Respiratory motion is a big problem in abdominal T2W imaging. PROPELLER sequence is an excellent solution for motion artifact. However, it requires longer acquisition than FSE, limiting its wider adoption in clinical situations. Here, we propose to use a deep learning-based parallel imaging reconstruction for accelerating PROPELLER. Our approach applies deep learning to the reconstruction of blade images. Thus, training is robust to respiratory motion because blade data can be obtained with single shot. Preliminary results showed that the proposed method significantly outperformed SENSE reconstruction.