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Proceedings Paper

Improvement of resolution in full-view linear-array photoacoustic computed tomography using a novel adaptive weighting method
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Paper Abstract

Linear-array-based photoacoustic computed tomography is a popular methodology for deep and high resolution imaging. However, issues such as phase aberration, side-lobe effects, and propagation limitations deteriorate the resolution. The effect of phase aberration due to acoustic attenuation and constant assumption of the speed of sound (SoS) can be reduced by applying an adaptive weighting method such as the coherence factor (CF). Utilizing an adaptive beamforming algorithm such as the minimum variance (MV) can improve the resolution at the focal point by eliminating the side-lobes. Moreover, invisibility of directional objects emitting parallel to the detection plane, such as vessels and other absorbing structures stretched in the direction perpendicular to the detection plane can degrade resolution. In this study, we propose a full-view array level weighting algorithm in which different weighs are assigned to different positions of the linear array based on an orientation algorithm which uses the histogram of oriented gradient (HOG). Simulation results obtained from a synthetic phantom show the superior performance of the proposed method over the existing reconstruction methods.

Paper Details

Date Published: 22 March 2017
PDF: 7 pages
Proc. SPIE 10064, Photons Plus Ultrasound: Imaging and Sensing 2017, 100643H (22 March 2017); doi: 10.1117/12.2254181
Show Author Affiliations
Parsa Omidi, Univ. of Western Ontario (Canada)
Lawson Health Research Institute (Canada)
Mamadou Diop, Univ. of Western Ontario (Canada)
Lawson Health Research Institute (Canada)
Jeffrey Carson, Univ. of Western Ontario (Canada)
Lawson Health Research Institute (Canada)
Mohammadreza Nasiriavanaki, Wayne State Univ. (United States)
Barbara Ann Karmanos Cancer Institute (United States)

Published in SPIE Proceedings Vol. 10064:
Photons Plus Ultrasound: Imaging and Sensing 2017
Alexander A. Oraevsky; Lihong V. Wang, Editor(s)

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