  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. Various methods have
been proposed to address this issue, but they usually involve complex hardware
and/or time-consuming algorithms, hindering clinical translation. In this work,
we introduce a deep learning framework for SoS estimation and subsequent
aberration correction in a dual-modal PA/US imaging system exploiting a
clinical US probe. As the acquired PA and US images were inherently
co-registered, the estimated SoS distribution from US channel data using a deep
neural network was incorporated for accurate PA image reconstruction. The
framework comprised an initial pre-training stage based on digital phantoms,
which was further enhanced through transfer learning using physical phantom
data and associated SoS maps obtained from measurements. This framework
achieved a root mean square error of 10.2 m/s and 15.2 m/s for SoS estimation
on digital and physical phantoms, respectively and structural similarity index
measures of up to 0.86 for PA reconstructions as compared to the conventional
approach of 0.69. A maximum of 1.2 times improvement in signal-to-noise ratio
of PA images was further demonstrated with a human volunteer study. Our results
show that the proposed framework could be valuable in various clinical and
preclinical applications to enhance PA image reconstruction.
