  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 2D and
3D knee MRI reconstruction with retrospective under-sampling. Quantitative
metrics including NRMSE, SSIM, and our proposed UFLoss were used to evaluate
the performance of the proposed method and compare it with others.
  Results: In-vivo experiments indicate that adding the UFLoss encourages
sharper edges and more faithful contrasts compared to traditional and
learning-based methods with pure l2 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 unsupervised learned feature
loss, which allows the training of DL-based reconstruction to obtain more
detailed texture, finer features, and sharper edges with higher overall image
quality under DL-based reconstruction frameworks.
