Ultrasonic Imaging最新文献

The diagnostic problem of grading evaluation of ultrasonic images of Metacarpophalangeal rheumatoid arthritis (RA) is mostly dependent on the skills of sonographers with training. A grading system is used to identify and evaluate the geometric and textural features of bone deterioration and synovium thickening. In this manuscript, utilizing optimized mixed-order relation-aware recurrent neural network for metacarpophalangeal rheumatoid arthritis grading via ultrasound images (MRAG-UI-MORARNN-BWKA) is proposed. First, Tianjin University of Traditional Chinese Medicine's First Teaching Hospital provides the input ultrasound images. The pre-processing step uses confidence partitioning sampling filtering (CPSF) to resize the input images and eliminate background noise. Afterward, the pre-processed images were given to unpaired multi-view graph clustering (UMGC) for segmenting the region of interest (ROI). The holistic dynamic frequency transformer (HDFT) was used for extracting the geometric features like area, thickness, and shape. The Black winged kite algorithm (BWKA) was then employed to optimize the mixed-order relation-aware recurrent neural network (MORARNN) for precise grading of rheumatoid arthritis detection, with grades 0 (no synovium thickening), 1, 2, and 3 (mild, moderate, and severe, respectively). Python is used in the implementation of the proposed MRAG-UI-MORARNN-BWKA method. The proposed strategy achieves significant improvements over existing methods in grading rheumatoid arthritis via ultrasound images. The proposed model attains an accuracy of 97.02%, precision of 97.5% and sensitivity of 97.25%, respectively. These results clearly indicate the better performance and robustness of the proposed method analyzed to existing methods.

In medical ultrasound imaging, achieving high-quality reconstructed images while avoiding a huge computational burden is an important challenge. The Null subtraction imaging (NSI) algorithm results in a high-resolution reconstructed image. However, this method is not successful in recovering the background speckle information. In this paper, a novel algorithm, known as NSI-based generalized coherence factor (GCF)-along with delay-and-sum (DAS), which is abbreviated as NSG-DAS, is developed to overcome this limitation. In the proposed method, by using a hybrid technique, the desired resolution and effective noise suppression of the NSI algorithm, as well as the background speckle information of the conventional DAS beamformer are recovered simultaneously. More precisely, by using the GCF method, a new weighing factor is introduced that enhances the coherent regions of the image and suppresses the off-axis signals. Evaluations prove the favorable performance of the suggested technique; in particular, by using the proposed NSG-DAS method, a resolution comparable to the NSI algorithm is achieved for the geabr0 dataset, which is improved by about 42% compared to DAS. Also, the contrast evaluation parameter of the suggested technique is comparable to the DAS algorithm and is improved by about 63% compared to the NSI method. This indicates the ability of the suggested technique to improve either resolution or contrast simultaneously.

Speckle noise in ultrasound imaging remains a major obstacle to accurate clinical interpretation and reliable anatomical segmentation. Existing enhancement methods often compromise anatomical details while reducing noise, particularly under challenging imaging conditions. To address this, we introduce an innovative hybrid framework combining the Smart Adaptive Framework for Image Enhancement (SAFIE), a denoising engine based on adaptive fractional convolutions and gradient-based refinement, with a segmentation strategy integrating superpixel-based hypergraph modeling and neural ordinary differential equations. This framework enables effective noise suppression and precise segmentation of anatomical structures by capturing both spatial coherence and temporal feature dynamics. The enhanced images reveal improved visibility of anatomical structures and boundaries. Qualitative evaluation by four experienced radiologists confirmed this improvement, with strong inter-observer agreement measured by Fleiss' kappa, highlighting the robustness and clinical relevance of the approach. Quantitative results corroborate these observations, demonstrating performance substantially superior to several state-of-the-art methods. Ablation studies further indicate that each component contributes significantly to overall improvement. These findings suggest that the proposed framework enhances segmentation reliability and provides robust support for diagnostic interpretation in ultrasound imaging.

Ultrasound array probes can transmit diverging wavefronts from virtual source (VS) locations behind the array to obtain ultrafast compounded images with a broad field-of-view, but determining a practical set of diverging-wave VS locations is non-trivial, given the infinite half-plane of possibilities. In this work, we propose VS placement at a constant radial distance r from the array origin, and we compare this to a previous (and less direct) method of VS placement at a constant opening angle β relative to the ends of the array. Each method was implemented in Field II with a 64 element, 2.7 MHz phased-array geometry to simulate point-spread functions (PSFs) at regular 10 mm intervals over the field-of-view; the lateral and axial resolution, peak side-to-main lobe amplitude ratio (PSMR), and maximum amplitude of each PSF were measured. Each method was also implemented on a research scanner with a corresponding probe to acquire images of a tissue-mimicking phantom for comparison. Results from both methods in simulation and phantom experiments showed that the increase in PSF lateral resolution with range was consistent (≈38 µm/mm) and the mean axial resolution agreed within 0.01 mm; mean differences in PSMR and amplitude were <5% and <4%, respectively. Generalized contrast-to-noise ratio (gCNR) was highest for the constant-β₂ method, with differences between methods within ±1%. These results indicate that, relative to the constant-β method, comparable image quality can be achieved with a streamlined constant-r method of VS placement for diverging-wave ultrafast imaging.

