Ultrasonic Imaging最新文献

{"title":"Regularization-Based 2D Strain Tensor Imaging in Quasi-Static Ultrasound Elastography <i>SAGE Publications</i>.","authors":"Anne-Lise Duroy,&nbsp;Valérie Detti,&nbsp;Agnès Coulon,&nbsp;Olivier Basset,&nbsp;Elisabeth Brusseau","doi":"10.1177/01617346231168982","DOIUrl":"https://doi.org/10.1177/01617346231168982","url":null,"abstract":"<p><p>Accurately estimating all strain components in quasi-static ultrasound elastography is crucial for the full analysis of biological media. In this study, 2D strain tensor imaging was investigated, focusing on the use of a regularization method to improve strain images. This method enforces the tissue property of (quasi-) incompressibility, while penalizing strong field variations, to smooth the displacement fields and reduce the noise in the strain components. The performance of the method was assessed with numerical simulations, phantoms, and in vivo breast tissues. For all the media examined, the results showed a significant improvement in both lateral displacement and strain, while axial fields were only slightly modified by the regularization. The introduction of penalty terms allowed us to obtain shear strain and rotation elastograms where the patterns around the inclusions/lesions were clearly visible. In phantom cases, the findings were consistent with the results obtained from the modeling of the experiments. Finally, the easier detectability of the inclusions/lesions in the final lateral strain images was associated with higher elastographic contrast-to-noise ratios (CNRs), with values in the range of [0.54-9.57] versus [0.08-0.38] before regularization.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10044382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multiscale Attentional Unet Model for Automatic Segmentation in Medical Ultrasound Images.","authors":"Rui Wang,&nbsp;Haoyuan Zhou,&nbsp;Peng Fu,&nbsp;Hui Shen,&nbsp;Yang Bai","doi":"10.1177/01617346231169789","DOIUrl":"https://doi.org/10.1177/01617346231169789","url":null,"abstract":"<p><p>Ultrasonography has become an essential part of clinical diagnosis owing to its noninvasive, and real-time nature. To assist diagnosis, automatically segmenting a region of interest (ROI) in ultrasound images is becoming a vital part of computer-aided diagnosis (CAD). However, segmenting ROIs on medical images with relatively low contrast is a challenging task. To better achieve medical ROI segmentation, we propose an efficient module denoted as multiscale attentional convolution (MSAC), utilizing cascaded convolutions and a self-attention approach to concatenate features from various receptive field scales. Then, MSAC-Unet is constructed based on Unet, employing MSAC instead of the standard convolution in each encoder and decoder for segmentation. In this study, two representative types of ultrasound images, one of the thyroid nodules and the other of the brachial plexus nerves, were used to assess the effectiveness of the proposed approach. The best segmentation results from MSAC-Unet were achieved on two thyroid nodule datasets (TND-PUH3 and DDTI) and a brachial plexus nerve dataset (NSD) with Dice coefficients of 0.822, 0.792, and 0.746, respectively. The analysis of segmentation results shows that our MSAC-Unet greatly improves the segmentation accuracy with more reliable ROI edges and boundaries, decreasing the number of erroneously segmented ROIs in ultrasound images.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10043862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combined ARFI and Shear Wave Imaging of Prostate Cancer: Optimizing Beam Sequences and Parameter Reconstruction Approaches.","authors":"Derek Y Chan, Daniel Cody Morris, Thomas J Polascik, Mark L Palmeri, Kathryn R Nightingale","doi":"10.1177/01617346231171895","DOIUrl":"10.1177/01617346231171895","url":null,"abstract":"<p><p>This study demonstrates the implementation of a shear wave reconstruction algorithm that enables concurrent acoustic radiation force impulse (ARFI) imaging and shear wave elasticity imaging (SWEI) of prostate cancer and zonal anatomy. The combined ARFI/SWEI sequence uses closely spaced push beams across the lateral field of view and simultaneously tracks both on-axis (within the region of excitation) and off-axis (laterally offset from the excitation) after each push beam. Using a large number of push beams across the lateral field of view enables the collection of higher signal-to-noise ratio (SNR) shear wave data to reconstruct the SWEI volume than is typically acquired. The shear