Abstract: Denoising of magnetic resonance (MR) images can be achieved using a deep neural network and an image acquisition process that uses multi-NEX (Number of Excitations) or multi-NSA (Number of Signal Averages or Acquisitions) to produce two or more complex-valued images of a region of interest. The set of images resulting from the image acquisition process can be input to a deep neural network that has been trained to produce a denoised MR image from a set of multi-NEX or multi-NSA images. The deep neural network can be implemented using a two-dimensional or three-dimensional convolutional neural network to match the dimensionality of the input images. Training of the denoising neural network can use real MR images.