Han Yiyong, Tzoumas Stratis, Nunes Antonio, Ntziachristos Vasilis, Rosenthal Amir
Institute for Biological and Medical Imaging, Technische Universitaet Muenchen and Helmholtz Zentrum Muenchen, Ingoldstaedter Landstrasse 1, Neuherberg D-85764, Germany.
Institute for Biological and Medical Imaging, Technische Universitaet Muenchen and Helmholtz Zentrum Muenchen, Ingoldstaedter Landstrasse 1, Neuherberg D-85764, Germany and Department of Electrical Engineering, Technion-Israel Institute of Technology, Haifa 32000, Israel.
Med Phys. 2015 Sep;42(9):5444-52. doi: 10.1118/1.4928596.
With recent advancement in hardware of optoacoustic imaging systems, highly detailed cross-sectional images may be acquired at a single laser shot, thus eliminating motion artifacts. Nonetheless, other sources of artifacts remain due to signal distortion or out-of-plane signals. The purpose of image reconstruction algorithms is to obtain the most accurate images from noisy, distorted projection data.
In this paper, the authors use the model-based approach for acoustic inversion, combined with a sparsity-based inversion procedure. Specifically, a cost function is used that includes the L1 norm of the image in sparse representation and a total variation (TV) term. The optimization problem is solved by a numerically efficient implementation of a nonlinear gradient descent algorithm. TV-L1 model-based inversion is tested in the cross section geometry for numerically generated data as well as for in vivo experimental data from an adult mouse.
In all cases, model-based TV-L1 inversion showed a better performance over the conventional Tikhonov regularization, TV inversion, and L1 inversion. In the numerical examples, the images reconstructed with TV-L1 inversion were quantitatively more similar to the originating images. In the experimental examples, TV-L1 inversion yielded sharper images and weaker streak artifact.
The results herein show that TV-L1 inversion is capable of improving the quality of highly detailed, multiscale optoacoustic images obtained in vivo using cross-sectional imaging systems. As a result of its high fidelity, model-based TV-L1 inversion may be considered as the new standard for image reconstruction in cross-sectional imaging.
随着光声成像系统硬件的最新进展，可以在单次激光照射下获取高度详细的横截面图像，从而消除运动伪影。尽管如此，由于信号失真或平面外信号，其他伪影源仍然存在。图像重建算法的目的是从嘈杂、失真的投影数据中获得最准确的图像。
在本文中，作者使用基于模型的方法进行声学反演，并结合基于稀疏性的反演过程。具体而言，使用了一个成本函数，该函数包括稀疏表示中图像的L1范数和一个总变差（TV）项。通过非线性梯度下降算法的数值有效实现来解决优化问题。基于TV-L1模型的反演在横截面几何形状中针对数值生成的数据以及来自成年小鼠的体内实验数据进行了测试。
在所有情况下，基于模型的TV-L1反演在性能上均优于传统的蒂霍诺夫正则化、TV反演和L1反演。在数值示例中，用TV-L1反演重建的图像在定量上与原始图像更相似。在实验示例中，TV-L1反演产生的图像更清晰，条纹伪影更弱。
本文结果表明，TV-L1反演能够提高使用横截面成像系统在体内获得的高度详细的多尺度光声图像的质量。由于其高保真度，基于模型的TV-L1反演可被视为横截面成像中图像重建的新标准。
