Search Results for author:
Found 34 papers, 18 papers with code
Motion-Robust T2* Quantification from Gradient Echo MRI with Physics-Informed Deep Learning
Results: Our extended version of PHIMO outperforms the learning-based baseline methods both qualitatively and quantitatively with respect to line detection and image quality.
PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRI
Neural implicit k-space representations (NIK) have shown promising results for dynamic magnetic resonance imaging (MRI) at high temporal resolutions.
Subspace Implicit Neural Representations for Real-Time Cardiac Cine MR Imaging
Conventional cardiac cine MRI methods rely on retrospective gating, which limits temporal resolution and the ability to capture continuous cardiac dynamics, particularly in patients with arrhythmias and beat-to-beat variations.
Highly efficient non-rigid registration in k-space with application to cardiac Magnetic Resonance Imaging
The accelerated scans in such applications result in imaging artifacts that compromise the motion estimation.
Attention Incorporated Network for Sharing Low-rank, Image and K-space Information during MR Image Reconstruction to Achieve Single Breath-hold Cardiac Cine Imaging
To address these limitations, we propose to embed information from multiple domains, including low-rank, image, and k-space, in a novel deep learning network for MRI reconstruction, which we denote as A-LIKNet.
Direct Cardiac Segmentation from Undersampled K-space Using Transformers
The prevailing deep learning-based methods of predicting cardiac segmentation involve reconstructed magnetic resonance (MR) images.
Attention-aware non-rigid image registration for accelerated MR imaging
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.
Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation
Neural implicit k-space representations have shown promising results for dynamic MRI at high temporal resolutions.
Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI
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
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
Approaches that rely on convolutional neural networks (CNNs) are limited to grid-like inputs and not easily applicable to sparse or partial measurements.
ICoNIK: Generating Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit Representations in k-Space
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
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.
Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modeling
We evaluate our approach on 92 in-house 2D+t cardiac MR subjects and compare it to MR reconstruction methods with image-domain regularizers.
Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review
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
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.
Motion-compensated MR CINE reconstruction with reconstruction-driven motion estimation
In cardiac CINE, motion-compensated MR reconstruction (MCMR) is an effective approach to address highly undersampled acquisitions by incorporating motion information between frames.
Neural Implicit k-Space for Binning-free Non-Cartesian Cardiac MR Imaging
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.
Learning-based and unrolled motion-compensated reconstruction for cardiac MR CINE imaging
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.
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.
Embedding Gradient-based Optimization in Image Registration Networks
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.
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.
