Identification and removal of laser-induced noise in photoacoustic imaging using singular value decomposition.
Identification and removal of laser-induced noise in photoacoustic imaging using singular value decomposition.
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DOI：
10.1364/boe.8.000068
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发表时间：
2017-01-01
影响因子：
3.4
通讯作者：
Desjardins AE
中科院分区：
文献类型：
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作者：
Hill ER;Xia W;Clarkson MJ;Desjardins AE
奇异值分解（SVD）用于识别和消除临床超声扫描仪采集的光声图像中激光引起的噪声。这种噪声在从多个换能器元件并行采集的射频数据中很突出，是由激发光源引起的。它是通过截断 SVD 矩阵来建模的，以便仅保留前几个最大的奇异值分量，并在图像重建之前将其减去。对于不同的光声源几何形状，研究了信号幅度和用于噪声建模的最大奇异值分量的数量的依赖性。使用模拟数据和测量噪声以及使用 L14-5/38 和 L40-8/12 线性阵列临床成像探头从人体前臂和手指体内采集的光声图像进行验证。研究发现，仅使用一个奇异值分量就足以实现从重建图像中几乎完全消除激光引起的噪声。该方法具有很大的潜力，可以通过并行数据采集来提高各种光声成像系统的图像质量。
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.