EDA-Net: Dense Aggregation of Deep and Shallow Information Achieves Quantitative Photoacoustic Blood Oxygenation Imaging Deep in Human Breast

Accurately and quantitatively imaging blood oxygen saturation (sO₂) is a very meaningful application of photoacoustic tomography (PAT), which is an important indicator for measuring physiological diseases and assisting cancer diagnostic and treatment. Yet, due to the complex optical properties of heterogeneous biological tissues, the diffusely scattered light in the tissue faces the unknown wavelength-dependent optical attenuation and causes the uncertain distribution of the fluence, which fundamentally limits the quantification accuracy of PAT for imaging sO₂. To tackle this problem, we propose an architecture, named EDA-Net, with Encoder, Decoder and Aggregator, which can aggregate features for a richer representation. We argue that the dense aggregated information helps to extract the comprehensive context information from the multi-wavelength PA images, then accurately infer the quantitative distribution of sO₂. The numerical experiment is performed by using PA images, which are obtained by Monte Carlo optical preprocessing and k-Wave acoustic preprocessing based on clinically-obtained female breast phantom. We also explore the effect of the combination of different wavelengths on the accuracy of estimating sO₂ to guide the design of PA imaging systems for meeting clinical needs. The experimental results demonstrate the efficacy and robustness of our proposed method, and also compare it with other methods to further prove the reliability of our quantitative sO₂ results.