate.

A Novel Approach to Breast Tumor Detection: Enhanced Speckle Reduction and Hybrid Classification in Ultrasound Imaging

Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effectivetreatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breastcancer fromultrasound images. The primary challenge is accurately distinguishing between malignant and benigntumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentationand classification. The main objective of the research paper is to develop an advanced methodology for breastultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, andmachine learning-based classification. A unique approach is introduced that combines Enhanced Speckle ReducedAnisotropic Diffusion (SRAD) filters for speckle noise reduction, U-NET-based segmentation, Genetic Algorithm(GA)-based feature selection, and Random Forest and Bagging Tree classifiers, resulting in a novel and efficientmodel. To test and validate the hybrid model, rigorous experimentations were performed and results state thatthe proposed hybrid model achieved accuracy rate of 99.9%, outperforming other existing techniques, and alsosignificantly reducing computational time. This enhanced accuracy, along with improved sensitivity and specificity,makes the proposed hybrid model a valuable addition to CAD systems in breast cancer diagnosis, ultimatelyenhancing diagnostic accuracy in clinical applications.

Preprocedure ultrasound imaging combined with palpation technique in epidural labor analgesia

BACKGROUND For parturients with paroxysmal uterine contraction pain,rapid analgesia is needed.We used preprocedure ultrasound imaging combined with the palpation technique in epidural analgesia for labor,and evaluated the usefulness of this technique in epidural labor analgesia.AIM To evaluate the usefulness of preprocedure ultrasound imaging in epidural analgesia for labor.METHODS In this prospective randomized observational study,72 parturients were assigned to two groups(combined or palpation group).The target interspace of all parturients was first identified by the palpation technique.Then in the combined group,preprocedure ultrasound imaging was used before epidural puncture.In the palpation group,only the traditional anatomical landmarks technique(palpation technique)was performed.The primary outcome was total duration of the epidural procedure(for the ultrasound group,the duration of the preprocedure ultrasound imaging was included).The secondary outcomes were the number of skin punctures,the success rate at first needle pass,the number of needle passes,the depth from the skin to epidural space,and the complications of the procedure.RESULTS Total duration of the epidural procedure was similar between the two groups(406.5±92.15 s in the combined group and 380.03±128.2 s in the palpation group;P=0.318).A significant improvement was demonstrated for epidural puncture and catheterization in the combined group.The number of needle passes was 1.14 in the combined group and 1.72 in the palpation group(P=0.001).The number of skin puncture sites was 1.20 in the combined group and 1.25 in the palpation group(P=0.398).The success rate at first needle pass was 88.89%in the combined group and 66.67%in the palpation group(P=0.045).CONCLUSION This study demonstrated that the total duration of epidural procedures with preprocedure ultrasound imaging combined with the palpation technique was not longer than the traditional anatomical landmarks technique,which were performed by six experienced anesthesiologists in parturients with normal weights undergoing labor analgesia.

Optically-excited simultaneous photoacoustic and ultrasound imaging based on aflexible gold-PDMS film

We constructed a flexible gold-polydimethylsiloxane(gold-PDMS)nanocomposites film with controllable thickness and light transmittance,to realize optically-excited simultaneous photo-acoustic(PA)and ultrasound(US)imaging under a single laser pulse irradiation.Benefiting from the excellent thermoelastic properties,the gold-PDMS film absorbs part of the incident laser energy and produces a high-intensity US,which is used to realize US imaging.Meanwhile,the partly transmitted light is used to excite samples for PA imaging.By controlling the thickness of the gold-PDMS,we can control the center frequency in the US imaging.We experimentally analyzed the frequency of the produced US signal by the gold-PDMSfilm and compared it with the finite element analysis(FEA)method,where the experiments agree with the FEA results.This method is demonstrated by the experiments on phantoms and a mouse model.Our work provides a cost-effective methodology for simultaneous PA and US imaging.

