Blind quality estimation of compressed sensing MRI

This paper presents a blind objective measure for visual quality of reconstructed MRI scans of various tissues. We analyze MRI data gathered using a customized k-space trajectory compressed sensing method which introduces visual artifacts. The goal of the proposed metric is to set a threshold for the visual quality of the sparse reconstruction in order to speed up the process of MRI acquisition and guarantee an image with a satisfactory quality for making the correct diagnosis. We use state-of-the-art machine learning for regression of local degradation features into local SSIM estimates which correlates well with human perception of visual quality. Experimental results show that a hard threshold can be applied for the needed quality during compressed sensing MRI acquisition in order to obtain optimal images for diagnosis.