Search Results for author: Kerstin Hammernik

Found 16 papers, 5 papers with code

Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging

no code implementations23 Mar 2022 Kerstin Hammernik, Thomas Küstner, Burhaneddin Yaman, Zhengnan Huang, Daniel Rueckert, Florian Knoll, Mehmet Akçakaya

We consider inverse problems with both linear and non-linear forward models for computational MRI, and review the classical approaches for solving these.

MRI Reconstruction

Differentially private training of residual networks with scale normalisation

no code implementations1 Mar 2022 Helena Klause, Alexander Ziller, Daniel Rueckert, Kerstin Hammernik, Georgios Kaissis

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

no code implementations7 Dec 2021 Huaqi Qiu, Kerstin Hammernik, Chen Qin, Daniel Rueckert

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.

Image Reconstruction Image Registration

Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data

no code implementations16 Sep 2021 Ines Machado, Esther Puyol-Anton, Kerstin Hammernik, Gastao Cruz, Devran Ugurlu, Bram Ruijsink, Miguel Castelo-Branco, Alistair Young, Claudia Prieto, Julia A. Schnabel, Andrew P. King

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.

MRI Reconstruction

Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation

1 code implementation2 Jul 2021 Chen Chen, Kerstin Hammernik, Cheng Ouyang, Chen Qin, Wenjia Bai, Daniel Rueckert

In this paper, we present a cooperative framework for training image segmentation models and a latent space augmentation method for generating hard examples.

Data Augmentation Image Reconstruction +2

Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction

1 code implementation22 Dec 2020 Chen Qin, Jinming Duan, Kerstin Hammernik, Jo Schlemper, Thomas Küstner, René Botnar, Claudia Prieto, Anthony N. Price, Joseph V. Hajnal, Daniel Rueckert

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.

De-aliasing Image Reconstruction

$Σ$-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction

1 code implementation18 Dec 2019 Kerstin Hammernik, Jo Schlemper, Chen Qin, Jinming Duan, Ronald M. Summers, Daniel Rueckert

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.

Image Enhancement Image Reconstruction +1

$Σ$-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction

1 code implementation11 Dec 2019 Jo Schlemper, Chen Qin, Jinming Duan, Ronald M. Summers, Kerstin Hammernik

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.

Image Reconstruction SSIM

Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction

no code implementations1 Apr 2019 Florian Knoll, Kerstin Hammernik, Chi Zhang, Steen Moeller, Thomas Pock, Daniel K. Sodickson, Mehmet Akcakaya

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.

MRI Reconstruction

Learning a Variational Network for Reconstruction of Accelerated MRI Data

1 code implementation3 Apr 2017 Kerstin Hammernik, Teresa Klatzer, Erich Kobler, Michael P. Recht, Daniel K. Sodickson, Thomas Pock, Florian Knoll

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.

Image Reconstruction Learning Theory

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