Using Deep Feature Distances for Evaluating MR Image Reconstruction Quality

Evaluation of MR reconstruction methods is challenged by the need for image quality (IQ) metrics which correlate strongly with radiologist-perceived IQ. We explore Deep Feature Distances (DFDs) as MR reconstruction IQ metrics, whereby distances between ground truth and reconstructed MR images are computed in a lower-dimensional feature space encoded by a CNN. In addition to comparing DFDs to two commonly used pixel-based MR IQ metrics in PSNR and SSIM via correlations to radiologist reader scores of MR image reconstructions, we explore the impact of domain shifts between the DFD encoder training data and the evaluated MR images. In particular, we assess two state-of-the-art but "out-of-domain" DFDs with encoders trained on natural images, an in-domain DFD trained on MR images alone, and propose two domain-adjacent DFDs trained on large medical imaging datasets (not limited to MR data). IQ metric performance is assessed via their correlations to 5 expert radiologist reader scores of MR image reconstructions. We make three striking observations: 1) all DFDs out-perform traditional IQ metrics, 2) DFDs performance approaches that of radiologist inter-reader variability, and, 3) surprisingly, out-of-domain DFDs perform comparably as an MR reconstruction IQ metric to in-domain and domain-adjacent DFDs. These results make it evident that DFDs should be used alongside traditional IQ metrics in evaluating MR reconstruction IQ, and suggest that general vision encoders are able to assess visual IQ across image domains.