首页期刊导航|IEEE transactions on ultrasonics, ferroelectrics, and frequency control
期刊信息/Journal information
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society
IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society
0885-3010
IEEE transactions on ultrasonics, ferroelectrics, and frequency control/Journal IEEE transactions on ultrasonics, ferroelectrics, and frequency controlEI
Pavel B. RosnitskiyOleg A. SapozhnikovVera A. KhokhlovaWayne Kreider...
564-580页
查看更多>>摘要：Transient acoustic holography is a useful technique for characterization of ultrasound transducers. It involves hydrophone measurements of the 2-D distribution of acoustic pressure waveforms in a transverse plane in front of the transducer—a hologram—and subsequent numerical forward projection (FP) or backward projection of the ultrasound field. This approach enables full spatiotemporal reconstruction of the acoustic field, including the vibrational velocity at the transducer surface. This allows identification of transducer defects as well as structural details of the radiated acoustic field such as sidelobes and hot spots. However, numerical projections may be time-consuming ( $10^{{10}}$ – $10^{{11}}$ operations with complex exponents). Moreover, backprojection from the measurement plane to the transducer surface is sensitive to misalignment between the axes of the positioning system and the axes associated with the transducer. This article presents an open-access transducer characterization toolbox for use in MATLAB or Octave on Windows computers (https://github.com/pavrosni/xDDx/releases). The core algorithm is based on the Rayleigh integral implemented in C++ executables for graphics and central processing units (GPUs and CPUs). The toolbox includes an automated procedure for correcting axes misalignments to optimize the visualization of transducer surface vibrations. Beyond using measured holograms, the toolbox can also simulate the fields radiated by user-defined transducers. Measurements from two focused 1.25-MHz 12-element sector transducers (apertures of 87 mm and focal distances of 65 and 87 mm) were used with the toolbox for demonstration purposes. Simulation speed tests for different computational devices showed a range of 0.2 s–3 min for GPUs and 1.6 s–57 min for CPUs.
Ryan HubbardDavid ChoiTejaswi WorlikarUlrich Scheven...
581-590页
查看更多>>摘要：Histotripsy has emerged as a promising therapeutic option for liver tumors, recently gaining food and drug administration (FDA) approval for clinical use in October 2023. Preclinical in vivo histotripsy experiments primarily utilize subcutaneous ectopic murine tumor models, which fail to accurately replicate the complex immunosuppressive tumor microenvironment (TME) of liver tumors. In order to address this gap, we present the design, development, and in vivo demonstration of a miniature, electronically steerable magnetic resonance imaging (MRI)-guided histotripsy array tailored for orthotopic murine liver tumor models. This novel system integrates an 89-element phased array within a 7.0-T small animal MRI scanner, enabling precise targeting through enhanced soft tissue contrast and 3-D visualization. The targeting accuracy of the array was validated in tissue-mimicking red blood cell (RBC) phantoms, exhibiting targeting precision of $0.24~\pm ~0.1$ mm. Subsequent in vivo experiments in naïve mice demonstrated successful liver ablations, confirmed by gross morphology and histological analysis. However, the presence of grating lobes led to undesired collateral damage, highlighted by lung hemorrhages, necessitating future adjustments in the array’s design. This study illustrates the foundational steps necessary for translating histotripsy experiments from subcutaneous to orthotopic models.
Vishwas V. TrivediKatia Flores BasterrecheaKenneth B. BaderHimanshu Shekhar...
591-600页
查看更多>>摘要：Histotripsy is a noninvasive focused ultrasound therapy that liquifies tissue via bubble activity. Conventional ultrasound imaging is used in current clinical practice to monitor histotripsy. Developing surrogate imaging metrics for successful treatment outcomes remains an unmet clinical need. The goal of this work was twofold. First, we investigated whether histotripsy bubble clouds detected with nonlinear imaging (chirp-coded subharmonic imaging with and without Volterra filtering) could be used to assess the ablation zone in vitro. Second, we evaluated the feasibility of improving bubble cloud contrast with this approach in ex vivo porcine kidney. Histotripsy bubble clouds were generated in red blood cell-doped agarose phantoms and imaged with a curvilinear ultrasound probe. The ablation zone was assessed based on images collected with a digital camera. The relationship between the bubble cloud area and the ablation area was assessed using receiver operating characteristic (ROC) analysis, F1 score, accuracy, and Matthews correlation coefficient. Histotripsy bubble clouds were also generated in ex vivo porcine tissue and the ability to improve bubble cloud contrast to tissue was evaluated. Implementing chirp-coded subharmonic imaging with the third-order Volterra filter enhanced contrast-to-tissue ratio (CTR) by up to $40.06~\pm ~0.70$ dB relative to standard imaging in vitro. Furthermore, subharmonic imaging combined with Volterra filtering estimated bubble cloud areas that best matched the ablation zone area based on the analysis metrics. Furthermore, ex vivo studies showed CTR improvement of up to $26.95~\pm ~6.49$ dB. Taken together, these findings advance image guidance and monitoring approaches for histotripsy.
Pedro ViannaParia MehrbodMuawiz ChaudharyMichael Eickenberg...
