As a widely used imaging modality in the medical field, ultrasound has been applied in community medicine, rural medicine, and even telemedicine in recent years. Therefore, the development of portable ultrasound devices has become a popular research topic. However, the limited size of portable ultrasound devices usually degrades the imaging quality, which reduces the diagnostic reliability. To overcome hardware limitations and improve the image quality of portable ultrasound devices, we propose a novel generative adversarial network (GAN) model to achieve mapping between low-quality ultrasound images and corresponding high-quality images. In contrast to the traditional GAN method, our two-stage GAN that cascades a U-Net network prior to the generator as a front end is built to reconstruct the tissue structure, details, and speckle of the reconstructed image. In the training process, an ultrasound plane-wave imaging (PWI) data-based transfer learning method is introduced to facilitate convergence and to eliminate the influence of deformation caused by respiratory activities during data pair acquisition. A gradual tuning strategy is adopted to obtain better results by the PWI transfer learning process. In addition, a comprehensive loss function is presented to combine texture, structure, and perceptual features. Experiments are conducted using simulated, phantom, and clinical data. Our proposed method is compared to four other algorithms, including traditional gray-level-based methods and learning-based methods. The results confirm that the proposed approach obtains the optimum solution for improving quality and offering useful diagnostic information for portable ultrasound images. This technology is of great significance for providing universal medical care.

In recent years, plane-wave imaging (PWI) has attracted considerable attention because of its high temporal resolution. However, the low spatial resolution of PWI limits its clinical applications, which has inspired various studies on the high spatial resolution reconstruction of plane-wave (PW) ultrasound images. Although compounding methods and traditional high spatial resolution reconstruction approaches can improve the image quality, these techniques decrease the temporal resolution. Since learning methods can fully reserve the high temporal resolution of PW ultrasounds, a novel convolutional neural network (CNN) model for the high spatial-temporal resolution reconstruction of PW ultrasound images is proposed in this paper. Considering the multiangle form of PW data, a multichannel model is introduced to produce balanced training. To combine local and contextual information, the multiscale model is adopted. These two innovations constitute our multichannel and multiscale CNN (MMCNN) model. Compared with traditional CNN methods, the proposed model uses a two-stage structure in which a cascading wavelet postprocessing stage is combined with the trained MMCNN model. Cascading wavelet postprocessing aims to preserve speckle information. Furthermore, a feedback system is appended to the iteration process of the network training to solve the overfitting problem and help produce convergence. Based on these improvements, an end-to-end mapping is established between a single-angle B-mode PW image and its corresponding multiangle compounded, high-resolution image. The experiments were conducted on simulated, phantom and real human data. The advantages of our proposed method were compared with a coherent plane-wave compounding method, a conventional maximum a posteriori-based high spatial resolution reconstruction method and a 2-D CNN compounding method, and the results verified that our approach is capable of attaining a better temporal resolution and comparable spatial resolution. In clinical usage, the proposed method is equipped to satisfy with many ultrafast imaging applications, which require high spatial-temporal resolution.

Plane wave imaging (PWI), a typical ultrafast medical ultrasound imaging mode, adopts single plane wave emission without focusing to achieve a high frame rate. However, the imaging quality is severely degraded in comparison with the commonly used focused line scan mode. Conventional adaptive beamformers can improve imaging quality at the cost of additional computation. In this paper, we propose to use a deep neural network (DNN) to enhance the performance of PWI while maintaining a high frame rate. In particular, the PWI response from a single point target is used as the network input, while the focused scan response from the same point serves as the desired output, which is the main contribution of this method. To evaluate the performance of the proposed method, simulations, phantom experiments and in vivo studies are conducted. The delay-and-sum (DAS), the coherence factor (CF), a previously proposed deep learning-based method and the DAS with focused scan are used for comparison. Numerical metrics, including the contrast ratio (CR), the contrast-to-noise ratio (CNR) and the speckle signal-to-noise ratio (sSNR), are used to quantify the performance. The results indicate that the proposed method can achieve superior resolution and contrast performance. Specifically, the proposed method performs better than the DAS in all metrics. Although the CF provides a higher CR, its CNR and sSNR are much lower than those of the proposed method. The overall performance is also better than that of the previous deep learning method and at the same level with focused scan performance. Additionally, in comparison with the DAS, the proposed method requires little additional computation, which ensures high temporal resolution. These results validate that the proposed method can achieve a high imaging quality while maintaining the high frame rate associated with PWI.

