MRIQA: Subjective Method and Objective Model for Magnetic Resonance Image Quality Assessment

Magnetic Resonance Imaging (MRI) is widely used for medical diagnosis, staging and follow-up of disease. However, MRI images may have artifacts due to various reasons such as patient movement or machine distortion, which may be unintentionally introduced during the procedure of medical image acquisition, processing, etc. These artifacts may affect the effectiveness of diagnosis or even cause false diagnosis. To solve this problem, we propose a general medical image quality assessment (MIQA) methodology, including subjective MIQA procedures and objective MIQA algorithms. We further apply this methodology to MRI images in this paper due to its widespread use in practical applications. We first establish a magnetic resonance imaging quality assessment (MRIQA) database, which contains 3809 MRI images. Then a subjective image quality assessment experiment is conducted by expert doctors according to the diagnostic value of these images, which split all MRI images into 1285 low quality images and 2524 high quality images. We then conduct a baseline deep learning experiment, and propose an attention based MIQANet model to automatically separate MRI images into high quality and low quality based on their diagnosis value. Our proposed method achieves a great quality assessment accuracy of 96.59%. The constructed MRIQA database and proposed MIQA model will be public available to further promote medical IQA research.