Normalized Spatial Autocorrelation in Ultrasound B-Mode Imaging for Point-Scatterer Detection.

OBJECTIVE: Point-scatterer detection plays a key role in medical ultrasound B-mode imaging. Speckle noise and insufficient spatial resolution are important factors affecting point-scatterer detection. To address this issue, normalized spatial autocorrelation in ultrasound B-mode imaging (NSACB) is proposed. METHODS: First, the acquired data are pre-processed by adding Gaussian white noise (GWN) with a certain signal-to-Gaussian white noise ratio (SGWNR). Next, normalized spatial autocorrelation is applied to the pre-processed data, and the data are divided into several new signals with different spatial lags. Then, the new signals are performed unsigned delay multiply and sum. Finally, the NSACB beamformed data are bandpass filtered by extracting the frequency component around twice the center frequency. Simulated and in vitro experiments were designed for validation. RESULTS: Simulations revealed that the lateral resolution of NSACB measured by the -6-dB mainlobe width can reach as high as 11.11% of delay and sum (DAS), 25.01% of filtered delay multiply and sum (F-DMAS) and 50% of LAG-FDMAS-SCF. The sidelobe level of the NSACB can be reduced at most by 28 dB. Experimental results of simple and complex scatterer phantoms indicate the image resolution of the proposed NSACB can even reach up to 18.76% of DAS, 27.28% of F-DMAS and 14.29% of LAG-FDMAS-SCF. Compared with these methods, the proposed NSACB can reduce the sidelobe level at least by 18 dB. CONCLUSION: Although the proposed method causes loss of the ability to observe hypo-echoic structures, these results suggest future work to determine the ability to detect breast microcalcifications, kidney stones, biopsy needle tracking and other scenarios requiring scatterer detection.

Artificial intelligence-assisted ultrasound-guided focused ultrasound therapy: a feasibility study.

OBJECTIVES: Focused ultrasound (FUS) therapy has emerged as a promising noninvasive solution for tumor ablation. Accurate monitoring and guidance of ultrasound energy is crucial for effective FUS treatment. Although ultrasound (US) imaging is a well-suited modality for FUS monitoring, US-guided FUS (USgFUS) faces challenges in achieving precise monitoring, leading to unpredictable ablation shapes and a lack of quantitative monitoring. The demand for precise FUS monitoring heightens when complete tumor ablation involves controlling multiple sonication procedures. METHODS: To address these challenges, we propose an artificial intelligence (AI)-assisted USgFUS framework, incorporating an AI segmentation model with B-mode ultrasound imaging. This method labels the ablated regions distinguished by the hyperechogenicity effect, potentially bolstering FUS guidance. We evaluated our proposed method using the Swin-Unet AI architecture, conducting experiments with a USgFUS setup on chicken breast tissue. RESULTS: Our results showed a 93% accuracy in identifying ablated areas marked by the hyperechogenicity effect in B-mode imaging. CONCLUSION: Our findings suggest that AI-assisted ultrasound monitoring can significantly improve the precision and control of FUS treatments, suggesting a crucial advancement toward the development of more effective FUS treatment strategies.

Enhancement of Ultrasound B-Mode Image Quality Using Nonlinear Filtered-Multiply-and-Sum Compounding for Improved Carotid Artery Segmentation.

In ultrasound B-mode imaging, the axial resolution (AR) is commonly determined by the duration or bandwidth of an excitation signal. A shorter-duration pulse will produce better resolution compared to a longer one but with compromised penetration depth. Instead of relying on the pulse duration or bandwidth to improve the AR, an alternative method termed filtered multiply and sum (FMAS) has been introduced in our previous work. For spatial-compounding, FMAS uses the autocorrelation technique as used in filtered-delay multiply and sum (FDMAS), instead of conventional averaging. FMAS enables a higher frame rate and less computational complexity than conventional plane-wave compound imaging beamformed with delay and sum (DAS) and FDMAS. Moreover, it can provide an improved contrast ratio and AR. In previous work, no explanation was given on how FMAS was able to improve the AR. Thus, in this work, we discuss in detail the theory behind the proposed FMAS algorithm and how it is able to improve the spatial resolution mainly in the axial direction. Simulations, experimental phantom measurements and in vivo studies were conducted to benchmark the performance of the proposed method. We also demonstrate how the suggested new algorithm may be used in a practical biomedical imaging application. The balloon snake active contour segmentation technique was applied to the ultrasound B-mode image of a common carotid artery produced with FMAS. The suggested method is capable of reducing the number of iterations for the snake to settle on the region-of-interest contour, accelerating the segmentation process.

Ultrasonic High-Resolution Imaging and Acoustic Tweezers Using Ultrahigh Frequency Transducer: Integrative Single-Cell Analysis.

