Computed tomography (CT) yields 3D imaging and recovery  of volumetric objects.

                    

Traditional CT has been based on linear models, in which scattering was often considered a disturbance. As computers become stronger, they enable solving more complex, nonlinear inverse problems. We leverage this capability, to formulate and solve CT which intentionally includes scattering events. Scattering then serves as a source of useful information. We show two applications for this approach. In medical X-ray CT, scattering helps to estimate the chemical decomposition per voxel, and significant dose reduction per quality of recovery. The approach further facilitates next-generation medical CT scanners.  Scattering is also dominant in atmospheric radiative transfer in optical wavelengths. Hence, we develop scattering-based CT for passive 3D sensing of scatterer distributions in the atmosphere (aerosols, clouds). This approach is planned for an upcoming space mission (CloudCT).