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Diffuse optical tomography (DOT) utilises near-infrared light for imagingspatially distributed optical parameters, typically the absorption andscattering coefficients. The image reconstruction problem of DOT is anill-posed inverse problem, due to the non-linear light propagation in tissuesand limited boundary measurements. The ill-posedness means that the imagereconstruction is sensitive to measurement and modelling errors. The Bayesianapproach for the inverse problem of DOT offers the possibility of incorporatingprior information about the unknowns, rendering the problem less ill-posed. Italso allows marginalisation of modelling errors utilising the so-calledBayesian approximation error method. A more recent trend in imagereconstruction techniques is the use of deep learning techniques, which haveshown promising results in various applications from image processing totomographic reconstructions. In this work, we study the non-linear DOT inverseproblem of estimating the absorption and scattering coefficients utilising a`model-based' learning approach, essentially intertwining learned componentswith the model equations of DOT. The proposed approach was validated with 2Dsimulations and 3D experimental data. We demonstrated improved absorption andscattering estimates for targets with a mix of smooth and sharp image features,implying that the proposed approach could learn image features that aredifficult to model using standard Gaussian priors. Furthermore, it was shownthat the approach can be utilised in compensating for modelling errors due tocoarse discretisation enabling computationally efficient solutions. Overall,the approach provided improved computation times compared to a standardGauss-Newton iteration.

A model-based iterative learning approach for diffuse optical tomography