次奈奎斯特采样在超声波成像中的应用

Sub-Nyquist sampling in ultrasound imaging

近年来提出的压缩感知(CS)理论指出,以低于香农定理规定的最低频率(2倍频)对稀疏信号进行采样,一样能够得到精确的信号重建结果,这种采样方法,称为次奈奎斯特采样(Sub-Nyquist Sampling)。将该采样方式应用于超声波成像之中,可以有效的减少数据点和采样频率,这意味着更小的机器尺寸以及更少的电能损耗。该文以现有的FRI)Finite Rate of Innovation)模型为基础,提出了一种新型的次奈奎斯特采样方案-多通道载波傅里叶系数混合采样方案,在每个通道,我们将原始信号与方波进行相乘,产生新的模拟信号,然后对其进行积分采样,就能得到一组原始信号傅里叶系数的线性变换,再从线性组合中得到原始信号的傅里叶系数,最后利用光谱法进行信号重建。实验表明,这种方案能够极大地减少采样频率和采样点数目,并且比以前的类似方案具有更好的抗噪能力。

The recent Compressed Sense theory states that a sparse signal is perfectly reconstructed at sub-Nyquist rate.It has been shown, that a much more significant sample reduction may be obtained, by applying CS to ultrasound imaging system. The significant reduction in the amounts of data will lead to the reduction of machinery size and power consumption.In this page we propose a multichannel architecture for sampling pulse streams at the rate of innovation.Our approach is based on modulating the input signals with a set of properly chosen waveforms,followed by a bank of integrators.We show that the pulse stream can be recovered from the proposed minimal-rate samples using standard tools taken from spectral estimation in a stable way even at high rates of innovation.The resulting scheme is flexible and exhibits better noise robustness.Finally,we process ultrasound imaging data using our techniques.