Zhang Jingke, Liu Jing, Fan Wei, Qiu Weibao, Luo Jianwen
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, People's Republic of China.
Shenzhen Mindray Bio-Medical Electronics Co., Ltd, Shenzhen 518057, People's Republic of China.
Phys Med Biol. 2022 May 2;67(10). doi: 10.1088/1361-6560/ac6202.
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 transducer element must be activated individually. In our previous study, we encoded all the transducer 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 (∼hours). In this study, we propose to accelerate the complete STA dataset reconstruction with minimal-norm least squares method.Partial Hadamard apodized plane wave (PW) transmissions were performed to acquire the PW dataset. Thereafter, the complete STA dataset can be reconstructed from the PW dataset with minimal-norm least squares method. Due to the orthogonality of partial Hadamard matrix, the minimal-norm least squares solution can be easily calculated.The proposed method is tested with simulation data and experimental phantom anddata. The results demonstrate that the proposed method achieves ∼5 × 10times faster reconstruction speed than CS algorithm. The simulation results demonstrate that the proposed method is capable of achieving the same accuracy as the conventional CS-STA method for the STA dataset reconstruction. The simulations, phantom andexperiments 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.
合成发射孔径（STA）超声成像以其在全视野中的理想聚焦而闻名。然而，它存在信噪比（SNR）低和帧率低的问题，因为每个换能器元件必须单独激活。在我们之前的研究中，我们用部分哈达玛矩阵对所有换能器元件进行编码，并使用压缩感知（CS）算法（CS-STA）重建完整的STA数据集。由于在每次发射中所有元件都被激活，且发射次数比STA少，该方法可以实现更高的SNR和更高的帧率。其主要缺点是CS重建耗时（约数小时）。在本研究中，我们提出用最小范数最小二乘法加速完整STA数据集的重建。进行部分哈达玛变迹平面波（PW）发射以获取PW数据集。此后，可以用最小范数最小二乘法从PW数据集重建完整的STA数据集。由于部分哈达玛矩阵的正交性，可以很容易地计算出最小范数最小二乘解。用模拟数据、实验体模和数据对所提出的方法进行了测试。结果表明，所提出的方法实现的重建速度比CS算法快约5×10倍。模拟结果表明，所提出的方法在STA数据集重建方面能够达到与传统CS-STA方法相同的精度。模拟、体模和实验表明，与STA相比，所提出的方法能够在保持空间分辨率和减少发射次数的情况下提高广义对比度噪声比（gCNR）和SNR。总之，LS-STA图像质量的提高和计算时间的减少为其在临床中的实时应用铺平了道路。
