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Dynamic imaging systems monitor physiological processes that evolve or change over time. However, image reconstruction from dynamic data is made difficult by data incompleteness and significant computational burden. Data incompleteness, in particular, arises from severe undersampling often necessary to increase frame rate by reducing data acquisition time and leads to ill-posedness of the reconstruction problem. Computational cost and memory requirements are particularly burdensome for three-dimensional problems, especially for applications in which high-resolution in space and time is needed. Two main approaches exist for dynamic image reconstruction. Frame-by-frame approaches solve a sequence of image reconstruction problems (one for each frame). Spatiotemporal approaches instead directly reconstruct the dynamic object using data from all imaging frames at once. Although statistically …