Deep learning methods are increasingly being used for accelerating magnetic resonance imaging (MRI) acquisition and reconstruction strategies. However, it is important to understand whether or not deep learning models have an inherent structural bias that may effectively create concerns in real-world settings. In this abstract, we show a strategy to understand and then reduce structural prior bias in deep models. The proposed approach decouples the spurious structural bias (prior) of a deep learning model by intervening in the input. Our proposed debiasing strategy is fairly robust and can work with any pre-trained deep learning MR reconstruction model.