Ultrasound Imaging Method Research Articles

Trends in dentomaxillofacial radiology.

Oral and maxillofacial diagnostic imaging is of paramount importance in dental clinical diagnosis, treatment planning, and follow-up procedures. Periapical radiographic examination and numerous panoramic systems are used in routine clinical dental practice. Cone beam CT is widely used and currently the method of choice in oral and maxillofacial implantology, endodontics, maxillofacial surgery, periodontics, degenerative temporomandibular joint disease, orthodontics, airway studies, sleep disorders, and forensic dentistry. Another innovative laboratory research tool that offers three-dimensional (3D) detailed high-resolution images of in vitro teeth and neighboring structures with submicrometric accuracy is microcomputed tomography. Ultra-high radiation doses, long scanning times, and high costs preclude its routine clinical use. In response to the high demand for a technique that could provide real-time images using a cost-effective, rapid, user-friendly, and portable technique without ionizing radiation, some authors proposed ultrasound imaging methods as an alternative to X-ray imaging techniques. Ultrasonography can be used in the dentomaxillofacial region for various diagnostic purposes such as salivary gland and superficial tissue examination. Recently, dedicated dental magnetic resonance imaging with appropriate software, hardware, sequences, and field of view tailored to fit dentomaxillofacial anatomy was introduced. Lately, 3D printing technologies and their application in dentistry has attracted attention. During 3D printing a given material is added in successive layers to create a 3D object. The application of this technology has the potential to decrease operation time and minimize operator bias and the possibility of procedural errors. Another hot topic regarding dentomaxillofacial radiology is artificial intelligence, which is a field related to computer science dedicated to developing systems or machines that can perform tasks traditionally associated with human intelligence. It is obvious that further investigation and research in the field of dentomaxillofacial radiology will make great contributions to diagnostic imaging for various dental specialties.

Combined Nanodrops Imaging and Ultrasound Localization Microscopy for Detecting Intracerebral Hemorrhage.

Advanced imaging methods are crucial for understanding stroke mechanisms and discovering effective treatments to reduce bleeding and enhance recovery. In pre-clinical in vivo stroke imaging, MRI, CT and optical imaging are commonly used to evaluate stroke outcomes in rodent models. However, MRI and CT have limited spatial resolution for rodent brains, and optical imaging is hindered by limited imaging depth of penetration. Here we introduce a novel contrast-enhanced ultrasound imaging method to overcome these challenges and characterize intracerebral hemorrhage with unique insights. We combined microbubble-based ultrasound localization microscopy (ULM) and nanodrop (ND)-based vessel leakage imaging to achieve simultaneous microvascular imaging and hemorrhage detection. ULM maps brain-wide cerebral vasculature with high spatial resolution and identifies microvascular impairments around hemorrhagic areas. NDs are sub-micron liquid-core particles that can extravasate due to blood-brain barrier breakdown, serving as positive contrast agents to detect hemorrhage sites. Our findings demonstrate that NDs could effectively accumulate in the hemorrhagic site and reveal the location of the bleeding areas upon activation by focused ultrasound beams. ULM further reveals the microvascular damage manifested in the form of reduced vascularity and decreased blood flow velocity across areas affected by the hemorrhagic stroke. The results demonstrate that sequential ULM combined with ND imaging is a useful imaging tool for basic in vivo research in stroke with rodent models where brain-wide detection of active bleeding and microvascular impairment are essential.

An unsupervised automatic texture classification method for ultrasound images of thyroid nodules

Objective.Ultrasound is the predominant modality in medical practice for evaluating thyroid nodules. Currently, diagnosis is typically based on textural information. This study aims to develop an automated texture classification approach to aid physicians in interpreting ultrasound images of thyroid nodules. However, there is currently a scarcity of pixel-level labeled datasets for the texture classes of thyroid nodules. The labeling of such datasets relies on professional and experienced doctors, requiring a significant amount of manpower. Therefore, the objective of this study is to develop an unsupervised method for classifying nodule textures.Approach.Firstly, we develop a spatial mapping network to transform the one-dimensional pixel value space into a high-dimensional space to extract comprehensive feature information. Subsequently, we outline the principles of feature selection that are suitable for clustering. Then we propose a pixel-level clustering algorithm with a region growth pattern, and a distance evaluation method for texture sets among different nodules is established.Main results.Our algorithm achieves a pixel-level classification accuracy of 0.931 for the cystic and solid region, 0.870 for the hypoechoic region, 0.959 for the isoechoic region, and 0.961 for the hyperechoic region. The efficacy of our algorithm and its concordance with human observation have been demonstrated. Furthermore, we conduct calculations and visualize the distribution of different textures in benign and malignant nodules.Significance.This method can be used for the automatic generation of pixel-level labels of thyroid nodule texture, aiding in the construction of texture datasets, and offering image analysis information for medical professionals.

