This work presents a deep learning approach to reconstruct MR images from undersampled k-space on 3D-FLAIR MR images. IR-net, a patch based 3D-Dense U-net, was designed to achieve this. 600 [JM1] [CGBY2] 3D-FLAIR MR images were used for training and testing. Aliased images were created by undersampling the high resolution 3D-FLAIR images in k-space using a Poisson distribution filter. The network was trained on patches from 550 aliased k-space data with their corresponding high resolution 3D-FLAIR MR images as ground truth and 50 images were held out for testing. IR-net successfully reconstructed the aliased images with significant improvement in SSIM and PSNR. [JM1]Are these 600 image slices, or 600 3D image volumes? [CGBY2]600 3D images.