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Found 7 papers, 4 papers with code
Convolutional Dictionary Learning by End-To-End Training of Iterative Neural Networks
Sparsity-based methods have a long history in the field of signal processing and have been successfully applied to various image reconstruction problems.
Convolutional Analysis Operator Learning by End-To-End Training of Iterative Neural Networks
Iterative neural networks - which contain the physical model - can overcome these issues.
An End-To-End-Trainable Iterative Network Architecture for Accelerated Radial Multi-Coil 2D Cine MR Image Reconstruction
The network is based on a computationally light CNN-component and a subsequent conjugate gradient (CG) method which can be jointly trained end-to-end using an efficient training strategy.
Unsupervised Adaptive Neural Network Regularization for Accelerated Radial Cine MRI
We compare the proposed reconstruction scheme to two ground truth-free reconstruction methods, namely a well known Total Variation (TV) minimization and an unsupervised adaptive Dictionary Learning (DIC) method.
Neural Networks-based Regularization for Large-Scale Medical Image Reconstruction
In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction.
Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI with Limited Data
Even when trained on only one single subject without data-augmentation, our approach yields results which are similar to the ones obtained on a large training dataset.
Shearlet-based compressed sensing for fast 3D cardiac MR imaging using iterative reweighting
High-resolution three-dimensional (3D) cardiovascular magnetic resonance (CMR) is a valuable medical imaging technique, but its widespread application in clinical practice is hampered by long acquisition times.