601-611页
查看更多>>摘要：Ultrasound (US) is considered a key modality for the clinical assessment of hepatic steatosis (i.e., fatty liver) due to its noninvasiveness and availability. Deep learning methods have attracted considerable interest in this field, as they are capable of learning patterns in a collection of images and achieve clinically comparable levels of accuracy in steatosis grading. However, variations in patient populations, acquisition protocols, equipment, and operator expertise across clinical sites can introduce domain shifts that reduce model performance when applied outside the original training setting. In response, unsupervised domain adaptation techniques are being investigated to address these shifts, allowing models to generalize more effectively across diverse clinical environments. In this work, we propose a test-time batch normalization (TTN) technique designed to handle domain shift, especially for changes in label distribution, by adapting selected features of batch normalization (BatchNorm) layers in a trained convolutional neural network model. This approach operates in an unsupervised manner, allowing robust adaptation to new distributions without access to label data. The method was evaluated on two abdominal US datasets collected at different institutions, assessing its capability in mitigating domain shift for hepatic steatosis classification. The proposed method reduced the mean absolute error in steatosis grading by 37% and improved the area under the receiver operating characteristic curves (AUC) for steatosis detection from 0.78 to 0.97, compared to nonadapted models. These findings demonstrate the potential of the proposed method to address domain shift in US-based hepatic steatosis diagnosis, minimizing risks associated with deploying trained models in various clinical settings.
查看更多>>摘要：Ultrasound computed tomography (USCT) is a promising technique for breast cancer detection because of its quantitative imaging capability of the speed of sound (SOS) of soft tissues and the fact that malignant breast lesions often have a higher SOS compared to healthy tissues in the human breast. Compared to waveform inversion-based USCT, bent-ray tracing USCT is relatively less computationally expensive, which particularly suits breast cancer screening in a large population. However, SOS image reconstruction using bent-ray tracing in USCT is a highly ill-conditioned problem, making it susceptible to measurement errors. This presents significant challenges in achieving stable and high-quality reconstructions. In this study, we show that using implicit neural representation (INR), the ill-conditioned problem can be well mitigated, and stable reconstruction is achievable. This INR approach uses a multilayer perceptron (MLP) with hash encoding to model the slowness map as a continuous function, to better regularize the inverse problem and has been shown more effective than classical approaches of solely adding regularization terms in the loss function. Thereby, we propose the bent-ray neural radiance fields (BentRay-NeRF) method for SOS image reconstruction to address the aforementioned challenges in classical SOS image reconstruction methods, such as the Gauss-Newton method. In silico and in vitro experiments showed that BentRay-NeRF has remarkably improved performance compared to the classical method in many aspects, including the image quality and the robustness of the inversion to different acquisition settings in the presence of measurement errors.
查看更多>>摘要：Class imbalance is a significant challenge in medical image analysis, particularly in lung ultrasound (LUS), where severe patterns are often underrepresented. Traditional oversampling techniques, which simply duplicate original data, have limited effectiveness in addressing this issue. To overcome these limitations, this study introduces a novel supervised autoencoder generative adversarial network (SA-GAN) for data augmentation, leveraging advanced generative artificial intelligence (AI) to create high-quality synthetic samples for minority classes. In addition, the traditional data augmentation technique is used for comparison. The SA-GAN incorporates an autoencoder to develop a conditional latent space, effectively addressing weight clipping issues and ensuring higher quality synthetic data. The generated samples are evaluated using similarity metrics and expert analysis to validate their utility. Furthermore, state-of-the-art neural networks are used for multiclass classification, and their performance is compared when trained with GAN-based augmentation versus traditional data augmentation techniques. These contributions enhance the robustness and reliability of AI models in mitigating class imbalance in LUS analysis.
Vassili PustovalovDuong Hung PhamCorentin AlixJean-Pierre Remeniéras...
636-645页
查看更多>>摘要：Ultrasound localization microscopy (ULM) represents a significant advancement over traditional ultrasound (US) imaging, enabling super-resolution (SR) imaging of microvascular structures with unprecedented detail, by using microbubbles (MBs) as highly reflective contrast agents. After injection into the bloodstream, MBs are localized in US images to reconstruct the microvasculature. However, this technique faces a tradeoff between MB localization accuracy and acquisition time. Perfusion with low MB concentrations reduces signal overlap and achieves high localization accuracy but requires extended acquisition times. Conversely, higher MB concentrations shorten acquisition times but increase signal overlap, limiting localization precision. Traditionally, ULM consists of five main steps: tissue filtering, MB detection, MB super-localization, tracking, and rendering. In this study, we present a novel approach that combines a robust principal component analysis (RPCA) with a computational SR technique, replacing the first three steps of ULM with a single process based on solving an SR inverse problem. This method isolates MB signals from background noise and enhances the localization of overlapping MBs, thereby improving overall ULM performance. The experimental results from simulated and in vivo data demonstrate that our proposed approach increases the SR factor by up to 30% and enhances the contrast ratio (CR) by 3.5 dB. It also produces comparable results across other image quality metrics. These improvements enable denser, higher contrast vascular images.
NETL
NSTL
IEEE