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Open Access Green as soon as Postprint is submitted to ZB.
Sparsity-based acoustic inversion in cross-sectional multiscale optoacoustic imaging.
Med. Phys. 42, 5444-5452 (2015)
PURPOSE: 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.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
L1 Minimization ; Model-based Reconstruction ; Optoacoustic Imaging ; Regularization ; Total Variation; Tomography In-vivo; Photoacoustic Tomography; Reconstruction; Algorithm; Deconvolution; Detectors; Images; Noise; Array
ISSN (print) / ISBN
0094-2405
e-ISSN
1522-8541
Journal
Medical Physics
Quellenangaben
Volume: 42,
Issue: 9,
Pages: 5444-5452
Publisher
American Institute of Physics (AIP)
Publishing Place
Melville
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Biological and Medical Imaging (IBMI)