A Modified CycleGAN for Multi-Organ Ultrasound Image Enhancement via Unpaired Pre-Training

Handheld ultrasound devices are known for their portability and affordability,making them widely utilized in underdeveloped areas and community healthcare for rapid diagnosis and early screening.However,the image quality of handheld ultrasound devices is not always satisfactory due to the limited equipment size,which hinders accurate diagnoses by doctors.At the same time,paired ultrasound images are difficult to obtain from the clinic because imaging process is complicated.Therefore,we propose a modified cycle generative adversarial network(cycleGAN) for ultrasound image enhancement from multiple organs via unpaired pre-training.We introduce an ultrasound image pre-training method that does not require paired images,alleviating the requirement for large-scale paired datasets.We also propose an enhanced block with different structures in the pre-training and fine-tuning phases,which can help achieve the goals of different training phases.To improve the robustness of the model,we add Gaussian noise to the training images as data augmentation.Our approach is effective in obtaining the best quantitative evaluation results using a small number of parameters and less training costs to improve the quality of handheld ultrasound devices.

Automatic Fetal Segmentation Designed on Computer-Aided Detection with Ultrasound Images

In the present research,we describe a computer-aided detection(CAD)method aimed at automatic fetal head circumference(HC)measurement in 2D ultrasonography pictures during all trimesters of pregnancy.The HC might be utilized toward determining gestational age and tracking fetal development.This automated approach is particularly valuable in low-resource settings where access to trained sonographers is limited.The CAD system is divided into two steps:to begin,Haar-like characteristics were extracted from ultrasound pictures in order to train a classifier using random forests to find the fetal skull.We identified the HC using dynamic programming,an elliptical fit,and a Hough transform.The computer-aided detection(CAD)program was well-trained on 999 pictures(HC18 challenge data source),and then verified on 335 photos from all trimesters in an independent test set.A skilled sonographer and an expert in medicine personally marked the test set.We used the crown-rump length(CRL)measurement to calculate the reference gestational age(GA).In the first,second,and third trimesters,the median difference between the standard GA and the GA calculated by the skilled sonographer stayed at 0.7±2.7,0.0±4.5,and 2.0±12.0 days,respectively.The regular duration variance between the baseline GA and the health investigator’s GA remained 1.5±3.0,1.9±5.0,and 4.0±14 a couple of days.The mean variance between the standard GA and the CAD system’s GA remained between 0.5 and 5.0,with an additional variation of 2.9 to 12.5 days.The outcomes reveal that the computer-aided detection(CAD)program outperforms an expert sonographer.When paired with the classifications reported in the literature,the provided system achieves results that are comparable or even better.We have assessed and scheduled this computerized approach for HC evaluation,which includes information from all trimesters of gestation.

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.

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.

Structured magnetic resonance imaging and endoanal ultrasound anal fistulas reporting template(SMART):An interdisciplinary Delphi consensus

BACKGROUND There is still considerable heterogeneity regarding which features of cryptoglandular anal fistula on magnetic resonance imaging(MRI)and endoanal ultrasound(EAUS)are relevant to surgical decision-making.As a con-sequence,the quality and completeness of the report are highly dependent on the training and experience of the examiners.AIM To develop a structured MRI and EAUS template(SMART)reporting the minimum dataset of information for the treatment of anal fistulas.METHODS This modified Delphi survey based on the RAND-UCLA appropriateness for consensus-building was conducted between May and August 2023.One hundred and fifty-one articles selected from a systematic review of the lite-rature formed the database to generate the evidence-based statements for the Delphi study.Fourteen questions were anonymously voted by an interdisciplinary multidisciplinary group for a maximum of three iterative rounds.The degree of agreement was scored on a numeric 0–10 scale.Group consensus was defined as a score≥8 for≥80%of the panelists.RESULTS Eleven scientific societies(3 radiological and 8 surgical)endorsed the study.After three rounds of voting,the experts(69 colorectal surgeons,23 radiologists,2 anatomists,and 1 gastroenterologist)achieved consensus for 12 of 14 statements(85.7%).Based on the results of the Delphi process,the six following features of anal fistulas were included in the SMART:Primary tract,secondary extension,internal opening,presence of collection,coexisting le-sions,and sphincters morphology.CONCLUSION A structured template,SMART,was developed to standardize imaging reporting of fistula-in-ano in a simple,systematic,time-efficient way,providing the minimum dataset of information and visual diagram useful to refer-ring physicians.

