A new generation of 3D tomography systems is based on multiple emitters and sensors that partially convolve measurements. A successful approach to deconvolve the measurements is to use nonlinear compressed sensing models. We will discuss two different nonlinear compressed sensing models and algorithms to deconvolve the measurements in a high dimensional setting, resulting in 3D image reconstruction.