HPDA/Zn as a CREB Inhibitor for Ultrasound imaging and Stabilization of Atherosclerosis Plaque+

Thrombosis,secondary to rupture of unstable plaque,is a fatal risk factor for myocardial infarction and ischemic stroke.At present,more novel methods are needed for the diagnosis and treatment of vulnerable plaque.Here,we report a hollow polydopamine/Zn(HPDA/Zn)ultrasound contrast agent.Through western-blot,Elisa,and other experiments,we found that in addition to having a good contrast-enhancement capability in ultrasound imaging in vitro and in vivo,HPDA/Zn also has the effect of reducing the expres-sion of CREB.CREB protein and its downstream-regulated proteins and factors are closely related to the stability of plaque.HPDA/Zn has the effect of reducing the expression of CREB protein,which leads to the decrease of expression of MMP-9,the regulatory pro-tein downstream of the CREB protein.In addition,it also reduces the secretion of inflammatory factors hs-CRP and IL-17A.Thus,HPDA/Zn can stabilize plaque by inhibiting CREB and reducing plaque vulnerable markers and inflammatory factors.In a word,HPDA/Zn is a kind of ultrasound contrast agent,which can stabilize plaques by inhibiting CREB protein.

Quantitative ultrasound brain imaging with multiscale deconvolutional waveform inversion

High-resolution images of human brain are critical for monitoring the neurological conditions in a portable and safe manner.Sound speed mapping of brain tissues provides unique information for such a purpose.In addition,it is particularly important for building digital human acoustic models,which form a reference for future ultrasound research.Conventional ultrasound modalities can hardly image the human brain at high spatial resolution inside the skull due to the strong impedance contrast between hard tissue and soft tissue.We carry out numerical experiments to demonstrate that the time-domain waveform inversion technique,originating from the geophysics community,is promising to deliver quantitative images of human brains within the skull at a sub-millimeter level by using ultra-sound signals.The successful implementation of such an approach to brain imaging requires the following items:signals of sub-megahertz frequencies transmitting across the inside of skull,an accurate numerical wave equation solver simulating the wave propagation,and well-designed inversion schemes to reconstruct the physical parameters of targeted model based on the optimization theory.Here we propose an innovative modality of multiscale deconvolutional waveform inversion that improves ultrasound imaging resolution,by evaluating the similarity between synthetic data and observed data through using limited length Wiener filter.We implement the proposed approach to iteratively update the parametric models of the human brain.The quantitative imaging method paves the way for building the accurate acoustic brain model to diagnose associated diseases,in a potentially more portable,more dynamic and safer way than magnetic resonance imaging and x-ray computed tomography.

Recent advances in applications of multimodal ultrasound-guided photoacoustic imaging technology

Photoacoustic imaging(PAI)is often performed simultaneously with ultrasound imaging and can provide functional and cellular information regarding the tissues in the anatomical markers of the imaging.This paper describes in detail the basic principles of photoacoustic/ultrasound(PA/US)imaging and its application in recent years.It includes near-infrared-region PA,photothermal,photodynamic,and multimode imaging techniques.Particular attention is given to the relationship between PAI and ultrasonic imaging;the latest high-frequency PA/US imaging of small animals,which involves not only B-mode,but also color Doppler mode,power Doppler mode,and nonlinear imaging mode;the ultrasonic model combined with PAI,including the formation of multimodal imaging;the preclinical imaging methods;and the most effective detection methods for clinical research for the future.

Artificial intelligence-based ultrasound imaging technologies for hepatic diseases

Ultrasound(US)imaging is a non-invasive,real-time,economical,and convenient imaging modality that has been widely used in diagnosing and treating hepatic diseases.Artificial intelligence(AI)technology can predict or make decisions based on the experience of clinical experts and knowledge obtained from training data.This technology can help clinicians improve the detection efficiency and evaluate hepatic diseases,promote clinical treatment of the liver,and predict the response of the liver after treatment.This review summarizes the current rapid development of US technology and related AI methods in the diagnosis and treatment of hepatic diseases.Covered topics include steatosis grading,fibrosis staging,detection of focal liver lesions,US image segmentation,multimodal image registration,and other applications.At present,the field of AI in US imaging is still in its early stages.With the future progress of AI technology,AI-based US imaging can further improve diagnosis,reduce medical costs,and optimize US-based clinical workflow.This technology has broad prospects for application to hepatic diseases.

