Paper
17 February 2020 Deep learning-based speed of sound aberration correction in photoacoustic images
Seungwan Jeon, Chulhong Kim
Author Affiliations +
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.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Seungwan Jeon and Chulhong Kim "Deep learning-based speed of sound aberration correction in photoacoustic images", Proc. SPIE 11240, Photons Plus Ultrasound: Imaging and Sensing 2020, 112400J (17 February 2020); https://doi.org/10.1117/12.2543440
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CITATIONS
Cited by 7 scholarly publications.
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KEYWORDS
Neural networks

Aberration correction

Reconstruction algorithms

Image processing

In vivo imaging

Computer simulations

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