IEEE transactions on ultrasonics, ferroelectrics, and frequency control最新文献

Ultrasound plane wave imaging is a cutting-edge technique that enables high frame-rate imaging. However, one challenge associated with high frame-rate ultrasound imaging is the high noise associated with them, hindering their wider adoption. Therefore, the development of a denoising method becomes imperative to augment the quality of plane wave images. Drawing inspiration from Denoising Diffusion Probabilistic Models (DDPMs), our proposed solution aims to enhance plane wave image quality. Specifically, the method considers the distinction between low-angle and high-angle compounding plane waves as noise and effectively eliminates it by adapting a DDPM to beamformed radiofrequency (RF) data. The method underwent training using only 400 simulated images. In addition, our approach employs natural image segmentation masks as intensity maps for the generated images, resulting in accurate denoising for various anatomy shapes. The proposed method was assessed across simulation, phantom, and in vivo images. The results of the evaluations indicate that our approach not only enhances image quality on simulated data but also demonstrates effectiveness on phantom and in vivo data in terms of image quality. Comparative analysis with other methods underscores the superiority of our proposed method across various evaluation metrics. The source code and trained model will be released along with the dataset at: http://code.sonography.ai.

Energy-based displacement tracking of ultrasound images can be implemented by optimizing a cost function consisting of a data term, a mechanical congruency term, and first- and second-order continuity terms. This approach recently provided a promising solution to two-dimensional axial and lateral displacement tracking in ultrasound strain elastography. However, the associated second-order regularizer only considers the unmixed second derivatives and disregards the mixed derivatives, thereby providing suboptimal noise suppression and limiting possibilities for total strain tensor imaging. We propose to improve axial, lateral, axial shear, and lateral shear strain estimation quality by formulating and optimizing a novel L1-norm-based second-order regularizer that penalizes both mixed and unmixed displacement derivatives. We name the proposed technique L1-MixTURE, which stands for L1-norm Mixed derivative for Total UltRasound Elastography. When compared to simulated ground truth results, the mean structural similarity (MSSIM) obtained with L1-MixTURE ranged 0.53 to 0.86 and the mean absolute error (MAE) ranged 0.00053 to 0.005. In addition, the mean elastographic signal-to-noise ratio (SNR) achieved with simulated, experimental phantom, and in vivo breast datasets, ranged 1.87 to 52.98, and the mean elastographic contrast-to-noise ratio (CNR) ranged 7.40 to 24.53. When compared to a closely related existing technique that does not consider the mixed derivatives, L1-MixTURE generally outperformed the MSSIM, MAE, SNR, and CNR by up to 37.96%, 67.82%, and 25.53% in the simulated, experimental phantom, and in vivo datasets, respectively. These results collectively highlight the ability of L1-MixTURE to deliver highly accurate axial, lateral, axial shear, and lateral shear strain estimates and advance the state-of-the-art in elastography-guided diagnostic and interventional decisions.

Quantitative muscle function analysis based on the ultrasound imaging, has been used for various applications, particularly with recent development of deep learning methods. The nature of speckle noises in ultrasound images poses challenges to accurate and reliable data annotation for supervised learning algorithms. To obtain a large and reliable dataset without manual scanning and labelling, we proposed a synthesizing pipeline to provide synthetic ultrasound datasets of muscle movement with an accurate ground truth, allowing augmenting, training, and evaluating models for different tasks. Our pipeline contained biomechanical simulation using finite element method, an algorithm for reconstructing sparse fascicles, and a diffusion network for ultrasound image generation. With the adjustment of a few parameters, the proposed pipeline can generate a large dataset of real-time ultrasound images with diversity in morphology and pattern. With 3,030 ultrasound images generated, we qualitatively and quantitatively verified that the synthetic images closely matched with the in-vivo images. In addition, we applied the synthetic dataset into different tasks of muscle analysis. Compared to trained on an unaugmented dataset, model trained on synthetic one had better cross-dataset performance, which demonstrates the feasibility of synthesizing pipeline to augment model training and avoid over-fitting. The results of the regression task show potentials under the conditions that the number of datasets or the accurate label are limited. The proposed synthesizing pipeline can not only be used for muscle-related study, but for other similar study and model development, where sequential images are needed for training.

Kidney stone disease is a major public health issue. By breaking stones with repeated laser irradiation, laser lithotripsy (LL) has become the main treatment for kidney stone disease. Laser-induced cavitation is closely associated with the stone damage in LL. Monitoring the cavitation activities during LL is thus crucial to optimizing the stone damage and maximizing LL efficiency. In this study, we have developed three-dimensional super-resolution passive cavitation mapping (3D-SRPCM), in which the cavitation bubble positions can be localized with an accuracy of 40 μm, which is 1/10th of the acoustic diffraction limit. Moreover, the 3D-SRPCM reconstruction speed has been improved by 300 times by adopting a GPU-based sparse-matrix beamforming approach. Using 3D-SRPCM, we studied LL-induced cavitation activities on BegoStones, both in free space of water and confined space of a kidney phantom. The dose-dependence analysis provided by 3D-SRPCM revealed that accumulated impact pressure on the stone surface has the highest correlation with the stone damage. By providing high-resolution cavitation mapping during LL treatment, we expect that 3D-SRPCM may become a powerful tool to improve the clinical LL efficiency and patient outcome.