SAFT imaging for high-density polyethylene using quasi-static components of ultrasonic longitudinal waves

High-density polyethylene (HDPE) is extensively utilized across various industries, including nuclear power, primarily for its exceptional properties. However, there are challenges with traditional linear ultrasound imaging systems due to the significant thicknesses and the highly attenuative of HDPE. High-frequency carrier waves can offer better imaging resolution but also suffer higher acoustic attenuation, which limits the propagation distance of primary longitudinal waves (PLW) and makes it difficult to detect defects within thick HDPEs. On the other hand, using low-frequency PLW for defect detection presents challenges in resolution despite lower attenuation and longer propagation distances. This study proposes a defect imaging method for HDPEs by using quasi-static components (QSC) generated along with high-frequency fundamental wave propagation because of the nonlinear effect. The QSC has the advantage of low attenuation because its carrier frequency is zero, which can propagate a long distance in a high acoustic attention medium like HDPE. A nonlinear ultrasonic imaging approach combining the QSC and synthetic aperture focusing technique is proposed for defect imaging in HDPEs. Experiments on HDPEs with single and multiple defects are conducted to verify the performance of the proposed method. For comparison, the imaging results using traditional linear ultrasounds with high (2.5 MHz) and low (0.5 MHz) carrier frequencies are also provided. The results show the proposed method has better imaging performance over traditional linear ultrasound imaging methods for defect defections in high acoustic attention medium.

Operando visualisation of lithium plating by ultrasound imaging of battery cells.

While developing battery cells, the achievement of fast-charging capability is heavily dependent on avoiding metallic plating on the anode surface (i.e., lithium plating in lithium-ion cells). However, this objective hinges on the effectiveness of plating detection. Currently, measurement techniques are either inadequate in providing spatial, temporal, or causal information, incur high costs when employing, e.g., neutron imaging, or are lengthy due to destructive post-mortem examinations that additionally lack operando data. In this work, we demonstrate an ultrasound imaging method for operando visualization of the interior of a multi-layer pouch battery cell. Here we show that this method can non-invasively visualize the formation and stripping of lithium plating during cycling. Extensive reference electrode studies and ex-situ analysis verify the effectiveness of our method for plating detection. Ultimately, this work enables researchers and industry to significantly accelerate the development of new cell technologies and their optimized utilization.

Lipoic Acid Nanoparticles Exert Effective Antiatherosclerosis Effects through Anti-Inflammatory and Antioxidant Pathways.

Oxidative stress and inflammation are key pathological features of atherosclerotic plaques. Numerous nanomedicines have been developed to alleviate oxidative stress and reduce inflammation within plaques. However, nonbioactive carrier materials reduce the bioavailability of nanomedicines and may pose potential biological toxicity. In this study, we utilized the unique amphiphilic chemical structure of lipoic acid (LA) to prepare LA nanoparticles (LA NPs) via a self-assembly method. Leveraging the inherent anti-inflammatory and antioxidant properties of LA, these NPs were used for the treatment of atherosclerosis. In an inflammatory macrophage model, LA NPs exhibited superior anti-inflammatory activity compared to free LA. Through ultrasound imaging and pathological methods, we discovered that LA NPs demonstrated nice antiatherosclerotic effects in an atherosclerotic mice model. Immunofluorescence analysis further indicated that the antiatherosclerotic effects of LA were associated with the alleviation of oxidative stress within the plaques, reduced macrophage infiltration, and downregulation of inflammatory cytokine levels. Therefore, LA NPs offer a promising therapeutic strategy for the treatment of atherosclerosis.

Body Contouring With Electromagnetic Treatment Plus Radiofrequency: A Review.