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.

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.

Data augmentation of ultrasound imaging for non-invasive white blood cell in vitro peritoneal dialysis

The limited amount of data in the healthcare domain and the necessity of training samples for increased performance of deep learning models is a recurrent challenge,especially in medical imaging.Newborn Solutions aims to enhance its non-invasive white blood cell counting device,Neosonics,by creating synthetic in vitro ultrasound images to facilitate a more efficient image generation process.This study addresses the data scarcity issue by designing and evaluating a continuous scalar conditional Generative Adversarial Network(GAN)to augment in vitro peritoneal dialysis ultrasound images,increasing both the volume and variability of training samples.The developed GAN architecture incorporates novel design features:varying kernel sizes in the generator’s transposed convolutional layers and a latent intermediate space,projecting noise and condition values for enhanced image resolution and specificity.The experimental results show that the GAN successfully generated diverse images of high visual quality,closely resembling real ultrasound samples.While visual results were promising,the use of GAN-based data augmentation did not consistently improve the performance of an image regressor in distinguishing features specific to varied white blood cell concentrations.Ultimately,while this continuous scalar conditional GAN model made strides in generating realistic images,further work is needed to achieve consistent gains in regression tasks,aiming for robust model generalization.

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.

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.

Using shapes correlation for active contour segmentation of uterine fibroid ultrasound images in computer-aided therapy

Segmenting the lesion regions from the ultrasound （US） images is an important step in the intra-operative planning of some computer-aided therapies. High-Intensity Focused Ultrasound （HIFU）, as a popular computer-aided therapy, has been widely used in the treatment of uterine fibroids. However, such segmentation in HIFU remains challenge for two reasons： （1） the blurry or missing boundaries of lesion regions in the HIFU images and （2） the deformation of uterine fibroids caused by the patient＇s breathing or an external force during the US imaging process, which can lead to complex shapes of lesion regions. These factors have prevented classical active contour-based segmentation methods from yielding desired results for uterine fibroids in US images. In this paper, a novel active contour-based segmentation method is proposed, which utilizes the correlation information of target shapes among a sequence of images as prior knowledge to aid the existing active contour method. This prior knowledge can be interpreted as a unsupervised clustering of shapes prior modeling. Meanwhile, it is also proved that the shapes correlation has the low-rank property in a linear space, and the theory of matrix recovery is used as an effective tool to impose the proposed prior on an existing active contour model. Finally, an accurate method is developed to solve the proposed model by using the Augmented Lagrange Multiplier （ALM）. Experimental results from both synthetic and clinical uterine fibroids US image sequences demonstrate that the proposed method can consistently improve the performance of active contour models and increase the robustness against missing or misleading boundaries, and can greatly improve the efficiency of HIFU therapy.

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.

Value of ultrasound and magnetic resonance imaging combined with tumor markers in the diagnosis of ovarian tumors

BACKGROUND Compare the diagnostic performance of ultrasound(US),magnetic resonance imaging(MRI),and serum tumor markers alone or in combination for detecting ovarian tumors.AIM To investigate the diagnostic value of US,MRI combined with tumor markers in ovarian tumors.METHODS The data of 110 patients with ovarian tumors,confirmed by surgery and pathology,were collected in our hospital from February 2018 to May 2023.The dataset included 60 cases of benign tumors and 50 cases of malignant tumors.Prior to surgery,all patients underwent preoperative US and MRI examinations,as well as serum tumor marker tests[carbohydrate antigen 125(CA125),human epididymis protein 4(HE4)].The aim of the study was to compare the diagnostic performance of these three methods individually and in combination for ovarian tumors.RESULTS This study found statistically significant differences in the ultrasonic imaging characteristics between benign and malignant tumors.These differences include echo characteristics,presence or absence of a capsule,blood flow resistance index,clear tumor shape,and blood flow signal display rate(P<0.05).The apparent diffusion coefficient values of the solid and cystic parts in benign tumors were found to be higher compared to malignant tumors(P<0.05).Additionally,the time-intensity curve image features of benign and malignant tumors showed significant statistical differences(P<0.05).The levels of serum CA125 and HE4 in benign tumors were lower than those in malignant tumors(P<0.05).The combined use of US,MRI,and tumor markers in the diagnosis of ovarian tumors demonstrates higher accuracy,sensitivity,and specificity compared to using each method individually(P<0.05).CONCLUSION US,MRI,and tumor markers each have their own advantages and disadvantages when it comes to diagnosing ovarian tumors.However,by combining these three methods,we can significantly enhance the accuracy of ovarian tumor diagnosis,enabling early detection and identification of the tumor’s nature,and providing valuable guidance for clinical treatment.

