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Proceedings Paper

Deconvolution based photoacoustic reconstruction for directional transducer with sparsity regularization
Author(s): Hamid Moradi; Shuo Tang; Septimiu E. Salcudean
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Paper Abstract

We define a deconvolution based photoacoustic reconstruction with sparsity regularization (DPARS) algorithm for image restoration from projections. The proposed method is capable of visualizing tissue in the presence of constraints such as the specific directivity of sensors and limited-view Photoacoustic Tomography (PAT). The directivity effect means that our algorithm treats the optically-generated ultrasonic waves based on which direction they arrive at the transducer. Most PA image reconstruction methods assume that sensors have omni-directional response; however, in practice, the sensors show higher sensitivity to the ultrasonic waves coming from one specific direction. In DPARS, the sensitivity of the transducer to incoming waves from different directions are considered. Thus, the DPARS algorithm takes into account the relative location of the absorbers with respect to the transducers, and generates a linear system of equations to solve for the distribution of absorbers. The numerical conditioning and computing times are improved by the use of a sparse Discrete Fourier Transform (DCT) representation of the distribution of absorption coefficients. Our simulation results show that DPARS outperforms the conventional Delay-and-Sum reconstruction method in terms of CNR and RMS errors. Experimental results confirm that DPARS provides images with higher resolution than DAS.

Paper Details

Date Published: 15 March 2016
PDF: 6 pages
Proc. SPIE 9708, Photons Plus Ultrasound: Imaging and Sensing 2016, 97082D (15 March 2016); doi: 10.1117/12.2212077
Show Author Affiliations
Hamid Moradi, The Univ. of British Columbia (Canada)
Shuo Tang, The Univ. of British Columbia (Canada)
Septimiu E. Salcudean, The Univ. of British Columbia (Canada)


Published in SPIE Proceedings Vol. 9708:
Photons Plus Ultrasound: Imaging and Sensing 2016
Alexander A. Oraevsky; Lihong V. Wang, Editor(s)

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