Improved accurate measurement models and improved iterative reconstruction algorithms would benefit cryo-electron tomography (cryo-ET) performance. Filtered back- projection and related algorithms, successful in CT and MRI, assume a measurement model which is not well matched to the limited range of projection angles, large angular increments, and incomplete projections in cryo-ET. Iterative methods, such as compressed sensing (CS) can include irregular measurement models and spatial extent constraints, and have great potential for solution of severely under-determined systems. This paper uses source models with square and pyramidal basis functions and variable finite width aperture measurement to compare space domain and frequency domain CS reconstruction approaches in the cryo-ET context.
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