Neurological disorders results in great clinical challenges and high societal burdens. Currently multi-contrast MRI exams are frequently used for diagnosis because of the various tissue contrasts provides complementary diagnosis information to distinguish normal tissue from pathology. However, the cost of acquiring these multiple sequences is extensive scanning time, which significantly increases both the diagnosis cost and patients’ discomfort. Here we proposed a new approach to accelerate multi-contrast imaging by using Parallel Imaging, Compressed Sensing and sharable information. We validated the new approach with experiments on both patients and healthy subjects. We demonstrate that we can reduce the multi-contrast MRI scanning time significantly while preserving the diagnostic information.