Fast aberration correction in 3D transcranial photoacoustic computed tomography via a learning-based image reconstruction method

Transcranial photoacoustic computed tomography (PACT) holds significant potential as a neuroimaging modality. However, compensating for skull-induced aberrations in reconstructed images remains a challenge. Although optimization-based image reconstruction methods (OBRMs) can account for the relevant wave physics, they are computationally demanding and generally require accurate estimates of the skull's viscoelastic parameters. To circumvent these issues, a learning-based image reconstruction method was investigated for three-dimensional (3D) transcranial PACT. The method was systematically assessed in virtual imaging studies that involved stochastic 3D numerical head phantoms and applied to experimental data acquired by use of a physical head phantom that involved a human skull. The results demonstrated that the learning-based method yielded accurate images and exhibited robustness to errors in the assumed skull properties, while substantially reducing computational times compared to an OBRM. To the best of our knowledge, this is the first demonstration of a learned image reconstruction method for 3D transcranial PACT.