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Transcranial photoacoustic computed tomography (PACT) is an emerging human neuroimaging modality that holds significant potential for clinical and scientific applications. However, accurate image reconstruction remains challenging due to skull-induced aberration of the measurement data. Model-based image reconstruction methods have been proposed that are based on the elastic wave equation. To be effective, such methods require that the elastic and acoustic properties of the skull are known accurately, which can be difficult to achieve in practice. Additionally, such methods are computationally burdensome. To address these challenges, a novel learningbased image reconstruction was proposed. The method involves the use of a deep neural network to map a preliminary image that was computed by use of a computationally efficient but approximate reconstruction method to a high-quality, de-aberrated, estimate of the induced initial pressure distribution within the cortical region of the brain. The method was systematically evaluated via computer-simulations that involved realistic, full-scale, three-dimensional stochastic head phantoms. The phantoms contained physiologically relevant optical and acoustic properties and stochastically synthesized vasculature. The results demonstrated that the learning-based method could achieve comparable performance to a state-of-the-art model-based method when the assumed skull parameters were accurate, and significantly outperformed the model-based method when uncertainty in the skull parameters was present. Additionally, the method can reduce image reconstruction times from days to tens of minutes. This study represents an important contribution to the development of transcranial PACT and will motivate the exploration of learning-based methods to help advance this important technology.
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