Ultrasonic Imaging最新文献

Follicular thyroid carcinoma (FTC) is the second most common thyroid cancer. Preoperative differentiation between benign and malignant follicular tumors remains challenging using ultrasound and fine needle aspiration biopsy (FNAB). Radiomics quantitatively evaluates diseases by extracting and analyzing features from medical images. This study aimed to assess the diagnostic value of ultrasound radiomics in distinguishing follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA) among TI-RADS 4a nodules. A retrospective analysis was conducted on the ultrasound images from 144 patients with TI-RADS 4a follicular thyroid neoplasms who underwent their first surgery in our hospital from January 2018 to June 2024. First, ultrasonographic characteristics (US) were analyzed from ultrasound images and diagnostic reports to build a US model. Second, ultrasound radiomics features were extracted from ultrasound images by the software of 3D-Slicer. According to the postoperative pathological results, the patients were divided into FTC group and FTA group. Following the principle of random allocation, the ratio of the training group (n = 86) to the validation group (n = 58) was 6:4. The ultrasound radiomics features were selected by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm in order to build a radiomics model. Finally, a combined model integrating ultrasonographic characteristics and radiomics features (combined-model) was developed. All models including US model, radiomics model and combined-model were built through multi-factor logistic regression analysis to differentiate and diagnose follicular thyroid neoplasms. The receiver operating characteristic curve (ROC), precision, recall and F1-Score were used to evaluate the efficacy of the models. One hundred forty-four patients with TI-RADS 4a follicular thyroid neoplasms were divided into FTC group (41 cases) and FTA group (103 cases) based on postoperative pathological results. A total of 858 ultrasound radiomics features were extracted from the ultrasound images. After screening, six optimal radiomics features were obtained. Among the three models, the combined-model demonstrated best performance in differentiating FTC from FTA, with the area under the curve (AUC) of 0.839 (95% CI: 0.663-1.000) in the validation group. The F1-Score reflected a balance between precision and recall, with overall performance being superior. Combined model of ultrasonographic characteristics and radiomics may be useful to distinguish FTC from FTA.

Tracheal and distal airway imaging enhance the evaluation of mucociliary clearance (MCC) and respiratory health. Herein, we characterize in vivo pulmonary imaging performance of a microbubble (MB) contrast agent optimized for muco-adhesion. A three-way crossover trial (12 mice, 3 imaging timepoints each) was conducted to evaluate tracheal ultrasound image enhancement following oropharyngeal instillation of standard MBs, our optimized MB formulation (TAP-cationic MBs), and lipid solution control. The feasibility of delivering our TAP-cationic MBs as an aerosol to the distal airways was also evaluated using a porcine model. Contrast imaging procedures were well-tolerated by both animal models. In mice, tracheal delineation was comparably enhanced with TAP-cationic MBs (contrast-to-noise ratio [CNR]: 42.26 dB) and standard MBs (CNR: 45.09 dB). Both exceeded lipid solution control (CNR: 11.9 dB, p < .05). In the porcine model, nebulized administration of TAP-cationic MBs yielded MB accumulation in the distal airways visible on transcutaneous ultrasound. Modifying the standard MB formulation to optimize muco-adhesion does not diminish image enhancement when administered oropharyngeally as a liquid solution, and when administered as an aerosol, TAP-cationic MBs deposit, and can be visualized in the distal lung airways. These findings support further development of MB contrast agents for pulmonary applications.

To develop dynamic monitoring and quantitative analysis of voluntary skeletal muscle contractions. A novel micro wearable ultrasound system was evaluated in 40 healthy female participants. Using pulsed wave Doppler imaging, we captured the muscle bundle contraction of the flexor digitorum superficialis in dominant hands during repeated isotonic contractions for 8 seconds, in a cycle of five rounds. Waveform patterns and derived peak systolic velocity (PSV) and muscle systolic time (MST) were recorded and analyzed. Participants with low skeletal muscle mass index (SMI < 5.7 kg/m²) or first-quartile handgrip strength (HS) exhibited a split waveform with bidirectional systolic patterns and reduced PSV stability (PSV was 10.24-11.31 cm/s and 10.12-11.71 cm/s for subjects with low-SMI or low-HS in the first round, and was 9.04-11.29 cm/s and 9.86-10.72 cm/s in the last round). In contrast, subjects with higher muscle mass and strength had regular muscle contraction waveforms and higher PSV, which decreased with increasing grip counts and recovered after rest (PSV was 11.11-15.47 cm/s and 11.21-14.88 cm/s for subjects with normal-SMI or normal-HS in the first round, and was 10.63-13.94 cm/s and 10.09-13.97 cm/s in the last round). The micro wearable ultrasound device enables continuous imaging of voluntary skeletal muscle contraction, and the waveforms and their derived quantitative indicators vary among individuals with different muscle mass and strength.

