The purpose of this work was to develop and evaluate a new deep-learning based image reconstruction framework, termed as Sampling-Augmented Neural neTwork with Incoherent Structure (SANTIS) for MR image reconstruction. Our approach combines efficient end-to-end CNN mapping with k-space consistency using the concept of cyclic loss to enforce data fidelity. Adversarial training is implemented for maintaining high quality perceptional image structure and incoherent k-space sampling is used to improve reconstruction accuracy and robustness. The performance of SANTIS was demonstrated for reconstructing vast undersampled Cartesian knee images and golden-angle radial liver images. Our study demonstrated that the proposed SANTIS framework represents a promising approach for efficient and robust MR image reconstruction at vast acceleration rate.