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.

翻译：光声图像重建涉及声学反演，需指定传播介质中的声速。由于缺乏异质软组织内声速空间分布信息，光声图像重建通常假设均匀声速分布（如1540米/秒），与超声成像类似。未补偿声速变化会导致像差伪影，降低图像质量。目前已提出多种方法解决该问题，但通常涉及复杂硬件和/或耗时算法，阻碍临床转化。本研究引入一种深度学习框架，用于双模态光声/超声成像系统中的声速估计及后续像差校正，该系统采用临床超声探头。由于采集的光声与超声图像天然共配准，通过深度神经网络从超声通道数据中估计声速分布，并将其用于精确光声图像重建。该框架包含基于数字体模的初始预训练阶段，进一步通过使用物理体模数据及测量获取的声速图进行迁移学习增强。该框架在数字体模和物理体模上的声速估计均方根误差分别为10.2米/秒和15.2米/秒，光声重建的结构相似性指数测量值最高达0.86（传统方法为0.69）。通过人体志愿者研究进一步证明，光声图像信噪比最高提升1.2倍。结果表明，所提框架可有效增强多种临床及临床前应用中的光声图像重建。