Ultrasonic Imaging最新文献

The homodyned-K (HK) distribution is a generalized model of envelope statistics whose parameters α (the clustering parameter) and k (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 α and k parametric maps were obtained, respectively. According to the contrast between the target region and background, the (α or k) 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 α and k 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 α_cws parametric imaging achieved the best accuracy and Dice score of coagulation zone detection.

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.

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.

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.

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; SD = 9.0 years) and 27 asymptomatic athletes (23.3 years; SD = 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 (r = -.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.

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.

Kidney transplantation is the most effective treatment for advanced chronic kidney disease (CKD). If the prognosis of transplantation can be predicted early after transplantation, it might improve the long-term survival of patients with transplanted kidneys. Currently, studies on the assessment and prediction of renal function by radiomics are limited. Therefore, the present study aimed to explore the value of ultrasound (US)-based imaging and radiomics features, combined with clinical features to develop and validate the models for predicting transplanted kidney function after 1 year (TKF-1Y) using different machine learning algorithms. A total of 189 patients were included and classified into the abnormal TKF-1Y group, and the normal TKF-1Y group based on their estimated glomerular filtration rate (eGFR) levels 1 year after transplantation. The radiomics features were derived from the US images of each case. Three machine learning methods were employed to establish different models for predicting TKF-1Y using selected clinical and US imaging as well as radiomics features from the training set. Two US imaging, four clinical, and six radiomics features were selected. Then, the clinical (including clinical and US image features), radiomics, and combined models were developed. The area under the curves (AUCs) of the models was 0.62 to 0.82 within the test set. Combined models showed statistically higher AUCs than the radiomics models (all p-values <.05). The prediction performance of different models was not significantly affected by the different machine learning algorithms (all p-values >.05). In conclusion, US imaging features combined with clinical features could predict TKF-1Y and yield an incremental value over radiomics features. A model integrating all available features may further improve the predictive efficacy. Different machine learning algorithms may not have a significant impact on the predictive performance of the model.

Breast cancer is the most common form of cancer and is still the second leading cause of death for women in the world. Early detection and treatment of breast cancer can reduce mortality rates. Breast ultrasound is always used to detect and diagnose breast cancer. The accurate breast segmentation and diagnosis as benign or malignant is still a challenging task in the ultrasound image. In this paper, we proposed a classification model as short-ResNet with DC-UNet to solve the segmentation and diagnosis challenge to find the tumor and classify benign or malignant with breast ultrasonic images. The proposed model has a dice coefficient of 83% for segmentation and achieves an accuracy of 90% for classification with breast tumors. In the experiment, we have compared with segmentation task and classification result in different datasets to prove that the proposed model is more general and demonstrates better results. The deep learning model using short-ResNet to classify tumor whether benign or malignant, that combine DC-UNet of segmentation task to assist in improving the classification results.

Twenty patients with pathologic nipple discharge underwent conventional galactography and contrast-enhanced ultrasound (CEUS) galactography. Images were reviewed for detection of suspicious lesions. Lesion localization information from CEUS galactography was recorded. We included 25 lesions from the 20 included patients. The pathological results revealed 13 intraductal papillomas. The detective rates of intraductal papilloma by conventional galactography and CEUS galactography were 92.31% and 100%, respectively. All the preoperative localizations of lesions from CEUS galactography were in accordance with the surgical detections. CEUS galactography is a highly effective tool for the detection of intraductal breast lesions, and it could provide accurate lesion localization information for an optimal surgical design.