Using deep feature distances for evaluating the perceptual quality of MR image reconstructions

作者：Philip M. Adamson, Arjun Desai, Jeffrey Dominic, Maya Varma, Christian Bluethgen, Jeff Wood, Ali Syed, Robert D. Boutin, Kathryn J. Stevens, Shreyas Vasanawala, John M. Pauly, Beliz Gunel, Akshay Chaudhari · 发表于：Magnetic Resonance in Medicine · 年份：2025 · DOI：10.1002/mrm.30437 · 被引用次数：2 · 研究领域：Radiology practices and education、Digital Radiography and Breast Imaging、Radiomics and Machine Learning in Medical Imaging

PURPOSE: Commonly used MR image quality (IQ) metrics have poor concordance with radiologist-perceived diagnostic IQ. Here, we develop and explore deep feature distances (DFDs)-distances computed in a lower-dimensional feature space encoded by a convolutional neural network (CNN)-as improved perceptual IQ metrics for MR image reconstruction. We further explore the impact of distribution shifts between images in the DFD CNN encoder training data and the IQ metric evaluation. METHODS: We compare commonly used IQ metrics (PSNR and SSIM) to two "out-of-domain" DFDs with encoders trained on natural images, an "in-domain" DFD trained on MR images alone, and two domain-adjacent DFDs trained on large medical imaging datasets. We additionally compare these with several state-of-the-art but less commonly reported IQ metrics, visual information fidelity (VIF), noise quality metric (NQM), and the high-frequency error norm (HFEN). IQ metric performance is assessed via correlations with five expert radiologist reader scores of perceived diagnostic IQ of various accelerated MR image reconstructions. We characterize the behavior of these IQ metrics under common distortions expected during image acquisition, including their sensitivity to acquisition noise. RESULTS: All DFDs and HFEN correlate more strongly with radiologist-perceived diagnostic IQ than SSIM, PSNR, and other state-of-the-art metrics, with correlations being comparable to radiologist inter-reader variability. Surprisingly, out-o...