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Found 16 papers, 5 papers with code
A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis
Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation.
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging
We consider inverse problems with both linear and non-linear forward models for computational MRI, and review the classical approaches for solving these.
Differentially private training of residual networks with scale normalisation
The training of neural networks with Differentially Private Stochastic Gradient Descent offers formal Differential Privacy guarantees but introduces accuracy trade-offs.
GraDIRN: Learning Iterative Gradient Descent-based Energy Minimization for Deformable Image Registration
The forward pass of the network takes explicit image dissimilarity gradient steps and generalized regularization steps parameterized by Convolutional Neural Networks (CNN) for a fixed number of iterations.
Complex-valued deep learning with differential privacy
We present $\zeta$-DP, an extension of differential privacy (DP) to complex-valued functions.
The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images
Our dataset contains short axis images from 4 different MR scanners and 3 different pathology groups.
Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data
The framework consists of a deep learning model for the reconstruction of 2D+t cardiac cine MRI images from undersampled data, an image quality-control step to detect good quality reconstructions, followed by a deep learning model for bi-ventricular segmentation, a quality-control step to detect good quality segmentations and automated calculation of cardiac functional parameters.
Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation
In this paper, we present a cooperative framework for training image segmentation models and a latent space augmentation method for generating hard examples.
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction
To this end, we solve the linear inverse problem of undersampled MRI reconstruction in a variational setting.
Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction
The iterative model is embedded into a deep recurrent neural network which learns to recover the image via exploiting spatio-temporal redundancies in complementary domains.
CG-SENSE revisited: Results from the first ISMRM reproducibility challenge
The reference implementations were in good agreement, both visually and in terms of image similarity metrics.
Deep Network Interpolation for Accelerated Parallel MR Image Reconstruction
We present a deep network interpolation strategy for accelerated parallel MR image reconstruction.
$Σ$-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction
Purpose: To systematically investigate the influence of various data consistency layers, (semi-)supervised learning and ensembling strategies, defined in a $\Sigma$-net, for accelerated parallel MR image reconstruction using deep learning.
$Σ$-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction
We explore an ensembled $\Sigma$-net for fast parallel MR imaging, including parallel coil networks, which perform implicit coil weighting, and sensitivity networks, involving explicit sensitivity maps.
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction
Both linear and non-linear methods are covered, followed by a discussion of recent efforts to further improve parallel imaging using machine learning, and specifically using artificial neural networks.
Learning a Variational Network for Reconstruction of Accelerated MRI Data
Due to its high computational performance, i. e., reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow.
