Learning-based sound speed reconstruction and aberration correction in linear-array photoacoustic/ultrasound imaging
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Photoacoustic (PA) image reconstruction involves acoustic inversion that necessitates the specification of the speed of sound (SoS) within the medium of propagation. Due to the lack of information on the spatial distribution of the SoS within heterogeneous soft tissue, a homogeneous SoS distribution (such as 1540 m/s) is typically assumed in PA image reconstruction, similar to that of ultrasound (US) imaging. Failure to compensate the SoS variations leads to aberration artefacts, deteriorating the image quality. In this work, we developed a deep learning framework for SoS reconstruction and subsequent aberration correction in a dual-modal PA/US imaging system sharing a clinical US probe. As the PA and US data were inherently co-registered, the reconstructed SoS distribution from US channel data using deep neural networks was utilised for accurate PA image reconstruction. On a numerical and a tissue-mimicking phantom, this framework was able to significantly suppress US aberration artefacts, with the structural similarity index measure (SSIM) of up to 0.8109 and 0.8128 as compared to the conventional approach (0.6096 and 0.5985, respectively). The networks, trained only on simulated US data, also demonstrated a good generalisation ability on data from ex vivo tissues and the wrist and fingers of healthy human volunteers, and thus could be valuable in various in vivo applications to enhance PA image reconstruction.
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