Search Results for author: Kerstin Hammernik

Found 29 papers, 13 papers with code

Attention-aware non-rigid image registration for accelerated MR imaging

1 code implementation26 Apr 2024 Aya Ghoul, Jiazhen Pan, Andreas Lingg, Jens Kübler, Patrick Krumm, Kerstin Hammernik, Daniel Rueckert, Sergios Gatidis, Thomas Küstner

The proposed method was evaluated on in-house acquired fully sampled and accelerated data of 101 patients and 62 healthy subjects undergoing cardiac and thoracic MRI.

Image Registration Motion Estimation

Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI

1 code implementation13 Mar 2024 Hannah Eichhorn, Veronika Spieker, Kerstin Hammernik, Elisa Saks, Kilian Weiss, Christine Preibisch, Julia A. Schnabel

We demonstrate the potential of PHIMO for the application of T2* quantification from gradient echo MRI, which is particularly sensitive to motion due to its sensitivity to magnetic field inhomogeneities.

Propagation and Attribution of Uncertainty in Medical Imaging Pipelines

1 code implementation28 Sep 2023 Leonhard F. Feiner, Martin J. Menten, Kerstin Hammernik, Paul Hager, Wenqi Huang, Daniel Rueckert, Rickmer F. Braren, Georgios Kaissis

In this paper, we propose a method to propagate uncertainty through cascades of deep learning models in medical imaging pipelines.

NISF: Neural Implicit Segmentation Functions

1 code implementation15 Sep 2023 Nil Stolt-Ansó, Julian McGinnis, Jiazhen Pan, Kerstin Hammernik, Daniel Rueckert

Approaches that rely on convolutional neural networks (CNNs) are limited to grid-like inputs and not easily applicable to sparse or partial measurements.

Cardiac Segmentation Image Segmentation +2

ICoNIK: Generating Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit Representations in k-Space

1 code implementation17 Aug 2023 Veronika Spieker, Wenqi Huang, Hannah Eichhorn, Jonathan Stelter, Kilian Weiss, Veronika A. Zimmer, Rickmer F. Braren, Dimitrios C. Karampinos, Kerstin Hammernik, Julia A. Schnabel

Motion-resolved reconstruction for abdominal magnetic resonance imaging (MRI) remains a challenge due to the trade-off between residual motion blurring caused by discretized motion states and undersampling artefacts.

The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning

no code implementations15 Aug 2023 Denis Prokopenko, Kerstin Hammernik, Thomas Roberts, David F A Lloyd, Daniel Rueckert, Joseph V Hajnal

We show that the best-performers recover a detailed depiction of the maternal anatomy on a large scale, but the dynamic properties of the fetal heart are under-represented.

Anatomy MRI Reconstruction

Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review

no code implementations11 May 2023 Veronika Spieker, Hannah Eichhorn, Kerstin Hammernik, Daniel Rueckert, Christine Preibisch, Dimitrios C. Karampinos, Julia A. Schnabel

To facilitate the transfer of ideas between different applications, this review provides a detailed overview of proposed methods for learning-based motion correction in MRI together with their common challenges and potentials.

Physics-Aware Motion Simulation for T2*-Weighted Brain MRI

1 code implementation20 Mar 2023 Hannah Eichhorn, Kerstin Hammernik, Veronika Spieker, Samira M. Epp, Daniel Rueckert, Christine Preibisch, Julia A. Schnabel

As T2*-weighted MRI is highly sensitive to motion-related changes in magnetic field inhomogeneities, it is of utmost importance to include physics information in the simulation.

Line Detection

Reconstruction-driven motion estimation for motion-compensated MR CINE imaging

no code implementations5 Feb 2023 Jiazhen Pan, Wenqi Huang, Daniel Rueckert, Thomas Küstner, Kerstin Hammernik

Contrary to state-of-the-art (SOTA) MCMR methods which break the original problem into two sub-optimization problems, i. e. motion estimation and reconstruction, we formulate this problem as a single entity with one single optimization.

Motion Estimation

Neural Implicit k-Space for Binning-free Non-Cartesian Cardiac MR Imaging

no code implementations16 Dec 2022 Wenqi Huang, Hongwei Li, Jiazhen Pan, Gastao Cruz, Daniel Rueckert, Kerstin Hammernik

While existing methods bin acquired data from neighboring time points to reconstruct one phase of the cardiac motion, our framework allows for a continuous, binning-free, and subject-specific k-space representation. We assign a unique coordinate that consists of time, coil index, and frequency domain location to each sampled k-space point.

Image Reconstruction

Learning-based and unrolled motion-compensated reconstruction for cardiac MR CINE imaging

no code implementations8 Sep 2022 Jiazhen Pan, Daniel Rueckert, Thomas Küstner, Kerstin Hammernik

Motion-compensated MR reconstruction (MCMR) is a powerful concept with considerable potential, consisting of two coupled sub-problems: Motion estimation, assuming a known image, and image reconstruction, assuming known motion.

Image Reconstruction Motion Estimation

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.

Embedding Gradient-based Optimization in Image Registration Networks

1 code implementation7 Dec 2021 Huaqi Qiu, Kerstin Hammernik, Chen Qin, Chen Chen, Daniel Rueckert

Deep learning (DL) image registration methods amortize the costly pair-wise iterative optimization by training deep neural networks to predict the optimal transformation in one fast forward-pass.

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

2 code implementations2 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 +4

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.

BIG-bench Machine Learning MRI Reconstruction

Learning a Variational Network for Reconstruction of Accelerated MRI Data

2 code implementations3 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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