An automatic region-based no-reference image quality evaluation method

Motivation: Clinicians are more concerned about areas of diagnostic significance, but there is a lack of quality evaluation method for region-based MRI images. Goal(s): Develop a deep learning-based automatic method for MRI interested region segmentation and image quality quantitative assessment. Approach: A segmentation model is trained to identify clinical regions of interest in MRI, and then automatically evaluate the quality of the extracted regions quantitatively. Results: The method has achieved good performance in segmentation and evaluation of multiple anatomies. Clinically, the image quality assessment results based on region of interest are consistent with the results evaluated by radiologists. Impact: Conventional automatic image quality assessment approaches rely on the full image. The method proposed in this paper pays more attention on anatomical regions of interest to clinicians, yielding results that better meet clinical needs.