Contrast-enhanced ultrasound Liver Imaging Reporting and Data System:Lights and shadows in hepatocellular carcinoma and cholangiocellular carcinoma diagnosis

BACKGROUND Contrast-enhanced ultrasound(CEUS)is considered a secondary examination compared to computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of hepatocellular carcinoma(HCC),due to the risk of misdiagnosing intrahepatic cholangiocarcinoma(ICC).The introduction of CEUS Liver Imaging Reporting and Data System(CEUS LI-RADS)might overcome this limitation.Even though data from the literature seems promising,its reliability in real-life context has not been well-established yet.AIM To test the accuracy of CEUS LI-RADS for correctly diagnosing HCC and ICC in cirrhosis.METHODS CEUS LI-RADS class was retrospectively assigned to 511 nodules identified in 269 patients suffering from liver cirrhosis.The diagnostic standard for all nodules was either biopsy(102 nodules)or CT/MRI(409 nodules).Common diagnostic accuracy indexes such as sensitivity,specificity,positive predictive value(PPV),and negative predictive value(NPV)were assessed for the following associations:CEUS LR-5 and HCC;CEUS LR-4 and 5 merged class and HCC;CEUS LR-M and ICC;and CEUS LR-3 and malignancy.The frequency of malignant lesions in CEUS LR-3 subgroups with different CEUS patterns was also determined.Inter-rater agreement for CEUS LI-RADS class assignment and for major CEUS pattern identification was evaluated.RESULTS CEUS LR-5 predicted HCC with a 67.6%sensitivity,97.7%specificity,and 99.3%PPV(P<0.001).The merging of LR-4 and 5 offered an improved 93.9%sensitivity in HCC diagnosis with a 94.3%specificity and 98.8%PPV(P<0.001).CEUS LR-M predicted ICC with a 91.3%sensitivity,96.7%specificity,and 99.6%NPV(P<0.001).CEUS LR-3 predominantly included benign lesions(only 28.8%of malignancies).In this class,the hypo-hypo pattern showed a much higher rate of malignant lesions(73.3%)than the iso-iso pattern(2.6%).Inter-rater agreement between internal raters for CEUS-LR class assignment was almost perfect(n=511,k=0.94,P<0.001),while the agreement among raters from separate centres was substantial(n=50,k=0.67,P<0.001).Agreement was stronger for arterial phase hyperenhancement(internal k=0.86,P<2.7×10-214;external k=0.8,P<0.001)than washout(internal k=0.79,P<1.6×10-202;external k=0.71,P<0.001).CONCLUSION CEUS LI-RADS is effective but can be improved by merging LR-4 and 5 to diagnose HCC and by splitting LR-3 into two subgroups to differentiate iso-iso nodules from other patterns.

Comparison of unenhanced magnetic resonance imaging and ultrasound in detecting very small hepatocellular carcinoma