Ultrasound elastography is a novel technology that assesses tissue elasticity. Elastography has been studied in subpleural consolidations, yet findings remain contradictory. This study aims to evaluate the utility of 2D-SWE for differentiating benign and malignant consolidations and to develop a simplified protocol accessible to inexperienced operators and applicable to all patients, regardless of clinical status. Prospective single-center study conducted in a tertiary care hospital. We enrolled 101 consecutive patients with consolidation identified on chest CT or X-ray. 2D-SWE was preferentially performed during forced inspiration; when unfeasible, measurements were acquired during end-expiration or spontaneous breathing. Quantitative measurements (shear wave speed, m/s; and elastic modulus, kPa), alongside qualitative elasticity scores, demonstrated statistically significant differences in distinguishing benign and malignant consolidations during multivariate analysis. ROC curve analysis identified optimal diagnostic cutoffs of 1.72 m/s and 9.1 kPa, both exhibiting 89% sensitivity and 80% specificity. The predominant measurement method was spontaneous breathing (90.1%). 2D-SWE effectively differentiates benign and malignant subpleural consolidations. Our simplified protocol, requiring only five valid measurements and adaptable to spontaneous breathing, if ratified in future studies, could replace complex techniques like prolonged apnea and serve as the standardized method in future clinical guidelines.

In shear wave elastography, viscoelastic properties of tissues can be estimated by fitting a rheological model to the phase velocity dispersion curve. However, there is a lack of consensus on the model that best represents tissue behavior. Model-free elastography approaches based on shear wave attenuation (SWA) and dispersion slope analysis have been reported previously. This study evaluated the ability of SWA and dispersion slope analysis to assess fluid content in situ using viscoelastic phantoms and ex vivo chicken breast. Model-free parameters were estimated in viscoelastic phantoms (with fluid percentages ranging from 72.6% to 79.9%, and pre- and post-compression by 10%) and ex vivo chicken breast samples pre- and post-hydration. Estimates of SWA were computed using the frequency-shift (FS) and the attenuation measuring shear wave elastography (AMUSE) methods. Dispersion slopes were computed from the phase velocity dispersion curves. The SWA coefficient estimates were strongly correlated with the fluid percentages in phantoms (r = 0.86 and 0.92 for FS and AMUSE methods, respectively, p < 0.001). However, no trends were observed for dispersion slope estimates (r = -0.73, p < 0.001). Thus, SWA was found to be a more sensitive parameter than the dispersion slope for differentiating phantoms with a range of in situ fluid content. Additionally, when phantoms were subjected to compression, SWA was sensitive to changes in compression-induced fluid variations in situ (p < 0.05), but dispersion slope showed no such trends (p = 0.12). The SWA estimates of ex vivo samples significantly increased post-hydration using both methods (p < 0.05), while the dispersion slope decreased. The findings of this study demonstrate that SWA is sensitive to fluid content in situ, which motivates its further development as a marker to assess pathological conditions.