Electromagnetic treatments have recently been combined with radiofrequency to reduce the fat layer and simultaneously increase muscle thickness. Studies report treatment efficacy, using photographs and imaging methods. A literature review was conducted. Measurement data were tabulated. The EmSculpt Neo device (BTL Industries, Boston, MA) was used in all studies. Seven studies evaluated the fat thickness and muscle thickness using either ultrasound (1 study) or magnetic resonance imaging methods (6 studies). The abdomen was treated in 2 studies. The other studies treated the lateral thighs, flanks, buttocks, inner thighs, and upper arms. The mean reduction in fat thickness was 8.0 mm, and the mean increase in muscle thickness was 5.5 mm. All 7 studies were authored by BTL medical advisors. Measured changes were likely within the margin of error and of questionable clinical relevance. Few adverse events were reported. Photographs were frequently not standardized, showing changes in treated areas, but unexplained changes in nontreated areas as well. The magnetic resonance imaging scans were affected by differing levels of the axial slice. Measured changes were incremental and likely not indicative of a true treatment effect. The scientific basis for efficacy is tenuous, both for fat reduction and muscle hypertrophy. A scientific evaluation of the results fails to produce reliable evidence of a clinically meaningful result for this combination energy-based treatment. Financial conflict is a major factor. Liposuction remains the gold standard.

Real-time ultrasound AR 3D visualization toward better topological structure perception for hepatobiliary surgery.

Ultrasound serves as a crucial intraoperative imaging tool for hepatobiliary surgeons, enabling the identification of complex anatomical structures like blood vessels, bile ducts, and lesions. However, the reliance on manual mental reconstruction of 3D topologies from 2D ultrasound images presents significant challenges, leading to a pressing need for tools to assist surgeons with real-time identification of 3D topological anatomy. We propose a real-time ultrasound AR 3D visualization method for intraoperative 2D ultrasound imaging. Our system leverages backward alpha blending to integrate multi-planar ultrasound data effectively. To ensure continuity between 2D ultrasound planes, we employ spatial smoothing techniques to interpolate the widely spaced ultrasound planes. A dynamic 3D transfer function is also developed to enhance spatial representation through color differentiation. Comparative experiments involving our AR visualization of 3D ultrasound, alongside AR visualization of 2D ultrasound and 2D visualization of 3D ultrasound, demonstrated that the proposed method significantly reduced operational time(110.25 ± 27.83s compared to 292 ± 146.63s and 365.25 ± 131.62s), improved depth perception and comprehension of complex topologies, contributing to reduced pressure and increased personal satisfaction among users. Quantitative experimental results and feedback from both novice and experienced physicians highlight our system's exceptional ability to enhance the understanding of complex topological anatomy. This improvement is crucial for accurate ultrasound diagnosis and informed surgical decision-making, underscoring the system's clinical applicability.

Study the Occurrence of Mycobacterium Bovis in Tuberculosis of Peripheral Lymph Nodes and its Effect on the Course of the Disease.

The incidence of tuberculosis caused by Mycobacterium bovis, not only the pulmonary form, but also the form developing in the extrapulmonary organs, is also increasing from year to year. Despite the large number of EPTB, TBPLN occupies a leading place among diseases of this type and the study of its pathogenic strains is an urgent task in ensuring the effectiveness of treatment. In this regard, the main purpose of the presented manuscript is to determine the frequency of M. bovis in TBPLN, its effect on the development and course of the disease, as well as the effectiveness of treatment. For this purpose, for the first time, the features of education that occur in patients in peripheral lymph nodes using instrumental methods of ultrasound, computed tomography, and magnetic resonance imaging have been identified. In subsequent studies, 110 patients with peripheral lymph node pathology were diagnosed with TBPLN by detecting mycobacteria in pathological material using general hematological, microbiological and gene-molecular (Gene Xpert) methods. In order to ensure the high effectiveness of drugs used for medicinal purposes, strains of the pathogen were detected using histological, cytological studies, BCG test and specific analyzes such as Diaskintest, Quantiferon test, immunological tests. The study showed that about 80% of patients had M bovis in the overall assessment, 76.4% of patients were sensitive to rifampicin, 9.1% of patients had rifampicin-resistant bacteria, and 14.5% of patients did not have mycobacteria. Therapeutic measures were carried out in 2 different modes, such as standard and individual or with replacement, when all patients were divided into 2 groups. During the period from the 56-day intensive phase of standard treatment to the 84-day intensive phase, a total of 40 patients had a sharp decrease in lymph nodes, elimination of purulent inflammation, and after a while 22 patients in this group had a relapse. In the individual treatment regimen, Levofloxacin and linezolid were used instead of pyrazinamide. While the effectiveness of treatment was achieved in 48 patients of group II after 56 and 84 days of the intensive phase, relapses after a certain time were observed in only 6 patients. When choosing an individual treatment regimen in patients diagnosed with M. bovis, a decrease in relapses to 11.5% is achieved. When M. bovis is detected, an individual scheme of antibacterial treatment of tuberculosis is selected, in which, instead of pyrazinamide, it is recommended to choose one of the reserve lines, depending on the sensitivity of the pathogen to drugs.