wave arrival times were determined with cross-correlation of shear wave velocity signals in two dimensions after 3-D directional filtering to remove reflection artifacts. To combine data from serially interrogated lateral push locations, arrival times from different pushes were aligned by estimating the shear wave propagation time between push locations. Shear wave data acquired in an elasticity lesion phantom and reconstructed using this algorithm demonstrate benefits to contrast-to-noise ratio (CNR) with increased push beam density and 3-D directional filtering. Increasing the push beam spacing from 0.3 to 11.6 mm (typical for commercial SWEI systems) resulted in a 53% decrease in CNR. In human <i>in vivo</i> data, this imaging approach enabled high CNR (1.61-1.86) imaging of histologically-confirmed prostate cancer. The <i>in vivo</i> images had improved spatial resolution and CNR and fewer reflection artifacts as a result of the high push beam density, the high shear wave SNR, the use of multidimensional directional filtering, and the combination of shear wave data from different push beams.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10660585/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9687781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasound Homodyned-K Contrast-Weighted Summation Parametric Imaging Based on H-scan for Detecting Microwave Ablation Zones.","authors":"Sinan Li,&nbsp;Zhuhuang Zhou,&nbsp;Shuicai Wu,&nbsp;Weiwei Wu","doi":"10.1177/01617346231162928","DOIUrl":"https://doi.org/10.1177/01617346231162928","url":null,"abstract":"<p><p>The homodyned-K (HK) distribution is a generalized model of envelope statistics whose parameters <i>α</i> (the clustering parameter) and <i>k</i> (the coherent-to-diffuse signal ratio) can be used to monitor the thermal lesions. In this study, we proposed an ultrasound HK contrast-weighted summation (CWS) parametric imaging algorithm based on the H-scan technique and investigated the optimal window side length (WSL) of the HK parameters estimated by the XU estimator (an estimation method based on the first moment of the intensity and two log-moments, which was used in the proposed algorithm) through phantom simulations. H-scan diversified ultrasonic backscattered signals into low- and high-frequency passbands. After envelope detection and HK parameter estimation for each frequency band, the <i>α</i> and <i>k</i> parametric maps were obtained, respectively. According to the contrast between the target region and background, the (<i>α</i> or <i>k</i>) parametric maps of the dual-frequency band were weighted and summed, and then the CWS images were yielded by pseudo-color imaging. The proposed HK CWS parametric imaging algorithm was used to detect the microwave ablation coagulation zones of porcine liver ex vivo under different powers and treatment durations. The performance of the proposed algorithm was compared with that of the conventional HK parametric imaging and frequency diversity and compounding Nakagami imaging algorithms. For two-dimensional HK parametric imaging, it was found that a WSL equal to 4 pulse lengths of the transducer was sufficient for estimating the <i><i>α</i></i> and <i>k</i> parameters in terms of both parameter estimation stability and parametric imaging resolution. The HK CWS parametric imaging provided an improved contrast-to-noise ratio over conventional HK parametric imaging, and the HK <i>α</i>_cws parametric imaging achieved the best accuracy and Dice score of coagulation zone detection.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10043396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2023 Symposium Announcement.","authors":"","doi":"10.1177/01617346231168526","DOIUrl":"10.1177/01617346231168526","url":null,"abstract":"","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9341541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasound Patterns and Disease Progression in Medullary Sponge Kidney in Adults.","authors":"Mirela Liana Gliga,&nbsp;Cristian Chirila,&nbsp;Paula Maria Chirila","doi":"10.1177/01617346231165493","DOIUrl":"https://doi.org/10.1177/01617346231165493","url":null,"abstract":"<p><p>Our paper presents the ultrasound (US) patterns of a rare kidney disease-medullary sponge kidney (MSK)-that have not been described before in comparison with other causes of medullary hyperechogenicity and correlates them with the severity of the disease and prognosis. This is a clinical observational study of all US examinations in the Nephrology Department over a period of 6 years. The abdominal US focused on the kidneys was recorded. US characteristics of the medulla and cortex were analyzed. We found 10 patients with characteristic daisy flower (DF) kidneys. Positive diagnosis in association with other renal risk factors, prognosis, and evolution were evaluated. Two patterns of medullary hyperechogenicity were found and were correlated with disease severity and kidney function. The first pattern is a homogenous echogenicity of the medulla described as a \"daisy-like\" appearance. The second pattern: calcifications associated with medullar echogenicity, stone production, nephrocalcinosis, and impaired kidney function: \"atypical daisy-like.