Search Results for author:
Found 9 papers, 5 papers with code
NoSENSE: Learned unrolled cardiac MRI reconstruction without explicit sensitivity maps
We present a novel learned image reconstruction method for accelerated cardiac MRI with multiple receiver coils based on deep convolutional neural networks (CNNs) and algorithm unrolling.
Quantitative MR Image Reconstruction using Parameter-Specific Dictionary Learning with Adaptive Dictionary-Size and Sparsity-Level Choice
Objective: We propose a method for the reconstruction of parameter-maps in Quantitative Magnetic Resonance Imaging (QMRI).
PINQI: An End-to-End Physics-Informed Approach to Learned Quantitative MRI Reconstruction
While various learned and non-learned approaches have been proposed, the existing learned methods fail to fully exploit the prior knowledge about the underlying MR physics, i. e. the signal model and the acquisition model.
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.
