Simultaneous reconstruction of the initial pressure and sound speed in photoacoustic tomography using a deep-learning approach
This addresses the challenge of unknown sound speed distributions in photoacoustic tomography, which is crucial for medical imaging applications, though it appears incremental as it builds on existing model-based methods.
The paper tackled the problem of simultaneously reconstructing initial pressure and sound speed in photoacoustic tomography without prior knowledge, using a deep-learning approach that improved the initial pressure image in numerical simulations.
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.