Imad Barhumi

Photoacoustic tomography (PAT) combines the optical absorption contrast of biological tissue with the spatial resolution of ultrasound, yet recovering the initial pressure distribution from sparse-view sensor measurements remains an ill-posed inverse problem. Iterative compressive-sensing solvers and unrolled deep networks both retain a dependence on the system matrix at inference, which leaves real-time clinical reconstruction computationally expensive.

Abstract Photoacoustic tomography (PAT) reconstruction is a highly ill-posed inverse problem, particularly under limited-view acquisition, that leads to severe artifacts and loss of structural information. Diffusion-based generative models have recently been explored as a means of incorporating learned priors into image reconstruction.