Advances in Photoacoustic Imaging: Tomography and Applications

Dear Colleagues,

Photoacoustic tomography imaging, as a coupled physics imaging modality, utilizes the spatial variation of photon absorption within biological tissues to imaging. In practice, a variety of methods have emerged, including analytical formulations, especially backprojection formulations, as well as computational model-based techniques such as time inversion and iterative methods. However, the incompleteness of measurement data and the reliance on approximations for forward operators and acoustic models make these conventional methods only approximate solutions in practical cases. To ameliorate these problems, one strategy is to introduce additional a priori information to enhance the reconstruction process. One downside of this is that it can be computationally intensive and time-consuming, limiting its practical application. Therefore, there is an urgent need for a new method for photoacoustic tomography image reconstruction that aims to mitigate the effects of incomplete a while reducing the interference with the image. In this context, deep learning frameworks and data-driven methods show great potential but also face challenges, such as accuracy and robustness guarantees. In recent years, deep learning has been developing rapidly in photoacoustic tomography, with high-performance generative models (e.g., GANs, diffusion models) excelling in photoacoustic tomography image reconstruction. This Special Issue will focus on the application of deep learning in photoacoustic tomography and discuss the improvement of deep learning methods’ accuracy in reconstruction. Deep learning has great application prospects in photoacoustic tomography imaging and is well worth further research.

Dr. Xianlin Song
Guest Editor

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• photoacoustic tomography
• physics imaging modality
• a priori information
• accuracy and robustness
• deep learning
• generative models