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.

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.

Thyroid hormones are significant for controlling metabolism, and two common thyroid disorders, such as hypothyroidism. The hyperthyroidism are directly affect the metabolic rate of the human body. Predicting and diagnosing thyroid disease remain significant challenges in medical research due to the complexity of thyroid hormone regulation and its impact on metabolism. Therefore, this paper proposes a Complex-valued Spatio-Temporal Graph Convolution Neural Network optimized with Giraffe Kicking Optimization Algorithm for Thyroid Nodule Classification in Ultrasound Images (CSGCNN-GKOA-TNC-UI). Here, the ultrasound images are collected through DDTI (Digital Database of Thyroid ultrasound Imageries) dataset. The gathered data is given into the pre-processing stage using Bilinear Double-Order Filter (BDOF) approach to eradicate the noise and increase the input images quality. The pre-processing image is given into the Deep Adaptive Fuzzy Clustering (DAFC) for Region of Interest (RoI) segmentation. The segmented image is fed to the Multi-Objective Matched Synchro Squeezing Chirplet Transform (MMSSCT) for extracting the features, like Geometric features and Morphological features. The extracted features are fed into the CSGCNN, which classifies the Thyroid Nodule into Benign Nodules and Malign Nodules. Finally, Giraffe Kicking Optimization Algorithm (GKOA) is considered to enhance the CSGCNN classifier. The CSGCNN-GKOA-TNC-UI algorithm is implemented in MATLAB. The CSGCNN-GKOA-TNC-UI approach attains 34.9%, 21.5% and 26.8% higher f-score, 18.6%, 29.3 and 19.2% higher accuracy when compared with existing models: Thyroid diagnosis with classification utilizing DNN depending on hybrid meta-heuristic with LSTM method (LSTM-TNC-UI), innovative full-scale connected network for segmenting thyroid nodule in UI (FCG Net-TNC-UI), and Adversarial architecture dependent multi-scale fusion method for segmenting thyroid nodule (AMSeg-TNC-UI) methods respectively. The proposed model enhances thyroid nodule classification accuracy, aiding radiologists and endocrinologists. By reducing misclassification, it minimizes unnecessary biopsies and enables early malignancy detection.

The acoustic nonlinearity parameter (B/A) could enhance the diagnostic capabilities of conventional ultrasonography and quantitative ultrasound in tissues and diseases. Nonlinear acoustic propagation theory of plane waves has been used to develop a dual-energy model of the depletion of the fundamental related to the Gol'dberg number and subsequently to the B/A of media (a reference phantom is used as a baseline). The depletion method, however, needs a priori information of the attenuation coefficient (AC) of the assessed media. For this reason, recently, a work introduced a simultaneous estimator of the B/A and AC based on fitting depletion method measurements to a nonlinear model using the iterative algorithm Gauss-Newton Levenberg-Marquardt (GNLM). However, the GNLM method presented high sensitivity to the initial guess values of the algorithm which limits the robustness of the approach. In the present work, the Gauss-Newton method is combined with a total variation regularization approach (GNTV), which is achievable by expanding the nonlinear model of the GNLM method for joint estimation of the B/A and AC of all pixels of the parametric images instead of a block-wise approach. In addition, the GNTV used compounding data from several tone-burst transmissions at different center frequencies rather than only one narrowband tone-burst. The results suggest that incorporating regularization and increasing the number of frequencies improves the robustness of the GNTV compared to the GNLM method by accurately estimating B/A values in uniform and nonuniform experimental phantoms (mean relative error less than 18%). The best performance of B/A reconstruction was observed when the sample medium exhibited a constant Gol'dberg number.

Ultrasound imaging systems rely on accurate point spread function (PSF) estimation to support advanced image quality enhancement techniques such as deconvolution and speckle reduction. Phase aberration, caused by sound speed inhomogeneity within biological tissue, is inevitable in ultrasound imaging. It distorts the PSF by increasing sidelobe level and introducing asymmetric amplitude, making PSF estimation under phase aberration highly challenging. In this work, we propose a deep learning framework for estimating phase-aberrated PSFs using U-Net and complex U-Net architectures, operating on RF and complex k-space data, respectively, with the latter demonstrating superior performance. Synthetic phase aberration data, generated using the near-field phase screen model, is employed to train the networks. We evaluate various loss functions and find that log-compressed B-mode perceptual loss achieves the best performance, accurately predicting both the mainlobe and near sidelobe regions of the PSF. Simulation results validate the effectiveness of our approach in estimating PSFs under varying levels of phase aberration. Furthermore, we demonstrate that more accurate PSF estimation improves performance in a downstream phase aberration correction task, highlighting the broader utility of the proposed method.