Rejesh, NA and Kalva, SK and Pramanik, M and Arigovindan, M (2020) Photo-acoustic tomographic image reconstruction from reduced data using physically inspired regularization. In: Journal of Instrumentation, 15 (12).
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Abstract
We propose a model-based image reconstruction method for photoacoustic tomography (PAT) involving a novel form of regularization and demonstrate its ability to recover good quality images from significantly reduced size datasets. The regularization is constructed to suit the physical structure of typical PAT images. We construct it by combining second-order derivatives and intensity into a non-convex form to exploit a structural property of PAT images that we observe: in PAT images, high intensities and high second-order derivatives are jointly sparse. The specific form of regularization constructed here is a modification of the form proposed for fluorescence image restoration. This regularization is combined with a data fidelity cost, and the required image is obtained as the minimizer of this cost. As this regularization is non-convex, the efficiency of the minimization method is crucial in obtaining artifact-free reconstructions. We develop a custom minimization method for efficiently handling this non-convex minimization problem. Further, as non-convex minimization requires a large number of iterations and the PAT forward model in the data-fidelity term has to be applied in the iterations, we propose a computational structure for efficient implementation of the forward model with reduced memory requirements. We evaluate the proposed method on both simulated and real measured data sets and compare them with a recent reconstruction method that is based on a well-known fast iterative shrinkage threshold algorithm (FISTA). © 2020 IOP Publishing Ltd and Sissa Medialab.
| Item Type: | Journal Article |
|---|---|
| Publication: | Journal of Instrumentation |
| Publisher: | IOP Publishing Ltd |
| Additional Information: | Copyright to this article belongs to IOP Publishing Ltd |
| Keywords: | Data reduction; Iterative methods; Photoacoustic effect; Tomography, Computational structure; Efficient implementation; Image reconstruction methods; Non-convex minimization; Photoacoustic tomography; Reduced memory requirements; Second order derivatives; Tomographic image reconstruction, Image reconstruction |
| Department/Centre: | Division of Electrical Sciences > Electrical Engineering |
| Date Deposited: | 27 Jan 2021 06:06 |
| Last Modified: | 27 Jan 2021 06:06 |
| URI: | http://eprints.iisc.ac.in/id/eprint/67616 |
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