Detecting ovarian structures in ultrasound images is essential in gynecological and reproductive medicine. An automated detection system can serve as a valuable tool for physicians and assist in complex ultrasound interpretations. This study presents a CNN-based object detector designed to segment and count follicle regions in ovarian ultrasound images. Automated identification of ovarian follicles can aid in diagnosing conditions such as infertility, Polycystic Ovarian Syndrome (PCOS), ovarian cancer, and other reproductive health issues. The proposed model, Multi-Attention Residual Dilated UNet with Squeeze and Excitation (MARDSE-UNet), integrates residual UNet, dilated UNet, and squeeze-and-excitation blocks to enhance follicle detection performance. MARDSE-UNet achieved exceptional results, with 98.69% accuracy, 97.89% precision, 97.7% recall, an F1-score of 86.97%, and Intersection over Union (IoU) of 95.66% in follicle detection using 5-fold cross-validation. The USOVA3D dataset was used for experimentation. The model also incorporates a novel preprocessing stage to address noise and low contrast issues, as well as a post-processing stage to refine edges and extract features such as area, perimeter, and diameter of follicles for a more comprehensive performance comparison. The proposed model outperformed traditional CNN models and other state-of-the-art methods in comparative evaluations.

Polycystic Ovary Syndrome (PCOS) is a leading cause of female infertility and is associated with various health complications, including preterm abortions, anovulation, and ovarian cancer. It affects approximately 5% to 10% of women in their reproductive years. PCOS diagnosis often relies on ultrasound imaging to assess ovarian follicle size, count and arrangement. Accurately diagnosing PCOS in clinical practice poses significant challenges for radiologists due to the variability in follicle sizes and their complex relationships with surrounding blood vessels and tissues. This process is labor-intensive, prone to errors, and time-consuming. To address these challenges, numerous research efforts have focused on automating the detection of PCOS-affected ovaries. While advancements have been made, further improvements are needed to enhance diagnostic accuracy. Convolutional Neural Networks (CNNs) have shown promise in PCOS classification, but models relying solely on global features may achieve suboptimal results, as regional features are often overlooked. This paper introduces a feature fusion model named PCOSFusionNet designed to improve the accuracy of PCOS classification. The proposed system combines handcrafted features extracted using the Histogram of Oriented Gradients (HOG) descriptor with global features obtained from the VGG19 deep learning model. Additionally, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied during preprocessing to enhance image quality and improve feature extraction. The watershed method is employed for segmentation before classification. By integrating deep features with handcrafted features, the system achieves superior classification performance across multiple metrics, including accuracy, precision, recall, and F1-score, using five-fold cross-validation. The performance of the proposed PCOSFusionNet model was evaluated on two publicly available datasets. The first dataset (Dataset_1) contains 3856 ultrasound images and the second dataset (Dataset_2) comprises 12,680 ultrasound images. On these datasets, PCOSFusionNet achieved accuracies of 98.49% and 98.30%, respectively, surpassing existing state-of-the-art methods and demonstrating its effectiveness in PCOS diagnosis.

Kidney stone disease is a prevalent urological disorder that can result in severe pain, obstruction, and long-term complications if not detected and managed promptly. Traditional diagnostic approaches, particularly those relying on manual assessment of ultrasound images, often suffer from limitations such as subjective interpretation, dependency on radiologist expertise, and challenges in identifying small or complex stones. These constraints can lead to diagnostic delays and inconsistencies, especially in time-sensitive or resource-limited clinical settings. Therefore, the need for an intelligent, automated solution that enhances diagnostic accuracy and efficiency is more critical than ever. To address these issues, we propose a novel deep learning-based model called the Kronecker Self-Organizing Map Forward Harmonic Network (KSOMFHNet) for kidney stone classification using ultrasound imagery. The model begins with an image preprocessing phase, where a double bilateral filter is applied to effectively denoise the ultrasound images. Following this, the Deep Recursive Residual Network (DRRN) is employed to segment the kidney region accurately. Feature extraction is then performed using a combination of Binary Robust Independent Elementary Features (BRIEF), shape-based features, and Gray Level Co-Occurrence Matrix (GLCM) texture descriptors. These features are then used for classification via the KSOMFHNet, a hybrid architecture integrating the Deep Kronecker Neural Network (DKN) and Self-Organizing Map Network (SOMNet). This fusion enhances the model's learning capacity and spatial representation abilities. Experimental results demonstrate that KSOMFHNet achieves high performance, with an accuracy of 91.984%, a True Positive Rate (TPR) of 90.543%, a True Negative Rate (TNR) of 92.248%, a precision of 90.179%, and an F1-score of 90.360% for training data is 90%, highlighting its potential for clinical deployment.