论文标题
深度学习改善了高帧速率合成发射光圈成像的数据集恢复
Deep Learning Improves Dataset Recovery for High Frame Rate Synthetic Transmit Aperture Imaging
论文作者
论文摘要
合成发射光圈（STA）成像可以在整个视野中实现最佳的横向分辨率，而成本低帧速率（FR）和低信噪比（SNR）。在我们先前的研究中，提出了基于压缩感测的合成传输光圈（CS-STA）和最小的L2-norm最小二乘（LS-STA）方法，以从更少的Hadamard编码的平面波（PW）传输中恢复完整的STA数据集。结果表明，与STA成像相比，CS/LS-STA可以维持STA的高分辨率，并随着FR的增加而改善深区域的对比度。但是，这些方法将向恢复的Sta数据集引入错误，并随后对波束形成的图像产生严重的伪影。最近，我们发现基于LS-STA的恢复中引入的错误的理论解释是LS-STA方法忽略了真实STA数据的空空间组件。为了解决这个问题，我们建议在无效的空间学习框架（估计缺失的空空间组件）下培训卷积神经网络（CNN），以从更少的Hadamard编码的PW Transmissions中高准确恢复STA数据集。从Phantom和In Vivo样品中学到了低质量的STA数据集（使用LS-STA方法恢复）与相应的高质量STA数据集（使用完整的Hadamard编码的STA成像获得）之间的映射。就视觉质量，NRMSE，GCNR和FWHM而言，将提出的CNN-STA方法的性能与LS-STA，STA和HE-STA方法进行了比较。结果表明，所提出的方法可以提高STA数据集的恢复准确性，因此有效地抑制了使用LS-STA方法获得的图像中显示的伪影。此外，所提出的方法可以像LS-STA一样保持STA的高横向分辨率，而PW传输较少。
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.
