High fidelity deep learning‐based MRI reconstruction with instance‐wise discriminative feature matching loss

作者：Ke Wang, Jonathan I. Tamir, Alfredo De Goyeneche, Uri Wollner, Rafi Brada, Stella X. Yu, Michael Lustig · 发表于：Magnetic Resonance in Medicine · 年份：2022 · DOI：10.1002/mrm.29227 · 被引用次数：19 · 研究领域：Generative Adversarial Networks and Image Synthesis、Advanced MRI Techniques and Applications、Advanced Image Processing Techniques

PURPOSE: To improve reconstruction fidelity of fine structures and textures in deep learning- (DL) based reconstructions. METHODS: A novel patch-based Unsupervised Feature Loss (UFLoss) is proposed and incorporated into the training of DL-based reconstruction frameworks in order to preserve perceptual similarity and high-order statistics. The UFLoss provides instance-level discrimination by mapping similar instances to similar low-dimensional feature vectors and is trained without any human annotation. By adding an additional loss function on the low-dimensional feature space during training, the reconstruction frameworks from under-sampled or corrupted data can reproduce more realistic images that are closer to the original with finer textures, sharper edges, and improved overall image quality. The performance of the proposed UFLoss is demonstrated on unrolled networks for accelerated two- (2D) and three-dimensional (3D) knee MRI reconstruction with retrospective under-sampling. Quantitative metrics including normalized root mean squared error (NRMSE), structural similarity index (SSIM), and our proposed UFLoss were used to evaluate the performance of the proposed method and compare it with others. RESULTS: loss. More detailed textures can be seen in both 2D and 3D knee MR images. Quantitative results indicate that reconstruction with UFLoss can provide comparable NRMSE and a higher SSIM while achieving a much lower UFLoss value. CONCLUSION: We present UFLoss, a patch-based ...