Achieving Single Cell Acoustic Localisation with Deactivation Super Resolution.

Super-resolution optical microscopy enables optical imaging of cells, molecules and other biological structures beyond the diffraction limit. However, no similar method exists to super-resolve specific cells with ultrasound. Here we introduce Deactivation Super Resolution (DSR), an ultrasound imaging method that uses the acoustic deactivation of genetically encodable contrast agents to super-resolve individual cells with ultrasound as they navigate through structures that cannot be resolved by conventional imaging methods. DSR takes advantage of gas vesicles, which are air-filled sub-micron protein particles that can be expressed in genetically engineered cells to produce ultrasound contrast. Our experimental results show that DSR can distinguish sub-wavelength microstructures that standard B-mode ultrasound images fail to resolve by super- localizing individual mammalian cells. This study provides a proof of concept for the potential of DSR to serve as a super- resolution ultrasound technique for individual cell localization, opening new horizons in the field.

Far-focused pixel-based imaging of internal defects in multi-layered media based on attenuation compensation and circular coherence factor

ABSTRACT The multi-layered media composed of layers with different sound velocities exhibit anisotropy, strong attenuation, and interface reflection in terms of sound propagation, posing challenges for conventional ultrasound imaging methods, especially in detecting deep-seated and near-interface defects. In this study, an improved far-focused pixel-based (FPB) imaging method for detecting internal defects in multi-layered media is proposed. First, the time delay calculation in the FPB method is corrected using the shortest path searching method to determine the acoustic time-of-flight. Then, phase information is extracted from the echo signal to construct a circular coherence factor (CCF) that dynamically weights the time-corrected FPB images, addressing the issue of ineffective detection of near-interface defects caused by interface reflection. Finally, considering the sound attenuation in multi-layered media, the directivity coefficient, transmission coefficient, and divergence coefficient are determined through theoretical derivation to compensate for the echo amplitude. Experimental results demonstrate that the improved FPB method significantly enhances the reflection echo from deep-seated defects, reduces the maximum relative localisation error to 0.9%, and the quantisation error to 6.4%. The proposed method also greatly improves the lateral resolution and signal-to-noise ratio (SNR) of internal defect imaging compared to the total focusing method (TFM) and traditional FPB method.

Ring-array Ultrasound CT Reflection Imaging Applied to Breast Tumor Detection

Abstract Breast tumor is one of the most common malignant tumors among women worldwide. Ultrasound imaging is a widely used modality for its detection and diagnosis. In this study, we derived the theory of focusing and steering technique based on curved array and verified it by simulation. We proposed an ultrasound CT imaging method based on ring-array with multi-subarray simultaneous transmission and reception. We performed simulation and experimental tests and evaluated the images using SSIM, PSNR and image entropy. The simulation results showed that the proposed focusing and steering technique could accurately focus on the set position. And when the ring-array diameter was 200 mm, the imaging effect reached a better level when the number of array elements increased to 1024, and increasing the number of elements had little effect on the imaging effect. In the experimental test, the best result was obtained when the sub-aperture element number of the ring-array was 1, and the result was better when the ring-array element number was 1024. This result provides an effective significance for ultrasound CT imaging of breast cancer.

