Sparsity-based acoustic inversion in cross-sectional multiscale optoacoustic imaging.
OBJECTIVE
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.


METHODS
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.


RESULTS
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.


CONCLUSIONS
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.
Institute for Biological and Medical Imaging, Technische Universitaet Muenchen and Helmholtz Zentrum Muenchen, Ingoldstaedter Landstrasse 1, Neuherberg D-85764, Germany.
Yiyong
Y
Han
Institute for Biological and Medical Imaging, Technische Universitaet Muenchen and Helmholtz Zentrum Muenchen, Ingoldstaedter Landstrasse 1, Neuherberg D-85764, Germany.
Stratis
S
Tzoumas
Institute for Biological and Medical Imaging, Technische Universitaet Muenchen and Helmholtz Zentrum Muenchen, Ingoldstaedter Landstrasse 1, Neuherberg D-85764, Germany.
Antonio
A
Nunes
Institute for Biological and Medical Imaging, Technische Universitaet Muenchen and Helmholtz Zentrum Muenchen, Ingoldstaedter Landstrasse 1, Neuherberg D-85764, Germany.
Vasilis
V
Ntziachristos
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.
Amir
A
Rosenthal
D000162:Acoustics; D000465:Algorithms; D000818:Animals; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D051379:Mice; D008962:Models, Theoretical; D061088:Photoacoustic Techniques; D013997:Time Factors