BACKGROUND In hepatocellular carcinoma(HCC),detection and treatment prior to growth beyond 2 cm are important as a larger tumor size is more frequently associated with microvascular invasion and/or satellites.In the surveillance of very small HCC nodules(≤2 cm in maximum diameter,Barcelona clinical stage 0),we demonstrated that the tumor markers alpha-fetoprotein and PIVKA-Ⅱare not so useful.Therefore,we must survey with imaging modalities.The superiority of magnetic resonance imaging(MRI)over ultrasound(US)to detect HCC was confirmed in many studies.Although enhanced MRI is now performed to accurately diagnose HCC,in conventional clinical practice for HCC surveillance in liver diseases,unenhanced MRI is widely performed throughout the world.While,MRI has made marked improvements in recent years.AIM To make a comparison of unenhanced MRI and US in detecting very small HCC that was examined in the last ten years in patients in whom MRI and US examinations were performed nearly simultaneously.METHODS In 394 patients with very small HCC nodules,those who underwent MRI and US at nearly the same time(on the same day whenever possible or at least within 14 days of one another)at the first diagnosis of HCC were selected.The detection rate of HCC with unenhanced MRI was investigated and compared with that of unenhanced US.RESULTS The sensitivity of unenhanced MRI for detecting very small HCC was 95.1%(97/102,95%confidence interval:90.9-99.3)and that of unenhanced US was 69.6%(71/102,95%confidence interval:60.7-78.5).The sensitivity of unenhanced MRI for detecting very small HCC was significantly higher than that of unenhanced US(P<0.001).Regarding the location of HCC in the liver in patients in whom detection by US was unsuccessful,S7-8 was identified in 51.7%.CONCLUSION Currently,unenhanced MRI is a very useful tool for the surveillance of very small HCC in conventional clinical follow-up practice.

DEVELOPMENT OF A GENERIC ULTRASOUND VIBRO-ACOUSTIC IMAGING PLATFORM FOR TISSUE ELASTICITY AND VISCOSITY

Tissue elasticity and viscosity are always associated with pathological changes.As a new imaging method,ultrasound vibro-acoustic imaging is developed for quantitatively measuring tissue elasticity and viscosity which have important significance in early diagnosis of cancer.This paper developed an ultrasound vibro-acoustic imaging research platform mainly consisting of excitation part and detection part.The excitation transducer was focused at one location within the medium to generate harmonic vibration and shear wave propagation,and the detection transducer was applied to detect shear wave at other locations along shear wave propagation path using pulse-echo method.The received echoes were amplified,filtered,digitized and then processed by Kalman filter to estimate the vibration phase.According to the phase changes between different propagation locations,we estimated the shear wave speed,and then used it to calculate the tissue elasticity and viscosity.Preliminary phantom experiments based on this platform show results of phantom elasticity and viscosity close to literature values.Upcoming experiments are now in progress to obtain quantitative elasticity and viscosity in vitro tissue.

Lactoferrin-Conjugated Polylactic Acid Nanobubbles Encapsulated Perfluoropentane as a Contrast Agent for Ultrasound/Magnetic Resonance Dual-Modality Imaging

The development of contrast agents that can be activated by multiple modes is of great significance for tumor diagnosis.In this study,the lactoferrin(Lf)-conjugated polylactic acid(PLLA)nanobubbles(Lf-PLLA NBs)were used to encapsulate liquid perfluoropentane(PFP)with the double emulsion method,creating PFP loaded(PFP/Lf-PLLA)NBs for the ultrasound/magnetic resonance dual-modality imaging of subcutaneous tumor.The parti-cle diameter and stability of nanobubbles were investigated by photon correlation spectroscopy.The biocompat-ibility of nanobubbles was preliminarily evaluated by cell proliferation and migration assay,hemolysis rate,and blood biochemistry analysis.A B-mode clinical ultrasound real-time imaging system was used to perform ultra-sonic imaging in vivo.Magnetic resonance imaging in vivo was applied with a clinical 3.0 T magnetic resonance imaging(MRI)scanner system.The mean particle diameter of PFP/Lf-PLLA NBs was 320.2±4.1 nm with a low polydispersity index(PDI,0.145±0.025),and the NBs were negatively charged(−11.4±0.4 mV).The transmis-sion electron microscopy(TEM)results showed that PFP/Lf-PLLA NBs exhibited highly monodispersed and pos-sessed an obvious spherical structure of nanocapsules.Nanobubbles had good stability at 4°C.Different concentrations of the PFP/Lf-PLLA NBs solution had no effect on the cell in cytotoxicity and cell migration,and the results of hemolysis rate and blood biochemistry assay also indicated the good biocompatibility of NBs.On the ultrasound/magnetic resonance imaging of tumor-bearing mice,PFP/Lf-PLLA NBs showed signifi-cantly enhanced contrast ability of tumor tissue.Therefore,PFP/Lf-PLLA NBs had great potential to be a contrast agent for tumor dual-modality imaging in vivo.