IoT-Driven Approaches for Early Detection and Monitoring of Heart Disease: Current Trends and Future Directions

Cardiovascular disease (CVD) is a serious disease having a widespread effect on individuals across the world. Early and accurate detection of cardiac disease is crucial in healthcare, especially in the domain of cardiology. Currently, a non-invasive ultrasound imaging method is used that evaluates the structure, performance, and blood, allowing for the precise identification of a number of cardiac ailments, such as valve problems, heart failure, and congenital anomalies. These traditional techniques have some limitations, including high cost, the need for medical expertise and equipment, and the fact that they often create incorrect results due to human involvement. Furthermore, the traditional method takes more time to predict heart disease. Electrocardiogram (ECG) signals play a critical role in reducing death rates caused by CVDs, and they provide details regarding the heart patient’s health to a medical expert by employing an automated heart failure detection system. Recent developments in deep learning-based health care systems, such as ECG signal analysis, include CNN, LSTM, and other neural networks. In this research, we provide a hybrid deep learning based approach for the timely and accurate diagnosis of cardiovascular disease. The proposed system uses a hybrid of convolutional neural networks (CNN) and long short-term memory (LSTM) and utilizes the MIT-BIH ECG signal dataset for heart disease diagnosis. This study uses two different approaches with MIT-BIH arrhythmia imbalanced and balanced datasets. The first approach uses CNN and CNN-LSTM with an imbalanced dataset, and the second approach uses CNN and CNN-LSTM with a balanced dataset. The performance of both approaches was analyzed. The experimental outcomes show that the overall performance of both CNN, CNN-LSTM was excellent on a balanced dataset compared to imbalanced dataset. The proposed system achieved a better result than the previous suggested methods. Additionally, it is easy to adopt the suggested technique in the field of healthcare in order to identify heart disease.

Ultrasound normalized cumulative residual entropy imaging: Theory, methodology, and application

Background and Objective:Ultrasound information entropy imaging is an emerging quantitative ultrasound technique for characterizing local tissue scatterer concentrations and arrangements. However, the commonly used ultrasound Shannon entropy imaging based on histogram-derived discrete probability estimation suffers from the drawbacks of histogram settings dependence and unknown estimator performance. In this paper, we introduced the information-theoretic cumulative residual entropy (CRE) defined in a continuous distribution of cumulative distribution functions as a new entropy measure of ultrasound backscatter envelope uncertainty or complexity, and proposed ultrasound CRE imaging for tissue characterization. Methods:We theoretically analyzed the CRE for Rayleigh and Nakagami distributions and proposed a normalized CRE for characterizing scatterer distribution patterns. We proposed a method based on an empirical cumulative distribution function estimator and a trapezoidal numerical integration for estimating the normalized CRE from ultrasound backscatter envelope signals. We presented an ultrasound normalized CRE imaging scheme based on the normalized CRE estimator and the parallel computation technique. We also conducted theoretical analysis of the differential entropy which is an extension of the Shannon entropy to a continuous distribution, and introduced a method for ultrasound differential entropy estimation and imaging. Monte-Carlo simulation experiments were performed to evaluate the estimation accuracy of the normalized CRE and differential entropy estimators. Phantom simulation and clinical experiments were conducted to evaluate the performance of the proposed normalized CRE imaging in characterizing scatterer concentrations and hepatic steatosis (n = 204), respectively. Results:The theoretical normalized CRE for the Rayleigh distribution was π/4, corresponding to the case where there were ≥10 randomly distributed scatterers within the resolution cell of an ultrasound transducer. The theoretical normalized CRE for the Nakagami distribution decreased as the Nakagami parameter m increased, corresponding to that the ultrasound backscattered statistics varied from pre-Rayleigh to Rayleigh and to post-Rayleigh distributions. Monte-Carlo simulation experiments showed that the proposed normalized CRE and differential entropy estimators can produce a satisfying estimation accuracy even when the size of the test samples is small. Phantom simulation experiments showed that the proposed normalized CRE and differential entropy imaging can characterize scatterer concentrations. Clinical experiments showed that the proposed ultrasound normalized CRE imaging is capable to quantitatively characterize hepatic steatosis, outperforming ultrasound differential entropy imaging and being comparable to ultrasound Shannon entropy and Nakagami imaging. Conclusion:This study sheds light on the theory and methodology of ultrasound normalized CRE. The proposed ultrasound normalized CRE can serve as a new, flexible quantitative ultrasound envelope statistics parameter. The proposed ultrasound normalized CRE imaging may find applications in quantified characterization of biological tissues. Our code will be made available publicly at https://github.com/zhouzhuhuang.

AI-based automated segmentation for ovarian/adnexal masses and their internal components on ultrasound imaging.