Magnetic resonance image-guided versus ultrasound-guided high-intensity focused ultrasound in the treatment of breast cancer

Image-guided high-intensity focused ultrasound (HIFU) has been used for more than ten years, primarily in the treatment of liver and prostate cancers. HIFU has the advantages of precise cancer ablation and excellent protection of healthy tissue. Breast cancer is a common cancer in women. HIFU therapy, in combination with other therapies, has the potential to improve both oncologic and cosmetic outcomes for breast cancer patients by providing a curative therapy that conserves mammary shape. Currently, HIFU therapy is not commonly used in breast cancer treatment, and efforts to promote the application of HIFU is expected. In this article, we compare different image-guided models for HIFU and reviewed the status, drawbacks, and potential of HIFU therapy for breast cancer.

Imaging and endoscopic tools in pediatric inflammatory bowel disease: What’s new?

Pediatric inflammatory bowel disease(IBD)is a chronic inflammatory disorder,with increasing incidence and prevalence worldwide.There have been recent advances in imaging and endoscopic technology for disease diagnosis,treatment,and monitoring.Intestinal ultrasound,including transabdominal,transperineal,and endoscopic,has been emerging for the assessment of transmural bowel inflammation and disease complications(e.g.,fistula,abscess).Aside from surgery,IBD-related intestinal strictures now have endoscopic treatment options including through-the-scope balloon dilatation,injection,and needle knife stricturotomy and new evaluation tools such as endoscopic functional lumen imaging probe.Unsedated transnasal endoscopy may have a role in patients with upper gastrointestinal Crohn’s disease or those with IBD with new upper gastrointestinal symptoms.Improvements to dysplasia screening in pediatric patients with longstanding colonic disease or primary sclerosing cholangitis hold promise with the addition of virtual chromoendoscopy and ongoing research in the field of artificial intelligence-assisted endoscopic detection.Artificial intelligence and machine learning is a rapidly evolving field,with goals of further personalizing IBD diagnosis and treatment selection as well as prognostication.This review summarized these advancements,focusing on pediatric patients with IBD.

Diagnostic Accuracy and Complication Rates of Fusion Images Created Using Real-Time Ultrasound with CT for Identification of Peripheral Lung Lesions in Patients Undergoing Biopsy

Background: Fusion image improves lesion detectability and can be an effective tool for percutaneous ultrasound (US)-guide procedure. We describe the clinical benefit of US-guided lung biopsy using fusion image. Purpose: To retrospectively compare the diagnostic accuracy and complication rates of US-guided lung biopsy with B-mode alone and those of a fusion image created using real-time US and computed tomography (CT). Materials and Methods: Between September, 2013 and September, 2016, 50 peripheral lung lesions in 50 patients (40 males, 10 females;median, 74 years old) were performed by US-guided percutaneous cutting needle biopsy using the B-mode alone or fusion image. Final diagnoses were based on surgical outcomes or clinical follow-up results for at least 12 months after biopsy. To assess prebiopsy characteristics, all lesions were divided into two groups: group 1 (identification on B-mode) and group 2 (identification on fusion image). Results: Of 50 peripheral lesions, 40 lesions (80%) were detected by means of B-mode alone (group 1), and 10 lesions (20%) were identified by fusion image (group 2). The diagnostic accuracy of group 1 was 90% (36/40 lesions), and the diagnostic accuracy of group 2 was 100% (10/10 lesions). Nodule type and the size of the lesions showed significant group wise differences (p Conclusion: Fusion images created using real-time US and CT may be useful for identification of the minimal size of potential target lung lesions and may be more suitable for improved yields with US-guided lung biopsy.

