Abstract: Magnetic Resonance Imaging (MRI) has been extensively employed as an auxiliary means to diagnose pathological deterioration of brain, spinal cord and heart related diseases clinically. Nevertheless, imaging noise induced by both internal and external impacts restrict further improvement on diagnostic accuracy. This paper carries out a review on technological innovations ranging from earlier conventional approaches based on filter technique o state-of-the-art alternatives utilizing the deep learning network. Finally, some inductive summaries of the medical image quality assessments have been introduced. It also points out that existing deep learning methods, which rely on a large amount of data and manual annotation of medical image samples, are poorly interpretable. It is vital that clinical-oriented evaluation mechanism should be explored for clinical demands.