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Proceedings Paper

Deep learning-based speed of sound aberration correction in photoacoustic images
Author(s): Seungwan Jeon; Chulhong Kim
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Paper Abstract

Beamforming algorithms are widely used for photoacoustic (PA) imaging to reconstruct the initial pressure map. In the reconstruction process, they typically assumed that the imaged biological tissue was a homogeneous medium. However, as biological tissue is generally heterogeneous, the misassumption causes suboptimal image reconstruction. Because it is difficult to predict the heterogeneity of a medium, it was still common to reconstruct images assuming a uniform medium. To solve this problem, we introduce a deep learning-based algorithm that can correct the speed of sound (SoS) aberration in the PA image. We trained a neural network with the multiple simulation datasets and successfully corrected SoS aberrations in a PA in vivo image of the human forearm. We observed that the proposed algorithm effectively suppressed side lobes and noise in the PA image and greatly improves image quality.

Paper Details

Date Published: 17 February 2020
PDF: 4 pages
Proc. SPIE 11240, Photons Plus Ultrasound: Imaging and Sensing 2020, 112400J (17 February 2020); doi: 10.1117/12.2543440
Show Author Affiliations
Seungwan Jeon, Pohang Univ. of Science and Technology (Korea, Republic of)
Chulhong Kim, Pohang Univ. of Science and Technology (Korea, Republic of)

Published in SPIE Proceedings Vol. 11240:
Photons Plus Ultrasound: Imaging and Sensing 2020
Alexander A. Oraevsky; Lihong V. Wang, Editor(s)

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