Segmentation of ovarian/adnexal masses from surrounding tissue on ultrasound images is a challenging task. The separation of masses into different components may also be important for radiomic feature extraction. Our study aimed to develop an artificial intelligence-based automatic segmentation method for transvaginal ultrasound images that (1)outlines the exterior boundary of adnexal masses and (2)separates internal components. A retrospective ultrasound imaging database of adnexal masses was reviewed for exclusion criteria at the patient, mass, and image levels, with one image per mass. The resulting 54 adnexal masses (36 benign/18 malignant) from 53 patients were separated by patient into training (26 benign/12 malignant) and independent test (10 benign/6 malignant) sets. U-net segmentation performance on test images compared to expert detailed outlines was measured using the Dice similarity coefficient (DSC) and the ratio of the Hausdorff distance to the effective diameter of the outline ( ) for each mass. Subsequently, in discovery mode, a two-level fuzzy c-means (FCM) unsupervised clustering approach was used to separate the pixels within masses belonging to hypoechoic or hyperechoic components. The DSC (median [95% confidence interval]) was 0.91 [0.78, 0.96], and was 0.04 [0.01, 0.12], indicating strong agreement with expert outlines. Clinical review of the internal separation of masses into echogenic components demonstrated a strong association with mass characteristics. A combined U-net and FCM algorithm for automatic segmentation of adnexal masses and their internal components achieved excellent results compared with expert outlines and review, supporting future radiomic feature-based classification of the masses by components.

A modified sidelobe blanking beamforming method for ultrasound plane wave imaging.

In this paper, an ultrasound beamforming method for plane wave (PW) imaging based on modified sidelobe blanking (MSLB) is proposed to improve image resolution and contrast ratio (CR). In this framework, PWs from various angles were designed to create main and auxiliary beamformer signals. Specifically, the PW signals from all angles were first coherently combined to serve as the main beamformer output signals. To prevent excessive clutter and noise, output signals in the main beamformer were weighted by the generalized coherence factor. Subsequently, the PW signals were split into positive and negative angles to perform a subtraction, creating the auxiliary beamformer. Finally, signals in the main beamformer were compared with the signals in the auxiliary beamformer point by point to further eliminate the noises and clutters. Compared with the delay and sum, full width at half maximum of the MSLB for point targets was reduced by an average of 54.17% and 51.65% in simulations and experiments, respectively; and the corresponding CR was improved by 55.38% and 18.40% on average. The MSLB method provided better imaging quality in human carotid arteries. In conclusion, the proposed method can effectively improve image resolution and CR with low computational complexity.

A transcranial multiple waves suppression method for plane wave imaging based on Radon transform

Transcranial ultrasound imaging presents a significant challenge due to the intricate interplay between ultrasound waves and the heterogeneous human skull. The skull’s presence induces distortion, refraction, multiple scattering, and reflection of ultrasound signals, thereby complicating the acquisition of high-quality images. Extracting reflections from the entire waveform is crucial yet exceedingly challenging, as intracranial reflections are often obscured by strong amplitude direct waves and multiple scattering. In this paper, a multiple wave suppression method for ultrasound plane wave imaging is proposed to mitigate the impact of skull interference. Drawing upon prior research, we developed an enhanced high-resolution linear Radon transform using the maximum entropy principle and Bayesian method, facilitating wavefield separation. We detailed the process of wave field separation in the Radon domain through simulation of a model with a high velocity layer. When plane waves emitted at any steering angles, both multiple waves and first arrival waves manifested as distinct energy points. In the brain simulation, we contrasted the characteristic differences between skull reflection and brain-internal signal in Radon domain, and demonstrated that multiples suppression method reduces side and grating lobe levels by approximately 30 dB. Finally, we executed in vitro experiments using a monkey skull to separate weak intracranial reflection signals from strong skull reflections, enhancing the contrast-to-noise ratio by 85 % compared to conventional method using full waveform. This study deeply explores the effect of multiples on effective signal separation, addresses the complexity of wavefield separation, and verifies its efficacy through imaging, thereby significantly advancing ultrasound transcranial imaging techniques.

