Magnetic resonance imaging (MRI) is the preferred modality among radiologists in the clinic due to its superior depiction of tissue contrast. Its ability to capture different contrasts within an exam session allows it to collect additional diagnostic information. However, such multi-contrast MRI exams take a long time to scan, resulting in acquiring just a portion of the required contrasts. Consequently, synthetic multi-contrast MRI can improve subsequent radiological observations and image analysis tasks like segmentation and detection. Because of this significant potential, multi-contrast MRI synthesis approaches are gaining popularity. Recently, generative adversarial networks (GAN) have become the de facto choice for synthesis tasks in medical imaging due to their sensitivity to realism and high-frequency structures. In this study, we present a novel generative adversarial approach for multi-contrast MRI synthesis that combines the learning of deep residual convolutional networks and spatial modulation introduced by an attention gating mechanism to synthesize high-quality MR images. We show the superiority of the proposed approach against various synthesis models on multi-contrast MRI datasets.