Search

Effective clutter filtering is crucial in suppressing tissue clutter and extracting blood flow signal in Doppler ultrasound. Recent advances in eigen-based clutter filtering techniques have enabled ultrasound imaging of microvasculature without the need for contrast agents. However, simultaneously achieving fully adaptive, highly sensitive and real-time implementation of such eigen-based filtering techniques in clinical scanning scenarios for broad translation remains challenging. To address this, here we propose a fast spatiotemporal clutter filtering technique based on eigenvalue decomposition (EVD) and a novel localized data processing framework for robust and high-definition ultrasound imaging of blood flow. Unlike the existing local clutter filter that hard splits the ultrasound data into small blocks, our approach applies a series of 2D spatial Gaussian windows to the original data to generate local data subsets. This approach improves performance of flow detection while effectively avoiding undesired grid artifacts with dramatically reduced number of subsets required in local EVD filtering to shorten computation time. By leveraging the computational power of Graphics Processing Units (GPUs), we demonstrate the real-time implementation capability of the proposed approach. We also introduce and systematically evaluate several adaptive and automatic eigenvalue thresholding methods tailored for EVD-based filtering to facilitate optimization of blood flow imaging for either global or localized processing. The feasibility of the proposed clutter filtering technique is validated by experimental results from phantom and different in vivo studies, revealing robust clinical application potential. A tradeoff between improved performance and computational cost associated with the packet size and subset number in local processing is also investigated.

The data rate of airborne radar is much higher than the wireless data transfer rate in many detection applications, so the onboard data storage systems are usually used to store the radar data. Data storage systems with good seismic performance usually use NAND Flash as storage medium, and there is a widespread problem of long file management time, which seriously affects the data storage speed, especially under the limitation of platform miniaturization. To solve this problem, a data storage method based on machine learning is proposed for mini-airborne radar. The storage training model is established based on machine learning, and could process various kinds of radar data. The file management methods are classified and determined using the model, and then are applied to the storage of radar data. To verify the performance of the proposed method, a test was carried out on the data storage system of a mini-airborne radar. The experimental results show that the method based on machine learning can form various data storage methods adapted to different data rates and application scenarios. The ratio of the file management time to the actual data writing time is extremely low.

Synthetic transmit aperture (STA) imaging can achieve optimal lateral resolution in the full field of view, at the cost of low frame rate (FR) and low signal-to-noise ratio (SNR). In our previous studies, compressed sensing based synthetic transmit aperture (CS-STA) and minimal l2-norm least squares (LS-STA) methods were proposed to recover the complete STA dataset from fewer Hadamard-encoded plane wave (PW) transmissions. Results demonstrated that, compared with STA imaging, CS/LS-STA can maintain the high resolution of STA and improve the contrast in the deep region with increased FR. However, these methods would introduce errors to the recovered STA datasets and subsequently produce severe artifacts to the beamformed images. Recently, we discovered that the theoretical explanation for the error introduced in the LS-STA-based recovery is that LS-STA method neglects the null space component of the real STA data. To deal with this problem, we propose to train a convolutional neural network (CNN) under the null space learning framework (to estimate the missing null space component) for high-accuracy recovery of the STA dataset from fewer Hadamard-encoded PW transmissions. The mapping between the low-quality STA dataset (recovered using the LS-STA method) and the corresponding high-quality STA dataset (obtained using full Hadamard-encoded STA imaging, HE-STA) was learned from phantom and in vivo samples. The performance of the proposed CNN-STA method was compared with the LS-STA, STA, and HE-STA methods, in terms of visual quality, NRMSE, gCNR, and FWHM. The results demonstrate that the proposed method can improve the recovery accuracy of the STA datasets and therefore effectively suppress the artifacts presented in the images obtained using the LS-STA method. In addition, the proposed method can maintain the high lateral resolution of STA with fewer PW transmissions, as LS-STA does.

Synthetic transmit aperture (STA) ultrasound imaging is well known for ideal focusing in the full field of view. However, it suffers from low signal-to-noise ratio (SNR) and low frame rate, because each array element must be activated individually. In our previous study, we encoded all the array elements with partial Hadamard matrix and reconstructed the complete STA dataset with compressed sensing (CS) algorithm (CS-STA). As all the elements are activated in each transmission and the number of transmissions is smaller than that of STA, this method can achieve higher SNR and higher frame rate. Its main drawback is the time-consuming CS reconstruction. In this study, we accelerate the complete STA dataset reconstruction with minimal l2-norm least square method. Thanks of the orthogonality of partial Hadamard matrix, the minimal l2-norm least square solution can be easily calculated. The proposed method is tested with simulation data and experimental phantom and in-vivo data. The results demonstrate that the proposed method achieves ~5*10^3 times faster reconstruction speed than CS algorithm. The simulation results demonstrate that the proposed method is capable of achieving the same accuracy for STA dataset reconstruction as conventional CS-STA method. The simulations, phantom and in-vivo experiments show that the proposed method is capable of improving the generalized contrast-to-noise ratio (gCNR) and SNR with maintained spatial resolution and fewer transmissions, compared with STA. In conclusion, the improved image quality and reduced computational time of LS-STA pave the way for its real-time applications in the clinics.

The image reconstruction process in medical imaging can be treated as solving an inverse problem. The inverse problem is usually solved using time-consuming iterative algorithms with sparsity or other constraints. Recently, deep neural network (DNN)-based methods have been developed to accelerate the inverse-problem-solving process. However, these methods typically adopt supervised learning scheme, which requires ground truths, or labels of the solutions, for training. In many applications, it would be challenging or even impossible to obtain the ground truth, such as the tissue reflectivity function in ultrasound beamforming. In this study, a general framework based on self-supervised learning (SSL) scheme is proposed to train a DNN to solve the inverse problems. In this way, the measurements can be used as both the inputs and the labels during the training of DNN. The proposed SSL method is applied to four typical linear inverse problems for validation, i.e., plane wave ultrasound and photoacoustic image reconstructions, compressed sensing-based synthetic transmit aperture dataset recovery and deconvolution in ultrasound localization microscopy. Results show that, using the proposed framework, the trained DNN can achieve improved reconstruction accuracy with reduced computational time, compared with conventional methods., Comment: to be published in Proceedings of IEEE IUS 2021