Numerous studies have recently employed deep learning (DL) for accelerated MRI reconstruction. Physics-based DL-MRI techniques unroll an iterative optimization procedure into a recurrent neural network, by alternating between linear data consistency and neural network-based regularization units. Data consistency unit typically implements a gradient step. We hypothesize that further gains can be achieved by allowing dense connections within unrolled network, facilitating information flow. Thus, we propose to unroll a Nesterov-accelerated gradient descent that considers the history of previous iterations. Results indicate that this method considerably improves reconstruction over unrolled gradient descent schemes without skip connections.