【摘要】 针对超声阵列式光声计算层析成像技术数据采集量大、成像速度慢的问题,为拓展该技术在血流动力学等领域的应用,提出一种基于主成分分析(PCA)的快速光声计算层析图像重建方法。该方法首先通过部分全采样数据,构建样本图像矩阵;然后,通过矩阵分解运算构建信号投影矩阵;最后,基于该投影矩阵在三倍欠采样条件下快速重建出高质量三维光声图像。在体小鼠背部血管成像实验表明:与传统反投影光声图像重建方法相比,基于主成分分析的光声图像重建方法可将数据采集规模降低约35%,三维图像重建速度提高约40%,实现了三倍欠采样条件下高精度光声图像的快速采集与重建。更多还原

【Abstract】 Focusing on the issue that the data acquisition amount of Photoacoustic Computed Tomography( PACT) based on ultrasonic array is generally huge,and the imaging process is time-consuming,a fast photoacoustic computed tomography method with Principal Component Analysis( PCA) was proposed to extend its applications to the field of hemodynamics. First,the matrix of image samples was constructed with part of full-sampling data. Second,the projection matrix representing the signal features could be derived by the decomposition of the sample matrix. Finally,the high-quality three-dimensional photoacoustic images could be recovered by this projection matrix under three-fold under-sampling. The experimental results on vivo back-vascular imaging of a rat show that,compared to the traditional back-projection method,the data acquisition amount of PACT using PCA can be decreased by about 35%,and the three-dimensional reconstruction speed is improved by about40%. As a result,both the fast data acquisition and high-accurate image reconstruction are implemented successfully.更多还原