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Found 5 papers, 1 papers with code
Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guarantees
Our numerical results on in-vivo MRI data show that SelfDEQ leads to state-of-the-art performance using only undersampled and noisy training data.
SPICE: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation
However, estimation of accurate CSMs is a challenging problem when measurements are highly undersampled.
Image Reconstruction for MRI using Deep CNN Priors Trained without Groundtruth
We propose a new plug-and-play priors (PnP) based MR image reconstruction method that systematically enforces data consistency while also exploiting deep-learning priors.
Deformation-Compensated Learning for Image Reconstruction without Ground Truth
Deep neural networks for medical image reconstruction are traditionally trained using high-quality ground-truth images as training targets.
Deep Image Reconstruction using Unregistered Measurements without Groundtruth
One of the key limitations in conventional deep learning based image reconstruction is the need for registered pairs of training images containing a set of high-quality groundtruth images.
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