MRI is a critical diagnostic tool in medical practice, enabling non-invasive visualization of anatomy and physiological processes. Nonetheless, MRI has spatial resolution limitations, which may limit its diagnostic capabilities. Recently, a new technology employing Radio-frequency Amplification by Stimulated emission of Radiation (RASER) has emerged to improve MRI resolution. Similar to a laser, RASER-MRI signals spontaneously emerge without the need for a radiofrequency-pulse, which additionally enhances the safety ofthe process. However, RASER-MRI images frequently exhibit a significant presence of image artifacts due to the nonlinear behavior. Our work aims to determine whether the image artifacts can be eliminated using deep artificial neural networks. The proposed method involves training a deep artificial neural network on pure synthetic data. The data is generated by simulating RASER-signals from known image slices. Each image slice results in several RASER signals containing image artifacts which serve as input into our model. The model is supposed to reconstruct the image slice free of artifacts.