针对光声成像在实际应用中涉及的采样数据不足, 提出了一种基于全变分法的光声图像重建方法。通过计算重建图像的模拟信号与实际信号的残差来更新图像, 进行迭代以获取重建图像。在迭代重建的过程中引入压缩传感理论中的全变分法, 通过梯度下降法得到全变分最小的图像。通过数值仿真, 模拟了在不足采样情况下的图像重建。结果表明, 全变分重建法的重建效果比滤波反投影法、反卷积重建算法及代数重建算法等3种方法更好。在30个采样点的情况下, 重建图像的峰值信噪比值比上述3种算法的重建结果分别高出30.98, 22.09和8.35 dB。另外, 仿体实验结果也表明该方法能更有效地避免噪声的干扰。

For insufficient sampling data existing in the practical application of Photoacoustic Tomography (PAT), a reconstruction method based on total variation method was proposed for photoacoustic imaging to solve this problem. The residual between the real signals and the simulated ones from the reconstructed image was calculated for the update image, and the iteration was implemented to obtain the reconstruction image. During the process of iteration, the total variation method in the compressed sensing was utilized to obtain the image with the smallest total variation value by the gradient descent method. Through the numerical simulation, the image reconstruction in the case of insufficient sampling data was accomplished. The results demonstrate that the reconstruction method based on the total variation has better performance as compared with the filtered back-projection method, deconvolution reconstruction method and algebraic reconstruction method. For the 30 sampling points, the peak signal-noise ratio of the reconstructed image is 30.98, 22.09, 8.35 dB higher than those reconstructed by tree kinds of other methods metioned above, respectively. The result of in vitro experiment also shows that this method is more effective for noise suppression.