Simultaneous reconstruction of the initial pressure and sound speed in photoacoustic tomography using a deep-learning approach

Hongming Shan; Christopher Wiedeman; Ge Wang; Yang Yang

doi:10.1117/12.2529984

9 September 2019 Simultaneous reconstruction of the initial pressure and sound speed in photoacoustic tomography using a deep-learning approach

Hongming Shan, Christopher Wiedeman, Ge Wang, Yang Yang

Author Affiliations +

Proceedings Volume 11105, Novel Optical Systems, Methods, and Applications XXII; 1110504 (2019) https://doi.org/10.1117/12.2529984
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

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.

Citation Download Citation

Hongming Shan, Christopher Wiedeman, Ge Wang, and Yang Yang "Simultaneous reconstruction of the initial pressure and sound speed in photoacoustic tomography using a deep-learning approach", Proc. SPIE 11105, Novel Optical Systems, Methods, and Applications XXII, 1110504 (9 September 2019); https://doi.org/10.1117/12.2529984

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available