In the medical ultrasound field, ultrafast imaging has recently become a hot topic. However, the diagnostic reliability of ultrafast high-frame rate plane-wave (PW) imaging is reduced by its low-quality images. The medical ultrasound equipment on the market usually adopts the line-scanning mode, which can obtain high-quality images at a very low frame rate. In addition, many proven data-driven ultrasound image processing methods are trained by line-scan images. Since the gray-level distributions of line-scan images and PW images are very different, these gray-level distribution-sensitive methods cannot be generalized to ultrafast ultrasound imaging, which limits further applications. Hence, we propose an ultrasound-transfer generative adversarial network to improve the quality of PW images and extend the existing image processing methods to ultrafast ultrasound imaging by reconstructing PW images into line-scan images. This network adopts a residual dense generator with a self-attention system that fully uses the hierarchical features and generates details from all the relevant physiological information. A projection discriminator and spectral normalization are introduced to increase the discernibility and to maintain a balance between the generator and the discriminator. Moreover, we reorganize the transmit sequence of the transducer array to eliminate the negative influence of human movements and facilitate the convergence of the proposed model. The experimental results are evaluated with five metrics, which confirm the feasibility of the proposed method to obtain a line-scan-quality image with a very high frame rate. This technology could significantly popularize ultrafast medical ultrasound imaging.

Multitransmission modalities, such as plane wave compounding and synthetic aperture imaging, are promising techniques for ultrafast ultrasound imaging. Adaptive beamformers have been proposed to improve the imaging quality. Two common categories of adaptive beamformers are the minimum variance (MV)-based beamformers and the coherence factor (CF)-based beamformers. The MV can significantly improve the resolution while lacking robustness. It is also computationally expensive for multitransmission modalities. The CF can increase the contrast while over-suppressing some desired signals. In this paper, we propose a novel beamformer for better imaging quality in multitransmission ultrasound modalities. Specifically, the MV weighting process is applied to the receiving and transmitting beamforming. The spatial smoothing technique is modified for both dimensions to enhance the robustness. Then, the CF-based weights are calculated using the MV beamformed output. The submatrix technique is also used in the CF process to avoid over-suppression. Simulations and experiments are conducted to evaluate the performance of the proposed method. The results show that it can preserve the high resolution of MV and the high contrast of CF. Compared with the traditional compounding method, the full-width at half-maximum is smaller and the contrast ratio is significantly increased. Anatomic structures of an in vivo human carotid artery are more distinguishable. Because of the spatial smoothing in both dimensions, the proposed beamformer also has high robustness against the channel noise and sound velocity errors.

Ultrafast ultrasound imaging provides very high frame rates but provides poor imaging quality due to unfocused beams. The delay multiply and sum (DMAS) beamformer has been used to improve ultrafast ultrasound imaging contrast but is always accompanied by oversuppression, which produces low-quality speckle images and degrades the contrast performance. A smaller maximum lag in the signed DMAS (sDMAS) contributes better speckle preservation but lower resolution for hyperechoic scatters. To overcome this tradeoff, a regional-lag signed delay multiply and sum (rsDMAS) beamformer is proposed in this article. Innovatively, a region discrimination tool realized by the generalized coherence factor (GCF) is used to limit the maximum lag for spatial coherence estimation. Subaperture coherence smoothing estimates the short-lag coherence instead of multiplication in pairs, thereby reducing calculation complexity and smoothing the speckle texture. Normalization and sign correction are also introduced to achieve better beamforming output. The simulated, phantom, and in vivo data are adopted to evaluate the effectiveness of the proposed beamformer. Numerical results show that the proposed method achieves improvements of the contrast ratio (CR) by 9%, contrast-to-noise ratio (CNR) by 41%, speckle signal-to-noise ratio (sSNR) by 41%, and generalized contrast-to-noise ratio (gCNR) by 0.0004 compared with DMAS (in simulation). Resolution experiments show that the proposed method obtains a loss of 0.07 mm in the full width at half maximum (FWHM) and the same separability of close point scatters as DMAS. These findings indicate that the proposed method achieves higher contrast performance at less obvious sacrifice of the lateral resolution than DMAS.