DUNE enables exact data-consistency gradients via VJP when deep unrolled networks operate in representation space, yielding better MRI reconstructions than prior heuristic-DC variants.
IEEE Transactions on Biomedical Engineering pp
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MoE-dqINR factorizes INR-based MRI reconstruction into shared spatial experts plus state-conditioned routing to unify dynamic and quantitative reconstruction at roughly 30 seconds per scan.
citing papers explorer
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Deep Unrolled Networks in Representation Space Applied to MRI Reconstruction
DUNE enables exact data-consistency gradients via VJP when deep unrolled networks operate in representation space, yielding better MRI reconstructions than prior heuristic-DC variants.
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MoE-dqINR: A Unified Mixture-of-Experts Implicit Neural Representation Framework for Scan-Specific Dynamic and Quantitative MRI Reconstruction
MoE-dqINR factorizes INR-based MRI reconstruction into shared spatial experts plus state-conditioned routing to unify dynamic and quantitative reconstruction at roughly 30 seconds per scan.