A Transfer Learning Approach Based on Ultrasound Images for Liver Cancer Detection

The convolutional neural network(CNN)is one of the main algorithms that is applied to deep transfer learning for classifying two essential types of liver lesions;Hemangioma and hepatocellular carcinoma(HCC).Ultrasound images,which are commonly available and have low cost and low risk compared to computerized tomography(CT)scan images,will be used as input for the model.A total of 350 ultrasound images belonging to 59 patients are used.The number of images with HCC is 202 and 148,respectively.These images were collected from ultrasound cases.info(28 Hemangiomas patients and 11 HCC patients),the department of radiology,the University of Washington(7 HCC patients),the Atlas of ultrasound Germany(3 HCC patients),and Radiopedia and others(10 HCC patients).The ultrasound images are divided into 225,52,and 73 for training,validation,and testing.A data augmentation technique is used to enhance the validation performance.We proposed an approach based on ensembles of the best-selected deep transfer models from the on-the-shelf models:VGG16,VGG19,DenseNet,Inception,InceptionResNet,ResNet,and EfficientNet.After tuning both the feature extraction and the classification layers,the best models are selected.Validation accuracy is used for model tuning and selection.The accuracy,sensitivity,specificity and AUROC are used to evaluate the performance.The experiments are concluded in five stages.The first stage aims to evaluate the base model performance by training the on-the-shelf models.The best accu-racy obtained in the first stage is 83.5%.In the second stage,we augmented the data and retrained the on-the-shelf models with the augmented data.The best accuracy we obtained in the second stage was 86.3%.In the third stage,we tuned the feature extraction layers of the on-the-shelf models.The best accuracy obtained in the third stage is 89%.In the fourth stage,we fine-tuned the classification layer and obtained an accuracy of 93%as the best accuracy.In the fifth stage,we applied the ensemble approach using the best three-performing models and obtained an accuracy,specificity,sensitivity,and AUROC of 94%,93.7%,95.1%,and 0.944,respectively.

Vault predicting after implantable collamer lens implantation using random forest network based on different features in ultrasound biomicroscopy images

AIM:To analyze ultrasound biomicroscopy(UBM)images using random forest network to find new features to make predictions about vault after implantable collamer lens(ICL)implantation.METHODS:A total of 450 UBM images were collected from the Lixiang Eye Hospital to provide the patient’s preoperative parameters as well as the vault of the ICL after implantation.The vault was set as the prediction target,and the input elements were mainly ciliary sulcus shape parameters,which included 6 angular parameters,2 area parameters,and 2 parameters,distance between ciliary sulci,and anterior chamber height.A random forest regression model was applied to predict the vault,with the number of base estimators(n_estimators)of 2000,the maximum tree depth(max_depth)of 17,the number of tree features(max_features)of Auto,and the random state(random_state)of 40.0.RESULTS:Among the parameters selected in this study,the distance between ciliary sulci had a greater importance proportion,reaching 52%before parameter optimization is performed,and other features had less influence,with an importance proportion of about 5%.The importance of the distance between the ciliary sulci increased to 53% after parameter optimization,and the importance of angle 3 and area 1 increased to 5% and 8%respectively,while the importance of the other parameters remained unchanged,and the distance between the ciliary sulci was considered the most important feature.Other features,although they accounted for a relatively small proportion,also had an impact on the vault prediction.After parameter optimization,the best prediction results were obtained,with a predicted mean value of 763.688μm and an actual mean value of 776.9304μm.The R²was 0.4456 and the root mean square error was 201.5166.CONCLUSION:A study based on UBM images using random forest network can be performed for prediction of the vault after ICL implantation and can provide some reference for ICL size selection.

