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Photoacoustic (PA) image reconstruction involves acoustic inversion thatnecessitates the specification of the speed of sound (SoS) within the medium ofpropagation. Due to the lack of information on the spatial distribution of theSoS within heterogeneous soft tissue, a homogeneous SoS distribution (such as1540 m/s) is typically assumed in PA image reconstruction, similar to that ofultrasound (US) imaging. Failure to compensate the SoS variations leads toaberration artefacts, deteriorating the image quality. In this work, wedeveloped a deep learning framework for SoS reconstruction and subsequentaberration correction in a dual-modal PA/US imaging system sharing a clinicalUS probe. As the PA and US data were inherently co-registered, thereconstructed SoS distribution from US channel data using deep neural networkswas utilised for accurate PA image reconstruction. On a numerical and atissue-mimicking phantom, this framework was able to significantly suppress USaberration artefacts, with the structural similarity index measure (SSIM) of upto 0.8109 and 0.8128 as compared to the conventional approach (0.6096 and0.5985, respectively). The networks, trained only on simulated US data, alsodemonstrated a good generalisation ability on data from ex vivo tissues and thewrist and fingers of healthy human volunteers, and thus could be valuable invarious in vivo applications to enhance PA image reconstruction.

Learning-based sound speed reconstruction and aberration correction in linear-array photoacoustic/ultrasound imaging