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Appl. Sci. 2018, 8(9), 1570;

A Novel Dictionary-Based Image Reconstruction for Photoacoustic Computed Tomography

Department of Biomedical Engineering, Wayne State University, Detroit, MI 48202, USA
Department of Biomedical Engineering, University of Western Ontario, London, ON N6A 3K7, Canada
Department of Biomedical Engineering, Tarbiat Modares University, Tehran 14115-111, Iran
Department of NanoEngineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92092, USA
Author to whom correspondence should be addressed.
Received: 9 July 2018 / Revised: 26 August 2018 / Accepted: 4 September 2018 / Published: 6 September 2018
(This article belongs to the Special Issue Photoacoustic Tomography (PAT))
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One of the major concerns in photoacoustic computed tomography (PACT) is obtaining a high-quality image using the minimum number of ultrasound transducers/view angles. This issue is of importance when a cost-effective PACT system is needed. On the other hand, analytical reconstruction algorithms such as back projection (BP) and time reversal, when a limited number of view angles is used, cause artifacts in the reconstructed image. Iterative algorithms provide a higher image quality, compared to BP, due to a model used for image reconstruction. The performance of the model can be further improved using the sparsity concept. In this paper, we propose using a novel sparse dictionary to capture important features of the photoacoustic signal and eliminate the artifacts while few transducers is used. Our dictionary is an optimum combination of Wavelet Transform (WT), Discrete Cosine Transform (DCT), and Total Variation (TV). We utilize two quality assessment metrics including peak signal-to-noise ratio and edge preservation index to quantitatively evaluate the reconstructed images. The results show that the proposed method can generate high-quality images having fewer artifacts and preserved edges, when fewer view angles are used for reconstruction in PACT. View Full-Text
Keywords: photoacoustic imaging; image quality assessment; image formation theory; image reconstruction techniques; sparsity photoacoustic imaging; image quality assessment; image formation theory; image reconstruction techniques; sparsity

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Omidi, P.; Zafar, M.; Mozaffarzadeh, M.; Hariri, A.; Haung, X.; Orooji, M.; Nasiriavanaki, M. A Novel Dictionary-Based Image Reconstruction for Photoacoustic Computed Tomography. Appl. Sci. 2018, 8, 1570.

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