Image-Based Ultrasound Speed Estimation: Phantom and Human Liver Studies

Purpose: A novel image-based method for speed of sound (SoS) estimation is proposed and experimentally validated on a tissue-mimicking ultrasound phantom and normal human liver in vivo using linear and curved array transducers. Methods: When the beamforming SoS settings are adjusted to match the real tissue’s SoS, the ultrasound image at regions of interest will be in focus and the image quality will be optimal. Based on this principle, both a tissue-mimicking ultrasound phantom and normal human liver in vivo were used in this study. Ultrasound image was acquired using different SoS settings in beamforming channels ranging from 1420 m/sec to 1600 m/sec. Two regions of interest (ROIs) were selected. One was in a fully developed speckle region, while the other contained specular reflectors. We evaluated the image quality of these two ROIs in images acquired at different SoS settings in beamforming channels by using the normalized autocorrelation function (ACF) of the image data. The values of the normalized ACF at a specific lag as a function of the SoS setting were computed. Subsequently, the soft tissue’s SoS was determined from the SoS setting at the minimum value of the normalized ACF. Results: The value of the ACF as a function of the SoS setting can be computed for phantom and human liver images. SoS in soft tissue can be determined from the SoS setting at the minimum value of the normalized ACF. The estimation results show that the SoS of the tissue-mimicking phantom is 1460 m/sec, which is consistent with the phantom manufacturer’s specification, and the SoS of the normal human liver is 1540 m/sec, which is within the range of the SoS in a healthy human liver in vivo. Conclusion: Soft tissue’s SoS can be determined by analyzing the normalized ACF of ultrasound images. The method is based on searching for a minimum of the normalized ACF of ultrasound image data with a specific lag among different SoS settings in beamforming channels.

Secure Transmission of Compressed Medical Image Sequences on Communication Networks Using Motion Vector Watermarking

Medical imaging plays a key role within modern hospital management systems for diagnostic purposes.Compression methodologies are extensively employed to mitigate storage demands and enhance transmission speed,all while upholding image quality.Moreover,an increasing number of hospitals are embracing cloud computing for patient data storage,necessitating meticulous scrutiny of server security and privacy protocols.Nevertheless,considering the widespread availability of multimedia tools,the preservation of digital data integrity surpasses the significance of compression alone.In response to this concern,we propose a secure storage and transmission solution for compressed medical image sequences,such as ultrasound images,utilizing a motion vector watermarking scheme.The watermark is generated employing an error-correcting code known as Bose-Chaudhuri-Hocquenghem(BCH)and is subsequently embedded into the compressed sequence via block-based motion vectors.In the process of watermark embedding,motion vectors are selected based on their magnitude and phase angle.When embedding watermarks,no specific spatial area,such as a region of interest(ROI),is used in the images.The embedding of watermark bits is dependent on motion vectors.Although reversible watermarking allows the restoration of the original image sequences,we use the irreversible watermarking method.The reason for this is that the use of reversible watermarks may impede the claims of ownership and legal rights.The restoration of original data or images may call into question ownership or other legal claims.The peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)serve as metrics for evaluating the watermarked image quality.Across all images,the PSNR value exceeds 46 dB,and the SSIM value exceeds 0.92.Experimental results substantiate the efficacy of the proposed technique in preserving data integrity.

Adaptive Virtual Source Imaging Using the Sequence Intensity Factor:Simulation and Experimental Study

Virtual source(VS)imaging has been proposed to improve image resolution in medical ultrasound imaging.However,VS obtains a limited contrast due to the non-adaptive delay-and-sum(DAS)beamforming.To improve the image contrast and provide an enhanced resolution,adaptive weighting algorithms were applied in VS imaging.In this paper,we proposed an adjustable generalized coherence factor(aGCF)for the synthetic aperture sequential beamforming(SASB)ofVS imaging to improve image quality.The value of aGCF is adjusted by a sequence intensity factor(SIF)that is defined as the ratio between the effective low resolution scan lines(LRLs)intensity and total LRLs strength.The aGCF-weighted VS(aGCF-VS)images were compared with standard VS images and GCF-weighted VS(GCF-VS)images.Simulation and experimental results demonstrated that the contrast ratio(CR)and contrastto-noise ratio(CNR)of aGCF-VS are greatly improved,compared with standard VS imaging.And in comparison with GCF-VS,aGCF-VS can obtain better CNR and speckle signal-to-noise ratio(sSNR)whilemaintaining similar CR.Therefore,aGCF is suitable for VS imaging to improve contrast and preserve speckle pattern.

