The principle of quantitative acoustic microscopy (QAM) is to form two-dimensional (2D) acoustic parameter maps from a collection of radiofrequency (RF) signals acquired by raster scanning a biological sample. Despite their relatively simple structure consisting of two main reflections, RF signals are currently sampled at very high frequencies, e.g., at 2.5 GHz for QAM system employing a single-element transducer with a center frequency of 250-MHz. The use of such high sampling frequencies is challenging because of the potentially large amount of acquired data and the cost of the necessary analog to digital converters. Based on a parametric model characterizing QAM RF signals, the objective of this article is to use the finite rate of innovation (FRI) framework in order to significantly reduce the number of acquired samples. These are then used to compute Fourier coefficients that are directly fed into a state-of-the-art autoregressive (AR)-based method to estimate the model parameters, which finally leads to the reconstruction of accurate 2D maps. The combination of FRI and AR model for sampling and parametric map recovery allows decreasing the required number of samples per RF signal up to a factor of 18 compared to a conventional approach, with a minimal accuracy loss of quantitative acoustic maps, as proven by visual evaluations and numerical results, i.e. PSNR of 24.50 dB and 24.51 dB for the reconstructed speed of sound map and acoustic impedance map respectively.

中文翻译：

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从以创新率采样的 RF 信号基于自回归模型重建定量声学图

定量声学显微镜 (QAM) 的原理是从一组通过光栅扫描生物样本获得的射频 (RF) 信号形成二维 (2D) 声学参数图。尽管它们由两个主要反射组成的相对简单的结构，RF 信号目前在非常高的频率下采样，例如，对于采用中心频率为 250 MHz 的单元件换能器的 QAM 系统，采样频率为 2.5 GHz。由于潜在的大量采集数据和必要的模数转换器的成本，使用如此高的采样频率具有挑战性。基于表征 QAM RF 信号的参数模型，本文的目标是使用有限创新率 (FRI) 框架来显着减少采集样本的数量。然后将这些用于计算傅立叶系数，这些系数直接输入到最先进的基于自回归 (AR) 的方法中以估计模型参数，最终导致重建准确的 2D 地图。与传统方法相比，用于采样和参数图恢复的 FRI 和 AR 模型的组合允许将每个 RF 信号所需的样本数量减少多达 18 倍，并且定量声学图的精度损失最小，如视觉评估所证明的和数值结果，即声图和声阻抗图的重建速度分别为24.50 dB和24.51 dB的PSNR。最终导致重建精确的 2D 地图。与传统方法相比，用于采样和参数图恢复的 FRI 和 AR 模型的组合允许将每个 RF 信号所需的样本数量减少多达 18 倍，并且定量声学图的精度损失最小，如视觉评估所证明的和数值结果，即声图和声阻抗图的重建速度分别为24.50 dB和24.51 dB的PSNR。最终导致重建精确的 2D 地图。与传统方法相比，用于采样和参数图恢复的 FRI 和 AR 模型的组合允许将每个 RF 信号所需的样本数量减少多达 18 倍，并且定量声学图的精度损失最小，如视觉评估所证明的和数值结果，即声图和声阻抗图的重建速度分别为24.50 dB和24.51 dB的PSNR。