Ultrasound imaging is a highly valuable tool in imaging human tissues due to its non-invasive and easily accessible nature. Despite advances in the field of ultrasound research, conventional transducers with frequencies lower than 20 MHz face limitations in resolution for cellular applications. To address this challenge, we employed ultrahigh frequency (UHF) transducers and demonstrated their potential applications in the field of biomedical engineering, specifically for cell imaging and acoustic tweezers. The lateral resolution achieved with a 110 MHz UHF transducer was 20 µm, and 6.5 µm with a 410 MHz transducer, which is capable of imaging single cells. The results of our experiments demonstrated the successful imaging of a single PC-3 cell and a 15 µm bead using an acoustic scanning microscope equipped with UHF transducers. Additionally, the dual-mode multifunctional UHF transducer was used to trap and manipulate single cells and beads, highlighting its potential for single-cell studies in areas such as cell deformability and mechanotransduction.

Prediction of Nodal Metastasis in Lung Cancer Using Deep Learning of Endobronchial Ultrasound Images.

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a valid modality for nodal lung cancer staging. The sonographic features of EBUS helps determine suspicious lymph nodes (LNs). To facilitate this use of this method, machine-learning-based computer-aided diagnosis (CAD) of medical imaging has been introduced in clinical practice. This study investigated the feasibility of CAD for the prediction of nodal metastasis in lung cancer using endobronchial ultrasound images. Image data of patients who underwent EBUS-TBNA were collected from a video clip. Xception was used as a convolutional neural network to predict the nodal metastasis of lung cancer. The prediction accuracy of nodal metastasis through deep learning (DL) was evaluated using both the five-fold cross-validation and hold-out methods. Eighty percent of the collected images were used in five-fold cross-validation, and all the images were used for the hold-out method. Ninety-one patients (166 LNs) were enrolled in this study. A total of 5255 and 6444 extracted images from the video clip were analyzed using the five-fold cross-validation and hold-out methods, respectively. The prediction of LN metastasis by CAD using EBUS images showed high diagnostic accuracy with high specificity. CAD during EBUS-TBNA may help improve the diagnostic efficiency and reduce invasiveness of the procedure.

Effective diagnostic model construction based on discriminative breast ultrasound image regions using deep feature extraction.

PURPOSE: This research aims to analyze the diagnostic contribution of different discriminative regions of the breast ultrasound image and develop a more effective diagnosis method taking advantage of the discriminative regions' complementarity. METHODS: First, the discriminative regions of the original breast ultrasound image as the inner region of the lesion, the marginal zone of the lesion, and the posterior echo region of the lesion were defined. The pretrained Inception-V3 network was used to analyze the diagnostic contribution of these discriminative regions. Then, the network was applied to extract the deep features of the original image and the other three discriminative region images. Since there are many features, principal components analysis (PCA) was used to reduce the dimensionality of the extracted deep features. The selected deep features from different discriminative regions were fused to original image features and sent to the stacking ensemble learning classifier for classification experiments. In this study, 479 cases of breast ultrasound images, including 356 benign lesions and 123 malignant ones, were collected retrospectively and randomly divided into the training and validation set. RESULTS: Experimental results show that by using Inception-V3, the diagnostic performance of each discriminative region is different, and the diagnostic accuracy and the area under the ROC curve (AUC) of the lesion marginal zone image (78.3%, 0.798) are higher than those of the lesion inner region image (73.3%, 0.763) and the posterior echo region image (71.7%, 0.688), but lower than those of the original image (80.0%, 0.817). Furthermore, the best classification performance was obtained when all the four types of deep features (from the original image and three discriminative region images) were fused, and the ensemble learning for classification evaluation was employed. Compared with the original image, the classification accuracy and AUC increased from 80.83%, 0.818 to 85.00%, 0.872, and the classification sensitivity and specificity varied from 0.710, 0.798 to 0.871, 0.787. CONCLUSIONS: The inner region of the lesion, the marginal zone of the lesion, and the posterior echo region of the lesion play significant roles in the diagnosis of the breast ultrasound image. Deep feature fusion of these three kinds of images and the original image can effectively improve the accuracy of diagnosis.

Ultrasonography for diagnosis and management of nephrolithiasis: state of the art and new perspectives / L'ultrasonografia nella diagnosi e nel management della nefrolitiasi: stato dell'arte e prospettive future

The correct management of patients with kidney stones is a crucial issue for nephrologists. In recent years, the incidence and prevalence rates of nephrolithiasis have maintained a growing trend worldwide, showing a strong correlation with other systemic disease such as diabetes mellitus, hypertension, obesity, metabolic syndrome and chronic kidney disease. International guidelines indicate computed tomography as the first choice for all adult patients with suspected acute symptoms for obstructive nephrolithiasis. Intravenous pyelogram is more useful in the follow-up of patients with relapsing nephrolithiasis and known stone composition, while the high costs and the long image acquisition times limit the routine use of magnetic resonance. Recent innovative tools have improved the accuracy of kidney stone localization and measuring with B-Mode and color Doppler imaging, thereby reducing the gap between ultrasonography and computer tomography. The aim of this review is to report the latest evidence on risk factors and on the pathophysiology of nephrolithiasis, and to compare the utility of the available imaging techniques in the management of patients with kidney stones, focusing on the role of ultrasonography and the present and future strategies to improve its accuracy.

