This work explores the potentials of structure encoding in sparse tomographic reconstructions. We are encoding spatial structure with Markov Random Field (MRF) models and employ it within Magnetic Resonance Imaging (MRI) and Quantitative Microwave Tomography. We illustrate thereby also different ways of MRF modelling: as a discrete, binary field imposed on hidden labels and as a continuous model imposed on the observable field. In case of MRI, the analyzed approach is a straightforward extension of sparse MRI methods and is related to the so-called LaMP (Lattice Matching Pursuit) algorithm, but with a number of differences. In case of Microwave Tomography, we give another interpretation of structured sparsity using much different, but also effective approach. Thorough experiments demonstrate clear advantages of MRF based structure encoding in both cases and motivate strongly further development.