Image reconstruction in computed tomography has undergone a paradigm shift as a result of recent advancements in deep learning for computer vision applications. The two primary methodologies of deep sparse view reconstruction are image quality enhancement and stochastic reconstruction. The former adapts deep learning networks to reduce noise and strike artifacts from the sparse view reconstruction. The networks in the data domain interpolate missing sinogram data (i.e., Radon transform data) of sparse view reconstruction by learning from complete projection sinograms. The networks in the image domain restore the image quality by learning to detect and subtract streak-type artifacts from the undersampled reconstruction images. Either by learning from data or images, these deep learning models incorporate analytical transform (e.g., Radon transform) as physical-based knowledge for image reconstructions, cutting the number of projection views needed from hundreds to dozens. However, these methods still face the bottleneck of reconstructing images from single-digit views since they need enough views to perform the reconstruction via the Radon transform for further quality enhancement. An alternative approach is stochastic reconstruction. Stochastic reconstruction utilizes deep convolutional generative adversarial networks (GANs) as layered architecture to extract and reproduce the hierarchical features of image datasets. From representative 2D CT scan slices, GANs may stochastically generate a statistically equivalent 3D structure of porous media, 17 three-phase electrodes, and anisotropic polymer membranes. The synthetic volumes via stochastic approaches demonstrate statistical similarity with the original datasets, while they cannot reveal the real 3D structure from the input 2D images. The loss of localized geometric information is inevitable. Therefore, they are ineffective for transient in situ and operando studies or heterogeneity studies, where realistic variations in localized properties across time or space matter.