Sparsity-Based Recovery of Three-Dimensional Photoacoustic Images from Compressed Single-Shot Optical Detection.
Sparsity-Based Recovery of Three-Dimensional Photoacoustic Images from Compressed Single-Shot Optical Detection.
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DOI：
10.3390/jimaging7100201
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发表时间：
2021-10-02
影响因子：
3.2
通讯作者：
Luke GP
中科院分区：
文献类型：
--
作者：
Green D;Gelb A;Luke GP
光声（PA）成像将光激发与超声检测相结合，以实现生物样品的高分辨率成像。高能量脉冲激光通常用于在组织中的多厘米深度处成像。这些激光器通常具有低脉冲重复率，因此为了实时获取图像，每个图像只能使用一个激光脉冲。这个单一脉冲需要使用许多单独的检测器和接收电子设备来充分记录所产生的声波并形成图像。这样的要求使得许多PA成像系统既昂贵又复杂。本研究提出并模拟了一种体积PA成像方法，该方法使用最先进的压缩传感方法以降低的成本和复杂性水平实现初始压力分布（IPD）的实时采集。特别地，光学图像传感器的单次曝光用于捕获整个法布里-珀罗干涉测量声传感器。通过用检流计进行空间扫描来实现时间分辨编码。该光学系统还利用随机二进制掩模来将像素的预定子集设置为零，从而使得能够恢复时间分辨信号。使用两步迭代收缩和保留算法来重建IPD，利用IPD中自然发生的稀疏性以及由二进制掩码提供的附加结构。我们进行模拟数据的实验，并分析我们的新方法的性能。
Photoacoustic (PA) imaging combines optical excitation with ultrasonic detection to achieve high-resolution imaging of biological samples. A high-energy pulsed laser is often used for imaging at multi-centimeter depths in tissue. These lasers typically have a low pulse repetition rate, so to acquire images in real-time, only one pulse of the laser can be used per image. This single pulse necessitates the use of many individual detectors and receive electronics to adequately record the resulting acoustic waves and form an image. Such requirements make many PA imaging systems both costly and complex. This investigation proposes and models a method of volumetric PA imaging using a state-of-the-art compressed sensing approach to achieve real-time acquisition of the initial pressure distribution (IPD) at a reduced level of cost and complexity. In particular, a single exposure of an optical image sensor is used to capture an entire Fabry–Pérot interferometric acoustic sensor. Time resolved encoding as achieved through spatial sweeping with a galvanometer. This optical system further makes use of a random binary mask to set a predetermined subset of pixels to zero, thus enabling recovery of the time-resolved signals. The Two-Step Iterative Shrinking and Thresholding algorithm is used to reconstruct the IPD, harnessing the sparsity naturally occurring in the IPD as well as the additional structure provided by the binary mask. We conduct experiments on simulated data and analyze the performance of our new approach.