The ten-year evaluation of clinical characteristics in congenital lung anomaly in pediatrics; a retrospective study in North of Iran

IntroductionCongenital lung anomalies (CLA) are a group of anomalies, including congenital cystic adenomatoid malformation (CCAM), bronchopulmonary sequestrations (BPS), congenital lobar emphysema (CLE), and bronchogenic cysts (BC). The prevalence of these rare anomalies has risen in recent years, according to various population-based studies due to advances in fetal ultrasound technology.MethodThis retrospective study examines the diagnosis of CLA, and was conducted on 72 patients between March 2014 and March 2024 at Taleghani Pediatric Hospital in Gorgan, Iran.ResultThe average age was 18.8 ± 30.3 months, with the majority being boys (62.5%). Most participants had CCAM (41.7%), followed by CLE (18.1%), BPS (16.7%), pulmonary hypoplasia (9.7%), BC (8.3%), and hybrid lesion (5.6%). The majority of patients were Fars (62.5%), and the average hospitalization days was 9.4 ± 4.5 days. Cardiac anomalies were observed in 19.4% of the patients. 62 patients (86.1%) exhibited respiratory symptoms, and prenatal screening during pregnancy led to the diagnosis in 51 patients (70.8%). Most patients had left lung anomalies (43; 59.7%), and the majority (90.3%) survived. There is a statistically significant relation between needed for surgical treatment and patients’ type of pulmonary lesions (p-value: 0.02). In addition, there was a significant relation between the Fars ethnicity and the presence of cardiac anomalies (p-value: 0.04).ConclusionSome CLAs remain undiagnosed or untreated due to the rare nature of congenital lung anomalies. Nevertheless, improvements in ultrasound and other imaging methods will make diagnosing and managing these anomalies during the prenatal period more prevalent, resulting in enhanced understanding.

Multiparametric Brain Hemodynamics Imaging Using a Combined Ultrafast Ultrasound and Photoacoustic System.

Studying brain-wide hemodynamic responses to different stimuli at high spatiotemporal resolutions can help gain new insights into the mechanisms of neuro- diseases and -disorders. Nonetheless, this task is challenging, primarily due to the complexity of neurovascular coupling, which encompasses interdependent hemodynamic parameters including cerebral blood volume (CBV), cerebral blood flow (CBF), and cerebral oxygen saturation (SO2). The current brain imaging technologies exhibit inherent limitations in resolution, sensitivity, and imaging depth, restricting their capacity to comprehensively capture the intricacies of cerebral functions. To address this, a multimodal functional ultrasound and photoacoustic (fUSPA) imaging platform is reported, which integrates ultrafast ultrasound and multispectral photoacoustic imaging methods in a compact head-mountable device, to quantitatively map individual dynamics of CBV, CBF, and SO2 as well as contrast agent enhanced brain imaging at high spatiotemporal resolutions. Following systematic characterization, the fUSPA system is applied to study brain-wide cerebrovascular reactivity (CVR) at single-vessel resolution via relative changes in CBV, CBF, and SO2 in response to hypercapnia stimulation. These results show that cortical veins and arteries exhibit differences in CVR in the stimulated state and consistent anti-correlation in CBV oscillations during the resting state, demonstrating the multiparametric fUSPA system's unique capabilities in investigating complex mechanisms of brain functions.

Pixel-based small-window parametric ultrasound imaging for liver tumor characterization.

Characterizing liver tumors remains a challenge in clinical practice. Ultrasound parametric imaging based on statistical distribution can enhance image contrast compared with B-mode imaging, requiring scatterers following specific distributions. This study proposes a pixel-based small-window parametric ultrasound imaging method using weighted horizontally normalized Shannon entropy (WhNSE) and fuzzy entropy (FE) to improve detectability liver tumor. Pixel-based parametric imaging requires a sliding window to traverse across the B-mode image with the step of one pixel, while calculating the entropy by the pixel values in the window. The entropy is assigned to the center pixel of the sliding window. The entropy image is obtained after getting the entropy values of all pixels. FE and WhNSE are two novel entropies first applied to parametric imaging. The detection abilities of regions of interest (ROI) and the contrast-to-noise ratio (CNR) were evaluated through simulations and clinical explorations. In simulations, FE imaging showed the highest improvement in detecting hyperechoic ROIs, with a CNR gain up to 457.31% (p < 0.01) in simulations. WhNSE imaging demonstrated the best performance in hyperechoic ROI detection, with a CNR of 1.607 ± 0.816 (p = 0.05), significantly higher than B-mode images. The proposed pixel-based parametric imaging method based on fuzzy entropy and weighted horizontally normalized Shannon entropy both effectively enhance the contrast and detectability of ultrasound images. The imaging enhancement method of the pixel-based fuzzy entropy imaging with proper parameters got better detection performance, due to the consideration of the relationship of neighboring pixels.