In MRI, motion artifacts are among the most common types of artefacts. They can greatly degrade images and make them unusable for an accurate diagnosis. Traditional methods, such as prospective or retrospective motion correction, are commonly used to avoid or limit the presence of motion artifacts. Recently, several other methods based on deep learning approaches have been proposed to solve this problem. This work tries to enhance the performance of existing deep learning models by making use of additional information present as image priors. The proposed approach has shown promising results and will be further investigated for clinical validity.