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Simultaneous reconstruction of the initial pressure and sound speed in photoacoustic tomography using a deep-learning approach| Format | Member Price | Non-Member Price |
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Paper Abstract
Photoacoustic tomography seeks to reconstruct an acoustic initial pressure distribution from the measurement of the ultrasound waveforms. Conventional methods assume a-prior knowledge of the sound speed distribution, which practically is unknown. One way to circumvent the issue is to simultaneously reconstruct both the acoustic initial pressure and speed. In this article, we develop a novel data-driven method that integrates an advanced deep neural network through model-based iteration. The image of the initial pressure is significantly improved in our numerical simulation.
Paper Details
Date Published: 9 September 2019
PDF: 10 pages
Proc. SPIE 11105, Novel Optical Systems, Methods, and Applications XXII, 1110504 (9 September 2019); doi: 10.1117/12.2529984
Published in SPIE Proceedings Vol. 11105:
Novel Optical Systems, Methods, and Applications XXII
Cornelius F. Hahlweg; Joseph R. Mulley, Editor(s)
PDF: 10 pages
Proc. SPIE 11105, Novel Optical Systems, Methods, and Applications XXII, 1110504 (9 September 2019); doi: 10.1117/12.2529984
Show Author Affiliations
Hongming Shan, Rensselaer Polytechnic Institute (United States)
Christopher Wiedeman, Rensselaer Polytechnic Institute (United States)
Christopher Wiedeman, Rensselaer Polytechnic Institute (United States)
Published in SPIE Proceedings Vol. 11105:
Novel Optical Systems, Methods, and Applications XXII
Cornelius F. Hahlweg; Joseph R. Mulley, Editor(s)
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