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High-Fidelity Reconstruction with Instance-wise Discriminative Feature Matching Loss
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Ke Wang and Jonathan I. Tamir and Stella X. Yu and Michael Lustig
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Magna Laude Award, International Society for Magnetic Resonance in Medicine, Online, 8-14 August 2020
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Paper
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Abstract
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Machine-learning based reconstructions have shown great potential to reduce scan time while maintaining high image quality. However, commonly used per-pixel losses for the training don't capture perceptual differences between the reconstructed and the ground truth images, leading to blurring or reduced texture. Thus, we incorporate a novel feature representation-based loss function with the existing reconstruction pipelines (e.g. MoDL), which we called Unsupervised Feature Loss (UFLoss). In-vivo results on both 2D and 3D reconstructions show that the addition of the UFLoss can encourage more realistic reconstructed images with much more detail compared to conventional methods (MoDL and Compressed Sensing).
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Keywords
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MR reconstruction, unsupervised feature learning, instance discrimination,
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