X-ray computed Tomography (XCT) tools are non-invasive imaging tools which can capture sub-surface morphological and structural features in a range of materials and material systems. The fidelity of microstructure representation is essential for material property estimation, simulation, and materials design. Three-dimensional reconstructions of materials are often utilized to accurately model components and critically assess degradation and material transformation pathways. The resiliency of any quantitative assessment is highly dependent on the reconstruction of a 3D image from hundreds of 2D radiographic images acquired from different projection angles . The inverse Radon transform provides the mathematical basis for the conventional reconstruction process. Thousands of 2-D images at varying angles are typically acquired in order to reconstruct images into a 3D rendering. For dynamic or operando investigations, where you combine an action with imaging (e.g. imaging particles flowing), it is challenging to take thousands of images in the time period of the action. Thus, 3D imaging which requires fast acquisition times is fundamentally limited. Sparse view reconstruction approaches attempt to reduce the number of 2D images (e.g. sampling rate) necessary for 3D reconstruction. Decreasing the sampling rate enables transient in situ and operando studies. Reducing the number of images needed for a reconstruction can have the added benefit of less instrument maintenance, longer system lifetime, and simpler system design and hardware. Despite the promising features, sparse view reconstruction via conventional reconstruction methods suffers from the inevitable information loss that hinders correct image interpretation and quantification.