\" Medullary hyperechogenicity can have more US patterns. In MSK, if the medullary echogenicity is homogenous the evolution is benign, whereas the second, inhomogeneous pattern, has a variable clinical presentation with nephrocalcinosis and the outcome is more severe, leading to chronic kidney disease and impairing the quality of life.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9678639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthetic Aperture Ultrasound Imaging through Adaptive Integrated Transmitting-Receiving Beamformer.","authors":"Hasti Rostamikhanghahi,&nbsp;Sayed Mahmoud Sakhaei","doi":"10.1177/01617346231163835","DOIUrl":"https://doi.org/10.1177/01617346231163835","url":null,"abstract":"<p><p>Synthetic aperture (SA) technique is very attractive for ultrafast ultrasound imaging, as the entire medium can be insonified by a single emission. It also permits applying the dynamic focusing as well as adaptive beamforming both in transmission and reception, which results in an enhanced image. In this paper, we firstly show that the problem of designing the transmit and receive beamformers in SA structure can be formulated as a problem of designing a one-way beamformer on a virtual array with a lateral response equal to that of the two-way beamformer on SA. It is also demonstrated that the length of the virtual aperture is increased to the sum of the transmit aperture length and the receive one, which can result in an enhanced resolution. Moreover, a better estimation of the covariance matrix can be obtained which can be utilized for applying adaptive minimum variance (MV) beamforming method on the virtual array, and consequently the resolution and contrast properties would be enhanced. The performance of the new method is compared with other existing MV-based methods and is quantified by some metrics such as the full width at half maximum (FWHM) and generalized contrast to noise ratio (GCNR). Our validations on simulations and experimental data have shown that the new method is capable of obtaining higher GCNR values while retaining or decreasing FWHM values almost all the time. Moreover, for the same subarray length for estimating the covariance matrices, the computational burden of the new method is significantly lower than those of the existing rival methods.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9690787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Calcification Detection in Intravascular Ultrasound (IVUS) Images Using Transfer Learning Based MultiSVM model.","authors":"Priyanka Arora,&nbsp;Parminder Singh,&nbsp;Akshay Girdhar,&nbsp;Rajesh Vijayvergiya","doi":"10.1177/01617346231164574","DOIUrl":"https://doi.org/10.1177/01617346231164574","url":null,"abstract":"<p><p>Cardiovascular disease serves as the leading cause of death worldwide. Calcification detection is considered an important factor in cardiovascular diseases. Currently, medical practitioners visually inspect the presence of calcification using intravascular ultrasound (IVUS) images. The study aims to detect the extent of calcification as belonging to class I, II as mild calcification, and class III, IV as dense calcification from IVUS images acquired at 40 MHz. To detect calcification, the features were extracted using improved AlexNet architecture and then were fed into machine learning classifiers. The experiments were carried out using 14 real IVUS pullbacks of 10 patients. Experimental results show that the combination of traditional machine learning with deep learning approaches significantly improves accuracy. The results show that support vector machines outperform all other classifiers. The proposed model is compared with two other pre-trained models GoogLeNet (98.8%), SqueezeNet (99.2%), and exhibits considerable improvement in classification accuracy (99.8%). In the future other models such as Vision Transformers could be explored with additional feature selection methods such as ReliefF, PSO, ACO, etc. to improve the overall accuracy of diagnosis.