iR2D2 extends the R2D2 DNN series paradigm with an interlaced dual-series architecture and error-controlled updates to jointly reconstruct MR images and self-calibrate sensitivity maps from undersampled radial k-space data.
Learned primal-dual reconstruction
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LRMC is a deep-unfolded non-convex algorithm for large-scale robust matrix completion that learns its parameters for linear convergence and better empirical results than prior methods.
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Interlaced R2D2 DNN Series for Scalable Non-Cartesian MRI with Sensitivity Self-calibration
iR2D2 extends the R2D2 DNN series paradigm with an interlaced dual-series architecture and error-controlled updates to jointly reconstruct MR images and self-calibrate sensitivity maps from undersampled radial k-space data.
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Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery
LRMC is a deep-unfolded non-convex algorithm for large-scale robust matrix completion that learns its parameters for linear convergence and better empirical results than prior methods.