Manwar Rayyan, Li Xin, Kratkiewicz Karl, Zhu Dongxiao, Avanaki Kamran
Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois, USA.
Department of Computer Science, Wayne State University, Detroit, Michigan, USA.
J Biophotonics. 2023 Nov;16(11):e202300103. doi: 10.1002/jbio.202300103. Epub 2023 Aug 30.
One common method to improve the low signal-to-noise ratio of the photoacoustic (PA) signal generated from weak absorbers or absorbers located in deep tissue is to acquire signal multiple times from the same region and perform averaging. However, pulse-to-pulse laser fluctuations together with differences in the beam profile of the pulses create undeterministic multiple scattering processes in the tissue. This phenomenon consequently induces a spatiotemporal displacement in the PA signal samples which in turn deteriorates the effectiveness of signal averaging. Here, we present an adaptive coherent weighted averaging algorithm to adjust the locations and values of PA signal samples for more efficient signal averaging. The proposed method is evaluated in a linear array-based PA imaging setup of ex vivo sheep brain.
一种改善由弱吸收体或位于深部组织中的吸收体产生的光声（PA）信号低信噪比的常见方法是从同一区域多次采集信号并进行平均。然而，脉冲间的激光波动以及脉冲光束轮廓的差异会在组织中产生不确定的多重散射过程。这种现象进而在PA信号样本中引起时空位移，这反过来又会降低信号平均的有效性。在此，我们提出一种自适应相干加权平均算法，以调整PA信号样本的位置和值，实现更高效的信号平均。该方法在基于线性阵列的离体羊脑PA成像装置中进行了评估。
