Portable ultrasound is recently emerging as a new medical imaging modality featuring high portability, easy connectivity, and real-time on-site diagnostic ability. However, it does not yet provide ultrasound elastography function that enables the diagnosis of malignant lesions using elastic properties. This is mainly due to the limitations of hardware performance and wireless data transfer speed for processing the large amount of data for elastography. Therefore, data transfer reduction is one of the feasible solutions to overcome these limitations. Recently, compressive sensing (CS) theory has been rigorously studied as a means to break the conventional Nyquist sampling rate and thus can significantly decrease the amount of measurement signals without sacrificing signal quality. In this research, we implemented various CS reconstruction frameworks and comparatively evaluated their reconstruction performance for realizing ultrasound elastography function on portable ultrasound. Combinations of three most common model bases (Fourier transform [FT], discrete cosine transform [DCT], and wave atom [WA]) and two reconstruction algorithms (L1 minimization and block sparse Bayesian learning [BSBL]) were considered for CS frameworks. Echoic and elastography phantoms, were developed to evaluate the performance of CS on B-mode images and elastograms. To assess the reconstruction quality, mean absolute error (MAE), signal-to-noise ratio (SNRe), and contrast-to-noise ratio (CNRe) were measured on the B-mode images and elastograms from CS reconstructions. Results suggest that CS reconstruction adopting BSBL algorithm with DCT model basis can yield the best results for all the measures tested, and the maximum data reduction rate for producing readily discernable elastograms is around 60%.

中文翻译：

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便携式超声弹性成像的压缩传感

便携式超声最近成为一种新的医学成像方式，具有高便携性、易于连接和实时现场诊断能力。然而，它还没有提供能够使用弹性特性诊断恶性病变的超声弹性成像功能。这主要是由于弹性成像处理大量数据的硬件性能和无线数据传输速度的限制。因此，减少数据传输是克服这些限制的可行解决方案之一。最近，压缩传感 (CS) 理论作为一种打破传统奈奎斯特采样率的手段得到了严格的研究，因此可以在不牺牲信号质量的情况下显着减少测量信号的数量。在这项研究中，我们实现了各种 CS 重建框架，并比较评估了它们在便携式超声上实现超声弹性成像功能的重建性能。CS 框架考虑了三种最常见的模型库（傅立叶变换 [FT]、离散余弦变换 [DCT] 和波原子 [WA]）和两种重建算法（L1 最小化和块稀疏贝叶斯学习 [BSBL]）的组合。开发了回声和弹性成像模型以评估 CS 在 B 模式图像和弹性图上的性能。为了评估重建质量，在 B 模式图像和 CS 重建的弹性图上测量了平均绝对误差 (MAE)、信噪比 (SNRe) 和对比度噪声比 (CNRe)。