A Study on the Temperature Correlation of B-mode Ultrasonic Image Gray for Noninvasive Temperature Monitoring in Hyperthermia

This paper deals with the temperature correlation of gray scale of B-mode ultrasound image from heated tissue. In this study, many in-vitro fresh pig livers are heated in a temperature range from 28 ℃ to 45℃, from which a series of B-mode ultrasonic images of livers were obtained. The gray-value is evaluated from the ultrasound images respectively. A correlation of the mean gray value of the selected regions (12×12 pixels) in B-mode ultrasonic images of liver and its temperature was pointed out. And the experiment results agreed the evaluation well. And it is possible to monitor the tissue temperature changing in hyperthermia using this correlation.

Design and Experiments of Ultrasound Image-Guided Multi-DOF Robot System for Brachytherapy

To implant radioactive seeds through a needle precisely and safely, a novel multi-DOF surgical robotic system is presented in this paper for percutaneous prostate intervention through the patient’s perineum under real-time ultrasound image guidance. The proposed robot, which is designed with 9-DOF, consists of a 3-DOF automatic location platform for position adjustment, 2-DOF for automatic ultrasonic probe adjustment mounted with electromagnetic trackers, and 4-DOF for manually adjusting the guided template. Meanwhile, a new registration method based on the quaternion algorithm and least square method is developed, and the needle insertion is performed under the real-time guidance of a navigation system. Furthermore, the robot system has undergone some preliminary experiments with a laser tracker to evaluate the repeatability and accuracy of the robot system. The location error of the puncture needle tip can be controlled under 0.7 mm in air for the whole robotic system. The acquired results endorse the precision of the robot system for prostate seed implantation brachytherapy. © 2017, Tianjin University and Springer-Verlag GmbH Germany.

Ultrasound diagnosis of congenital Morgagni hernias: Ten years of experience at two Chinese centers

BACKGROUND Morgagni hernias are rare anomalies that are easily misdiagnosed or missed.AIM To summarize the ultrasound(US)imaging characteristics of Morgagni hernias through a comparison of imaging and surgical results.METHODS The records of children with Morgagni hernias who were hospitalized at two hospitals between January 2013 and November 2023 were retrospectively re-viewed in terms of clinical findings,US features,and operative details.RESULTS Between 2013 and 2023,we observed nine(five male and four female)children with Morgagni hernias.Upper abdominal scanning revealed a widening of the prehepatic space,with an abnormal channel extending from the xiphoid process to the right or left side of the thoracic cavity.The channel had intestinal duct and intestinal gas echoes.Hernia contents were found in the transverse colon(n=6),the colon and small intestine(n=2),and the colon and stomach(n=1).Among the patients,seven had a right-sided lesion,two had a left-sided lesion,and all of them had hernial sacs.CONCLUSION US imaging can accurately determine the location,extent,and content of Morgagni hernias.For suspected Mor-gagni hernias,we recommend performing sonographic screening first.

Linear endoscopic ultrasound:Current uses and future perspectives in mediastinal examination

This editorial elaborates on the current and future applications of linear endoscopic ultrasound(EUS),a substantial diagnostic and therapeutic modality for various anatomical regions.The scope of endosonographic assessment is broad and,among other factors,allows for the evaluation of the mediastinal anatomy and related pathologies,such as mediastinal lymphadenopathy and the staging of central malignant lung lesions.Moreover,EUS assessment has proven more accurate in detecting small lesions missed by standard imaging examinations,such as computed tomography or magnetic resonance imaging.We focus on its current uses in the mediastinum,including lung and esophageal cancer staging,as well as evaluating mediastinal lymphadenopathy and submucosal lesions.The editorial also explores future perspectives of EUS in mediastinal examination,including ultrasound-guided therapies,artificial intelligence integration,advancements in mediastinal modalities,and improved diagnostic approaches for various mediastinal lesions.