Unsupervised Conditional Generative Latent Optimization for Sparse-View Computed Tomography Image Reconstruction

An unsupervised conditional approach to the Generative Latent Optimization framework (cGLO) that benefits from the structural bias of a decoder network and a shared objective between multiple slices to reconstruct sparse-view CT images without requiring a large training dataset.