Motion artifacts in MRI degrade image quality typically with blurring or ghosting across the image along the phase encoding direction, and severe motion artifacts may result in non-diagnostic exams. We developed a deep learning model based on a densely connected residual network (DRN) with K-space blending (DRN-KB) method in a purpose to reduce motion artifacts in brain MRI. Our DRN model took advantage of residual learning and dense connection to achieve higher performance in motion reduction compared with denoising convolutional neural network (DnCNN). In addition, to overcome over-smoothing and reduced tissue contrast in the motion-reduction images produced by DRN, K-space blending was performed that part of central K-space of the original motion image were reserved whereas the remaining K-space was replaced by the motion-reduction images from DRN. The final MRI images were reconstructed using the blended K-space. The optimal blending ratio of the K-space was determined during the training process. Our DRN model was trained and tested with the axial T1-weighted (T1W) images with simulated motion. Two clinical cases (50 images) were used in training and validation and 16 cases (417 images) were used in testing. Structural SIMilarity (SSIM) index and improvement in signal-to-noise ratio (ISNR) were calculated to evaluate the image quality. Our DRN-KB method reduced motion artifacts with an increased SSIM of motion-reduction images (SSIM:0.95) compared with that of original motion images (SSIM:0.83) and demonstrated an improved SNR (ISNR: 2.89 dB). The performance of DRN-KB method was significantly better than that of the conventional DnCNN (P-value < 0.05).