Being the state-of-the-art parallel magnetic resonance imaging methods other than the deep learning approaches, the low-rank Hankel approaches embrace the advantage of holding low reconstruction errors. However, they demand intensive computations and high memory consumptions, thereby result in long reconstruction time. We proposed a new strategy for exploiting the low rankness and applied it to accelerate 2D imaging and T2 mapping. It is shown that the proposed method outperforms the state-of-the-art approaches in terms of lower reconstruction errors and more accurate mapping estimations. Besides, the proposed method required much less computation and memory consumption.