Automated localization and segmentation techniques for B-mode ultrasound images: A review.

B-mode ultrasound imaging is used extensively in medicine. Hence, there is a need to have efficient segmentation tools to aid in computer-aided diagnosis, image-guided interventions, and therapy. This paper presents a comprehensive review on automated localization and segmentation techniques for B-mode ultrasound images. The paper first describes the general characteristics of B-mode ultrasound images. Then insight on the localization and segmentation of tissues is provided, both in the case in which the organ/tissue localization provides the final segmentation and in the case in which a two-step segmentation process is needed, due to the desired boundaries being too fine to locate from within the entire ultrasound frame. Subsequenly, examples of some main techniques found in literature are shown, including but not limited to shape priors, superpixel and classification, local pixel statistics, active contours, edge-tracking, dynamic programming, and data mining. Ten selected applications (abdomen/kidney, breast, cardiology, thyroid, liver, vascular, musculoskeletal, obstetrics, gynecology, prostate) are then investigated in depth, and the performances of a few specific applications are compared. In conclusion, future perspectives for B-mode based segmentation, such as the integration of RF information, the employment of higher frequency probes when possible, the focus on completely automatic algorithms, and the increase in available data are discussed.

Imaging results of multi-modal ultrasound computerized tomography system designed for breast diagnosis.

Nowadays, in the era of common computerization, transmission and reflection methods are intensively developed in addition to improving classical ultrasound methods (US) for imaging of tissue structure, in particular ultrasound transmission tomography UTT (analogous to computed tomography CT which uses X-rays) and reflection tomography URT (based on the synthetic aperture method used in radar imaging techniques). This paper presents and analyses the results of ultrasound transmission tomography imaging of the internal structure of the female breast biopsy phantom CIRS Model 052A and the results of the ultrasound reflection tomography imaging of a wire sample. Imaging was performed using a multi-modal ultrasound computerized tomography system developed with the participation of a private investor. The results were compared with the results of imaging obtained using dual energy CT, MR mammography and conventional US method. The obtained results indicate that the developed UTT and URT methods, after the acceleration of the scanning process, thus enabling in vivo examination, may be successfully used for detection and detailed characterization of breast lesions in women.

Automatic dynamic range adjustment for ultrasound B-mode imaging.

In medical ultrasound imaging, dynamic range (DR) is defined as the difference between the maximum and minimum values of the displayed signal to display and it is one of the most essential parameters that determine its image quality. Typically, DR is given with a fixed value and adjusted manually by operators, which leads to low clinical productivity and high user dependency. Furthermore, in 3D ultrasound imaging, DR values are unable to be adjusted during 3D data acquisition. A histogram matching method, which equalizes the histogram of an input image based on that from a reference image, can be applied to determine the DR value. However, it could be lead to an over contrasted image. In this paper, a new Automatic Dynamic Range Adjustment (ADRA) method is presented that adaptively adjusts the DR value by manipulating input images similar to a reference image. The proposed ADRA method uses the distance ratio between the log average and each extreme value of a reference image. To evaluate the performance of the ADRA method, the similarity between the reference and input images was measured by computing a correlation coefficient (CC). In in vivo experiments, the CC values were increased by applying the ADRA method from 0.6872 to 0.9870 and from 0.9274 to 0.9939 for kidney and liver data, respectively, compared to the fixed DR case. In addition, the proposed ADRA method showed to outperform the histogram matching method with in vivo liver and kidney data. When using 3D abdominal data with 70 frames, while the CC value from the ADRA method is slightly increased (i.e., 0.6%), the proposed method showed improved image quality in the c-plane compared to its fixed counterpart, which suffered from a shadow artifact. These results indicate that the proposed method can enhance image quality in 2D and 3D ultrasound B-mode imaging by improving the similarity between the reference and input images while eliminating unnecessary manual interaction by the user.

3D image fusion technique and its clinical application / 医疗卫生装备

Objective To introduce a new method for ultrasonic imaging,3D image fusion technique,and its clinical applications and prospects.Methods A lot of domestic and foreign papers related to 3D image fusion technique were referred to.Results 3D image fusion technique had giant advantage in diagnosis and treatment of tumor.Conclusion 3D image fusion technique is found with a big potential and its extensive application has to take time.

Extended field of view ultrasound diagnostic B-mode imaging / 医疗卫生装备

The principle and technology of medical diagnostic ultrasound imaging for extended field of view (EFOV) are introduced. The EFOV imaging, which is realized through the matching, registration storing and composing of two or more original images, makes ultrasound diagnostic technicians see a panoramic image during observing a big tissue or organ, which will be very instrumental to making a correct judgment of the tissue or organ's status.