HIGH RESOLUTION REAL-TIME MEDICAL IMAGING BASED ON PARALLEL BEAMFORMING TECHNIQUE

Improvement of frame-rate is very important for high quality ultrasound imaging of fast-moving structures.It is also one of the key technologies of Three-Dimension(3-D) real-time medical imaging.In this paper,we have demonstrated a beamforming method which gives imaging frame-rate increment without sacrificing the quality of medical images.By using wider and fewer transmit beams in combination with four narrower parallel receive beams,potentially increasing the imaging frame-rate by a factor four.Through employing full transmit aperture,controlling the mainlobe width,and suppressing sidelobes of angular responses,the inherent gain loss of normal parallel beamfomer can be compensated in the maximal degree.The noise and interference signals also can be suppressed effectively.Finally,we show comparable lateral resolution and contrast of ultrasound images to normal single widow weighting beamformer on simulated phantoms of point targets,cyst and fetus of 12th week.As the computational cost is linear with the number of array elements and the same with Delay And Sum(DAS) beamformers,this method has great ad-vantages of possibility for high frame-rate real-time applications.

Implementation of lung ultrasound in the triage of pregnant women during the SARS-CoV-2 pandemics

Lung ultrasound(US)has been shown that it is able to detect interstitial lung disease,subpleural consolidations and acute respiratory distress syndrome in clinical and physical studies that assess its role in upper respiratory infections.It is used worldwide in the coronavirus disease 2019(COVID-19)outbreak and the effectiveness has been assessed in several studies.Fast diagnosis of COVID-19 is essential in deciding for patient isolation,clinical care and reducing transmission.Imaging the lung and pleura by ultrasound is efficient,cost-effective,and safe and it is recognized as rapid,repeatable,and reliable.Obstetricians are already using the US and are quite proficient in doing so.During the pandemic,performing lung US(LUS)right after the fetal assessment until reverse transcription polymerase chain reaction results are obtained,particularly in settings that have a centralized testing center,was found feasible for the prediction of the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection.The use of LUS is efficient in the triage and monitoring of pregnant women.Clinicians dealing with pregnant women should consider LUS as the first-line diagnostic tool in pregnant women during the SARS-CoV-2 pandemic.

Multi-Level Fusion in Ultrasound for Cancer Detection Based on Uniform LBP Features

Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging.Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise,an enhanced technique is not achieved.The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern(LBP)and filtered noise reduction.To surmount the above limitations and achieve the aim of the study,a new descriptor that enhances the LBP features based on the new threshold has been proposed.This paper proposes a multi-level fusion scheme for the auto-classification of the static ultrasound images of breast cancer,which was attained in two stages.First,several images were generated from a single image using the pre-processing method.Themedian andWiener filterswere utilized to lessen the speckle noise and enhance the ultrasound image texture.This strategy allowed the extraction of a powerful feature by reducing the overlap between the benign and malignant image classes.Second,the fusion mechanism allowed the production of diverse features from different filtered images.The feasibility of using the LBP-based texture feature to categorize the ultrasound images was demonstrated.The effectiveness of the proposed scheme is tested on 250 ultrasound images comprising 100 and 150 benign and malignant images,respectively.The proposed method achieved very high accuracy(98%),sensitivity(98%),and specificity(99%).As a result,the fusion process that can help achieve a powerful decision based on different features produced from different filtered images improved the results of the new descriptor of LBP features in terms of accuracy,sensitivity,and specificity.