奇异值分解
噪音(视频)
光声光谱学
迭代重建
奇异值
光学
图像质量
计算机科学
信号(编程语言)
激光器
声学
计算机视觉
人工智能
物理
图像(数学)
量子力学
特征向量
程序设计语言
作者
Emma R. Hill,Wenfeng Xia,Matthew J. Clarkson,Adrien E. Desjardins
摘要
Singular value decomposition (SVD) was used to identify and remove laser-induced noise in photoacoustic images acquired with a clinical ultrasound scanner. This noise, which was prominent in the radiofrequency data acquired in parallel from multiple transducer elements, was induced by the excitation light source. It was modelled by truncating the SVD matrices so that only the first few largest singular value components were retained, and subtracted prior to image reconstruction. The dependency of the signal amplitude and the number of the largest singular value components used for noise modeling was investigated for different photoacoustic source geometries. Validation was performed with simulated data and measured noise, and with photoacoustic images acquired from the human forearm and finger in vivo using L14-5/38 and L40-8/12 linear array clinical imaging probes. The use of only one singular value component was found to be sufficient to achieve near-complete removal of laser-induced noise from reconstructed images. This method has strong potential to increase image quality for a wide range of photoacoustic imaging systems with parallel data acquisition.
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