Deep learning-based speed of sound aberration correction in photoacoustic images
- Title
- Deep learning-based speed of sound aberration correction in photoacoustic images
- Authors
- Jeon, Seungwan; KIM, CHULHONG
- Date Issued
- 2020-02-17
- Publisher
- SPIE
- 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.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/102528
- Article Type
- Conference
- Citation
- Photonics West, Conference on Biomedical Optics, 2020-02-17
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- There are no files associated with this item.
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