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9684965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Infrared Thermography, Intratendon Vascular Resistance, and Echotexture in Athletes with Patellar Tendinopathy: A Cross-Sectional Study.","authors":"Francisco J Molina-Payá,&nbsp;José Ríos-Díaz,&nbsp;Francisco Carrasco-Martínez,&nbsp;Jacinto J Martínez-Payá","doi":"10.1177/01617346231153581","DOIUrl":"https://doi.org/10.1177/01617346231153581","url":null,"abstract":"<p><p>Ultrasonographic signs of tendinopathies are an increase in thickness, loss of alignment in collagen fibers and the presence of neovascularization. Nevertheless, analysis of intratendinous vascular resistance (IVR) can be more useful for understanding the physiological state of the tissue. To show thermal, echotextural, and Doppler signal differences in athletes with patellar tendinopathy and controls. Twenty-six athletes with patellar tendinopathy (PT) participants (30.1 years; <i>SD</i> = 9.0 years) and 27 asymptomatic athletes (23.3 years; <i>SD</i> = 5.38 years) were evaluated with thermographic and Doppler ultrasonography (DS). Area of Doppler signals (DS), echotextural parameters (echointensity and echovariation) and IVR were determined by image analysis. The statistical analysis was performed by Bayesian methods and the results were showed by Bayes Factor (BF10: probability of alternative hypothesis over null hypothesis), and Credibility intervals (CrI) of the effect. The absolute differences of temperature (TD) were clearly greater (BF10 = 19) in the tendinopathy group (patients) than in controls. Regarding temperature differences between the affected and healthy limb, strong evidence was found (BF₁₀ = 14) for a higher temperature (effect = 0.53°C; 95% CrI = 0.15°C-0.95°C) and very strong for reduced IVR compared (BF₁₀ = 71) (effect = -0.67; 95% CrI = -1.10 to 0.25). The differences in area of DS (BF₁₀ = 266) and EV (BF₁₀ = 266) were higher in tendinopathy group. TD showed a moderate positive correlation with VISA-P scores (tau-B = .29; 95% CrI = .04-.51) and strong correlation with IVR (<i>r</i> = -.553; 95%CrI = -.75 to .18). Athletes with patellar tendinopathy showed a more pronounced thermal difference, a larger area of Doppler signal, a lower IVR and a moderately higher echovariaton than controls. The correlation between temperature changes and IVR might be related with the coexistence of degenerative and inflammatory process in PT.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9684441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Boundary-oriented Network for Automatic Breast Tumor Segmentation in Ultrasound Images.","authors":"Mengmeng Zhang,&nbsp;Aibin Huang,&nbsp;Debiao Yang,&nbsp;Rui Xu","doi":"10.1177/01617346231162925","DOIUrl":"https://doi.org/10.1177/01617346231162925","url":null,"abstract":"<p><p>Breast cancer is considered as the most prevalent cancer. Using ultrasound images is a momentous clinical diagnosis method to locate breast tumors. However, accurate segmentation of breast tumors remains an open problem due to ultrasound artifacts, low contrast, and complicated tumor shapes in ultrasound images. To address this issue, we proposed a boundary-oriented network (BO-Net) for boosting breast tumor segmentation in ultrasound images. The BO-Net boosts tumor segmentation performance from two perspectives. Firstly, a boundary-oriented module (BOM) was designed to capture the weak boundaries of breast tumors by learning additional breast tumor boundary maps. Second, we focus on enhanced feature extraction, which takes advantage of the Atrous Spatial Pyramid Pooling (ASPP) module and Squeeze-and-Excitation (SE) block to obtain multi-scale and efficient feature information. We evaluate our network on two public datasets: Dataset B and BUSI. For the Dataset B, our network achieves 0.8685 in Dice, 0.7846 in Jaccard, 0.8604 in Precision, 0.9078 in Recall, and 0.9928 in Specificity. For the BUSI dataset, our network achieves 0.7954 in Dice, 0.7033 in Jaccard, 0.8275 in Precision, 0.8251 in Recall, and 0.9814 in Specificity. Experimental results show that BO-Net outperforms the state-of-the-art segmentation methods for breast tumor segmentation in ultrasound images. It demonstrates that focusing on boundary and feature enhancement creates more efficient and robust breast tumor segmentation.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9740593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}