755 citations · 2k across the 29 of their papers we have counts for
13 papers · 1 filter
Towards a Unified Theoretical Framework for Splitting-based Self-Supervised MRI Reconstruction
Siying Xu, Kerstin Hammernik, Daniel Rueckert +2
The demand for high-resolution, non-invasive imaging continues to drive innovation in magnetic resonance imaging (MRI), but long acquisition times remain a major practical limitati…
Attention Incorporated Network for Sharing Low-rank, Image and K-space Information during MR Image Reconstruction to Achieve Single Breath-hold Cardiac Cine Imaging
Siying Xu, Kerstin Hammernik, Andreas Lingg +5
Cardiac Cine Magnetic Resonance Imaging (MRI) provides an accurate assessment of heart morphology and function in clinical practice. However, MRI requires long acquisition times, w…
Attention-aware non-rigid image registration for accelerated MR imaging
Aya Ghoul, Jiazhen Pan, Andreas Lingg +6
Accurate motion estimation at high acceleration factors enables rapid motion-compensated reconstruction in Magnetic Resonance Imaging (MRI) without compromising the diagnostic imag…
Unlocking Robust Segmentation Across All Age Groups via Continual Learning
Chih-Ying Liu, Jeya Maria Jose Valanarasu, Camila Gonzalez +3
Most deep learning models in medical imaging are trained on adult data with unclear performance on pediatric images. In this work, we aim to address this challenge in the context o…
Adversarial Robustness of MR Image Reconstruction under Realistic Perturbations
Jan Nikolas Morshuis, Sergios Gatidis, Matthias Hein +1
Deep Learning (DL) methods have shown promising results for solving ill-posed inverse problems such as MR image reconstruction from undersampled -space data. However, these appr…
Masked Co-attentional Transformer reconstructs 100x ultra-fast/low-dose whole-body PET from longitudinal images and anatomically guided MRI
Yan-Ran, Wang, Liangqiong Qu +10
Despite its tremendous value for the diagnosis, treatment monitoring and surveillance of children with cancer, whole body staging with positron emission tomography (PET) is time co…
LAPNet: Non-rigid Registration derived in k-space for Magnetic Resonance Imaging
Thomas Küstner, Jiazhen Pan, Haikun Qi +7
Physiological motion, such as cardiac and respiratory motion, during Magnetic Resonance (MR) image acquisition can cause image artifacts. Motion correction techniques have been pro…
Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation
August DuMont Schütte, Jürgen Hetzel, Sergios Gatidis +4
Privacy concerns around sharing personally identifiable information are a major practical barrier to data sharing in medical research. However, in many cases, researchers have no i…
Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation
Karim Armanious, Sherif Abdulatif, Wenbin Shi +6
The concept of biological age (BA), although important in clinical practice, is hard to grasp mainly due to the lack of a clearly defined reference standard. For specific applicati…
Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers
J. Senapati, A. Guha Roy, S. Pölsterl +6
Image segmentation enables to extract quantitative measures from scans that can serve as imaging biomarkers for diseases. However, segmentation quality can vary substantially acros…
Unsupervised Adversarial Correction of Rigid MR Motion Artifacts
Karim Armanious, Aastha Tanwar, Sherif Abdulatif +3
Motion is one of the main sources for artifacts in magnetic resonance (MR) images. It can have significant consequences on the diagnostic quality of the resultant scans. Previously…
ipA-MedGAN: Inpainting of Arbitrary Regions in Medical Imaging
Karim Armanious, Vijeth Kumar, Sherif Abdulatif +3
Local deformations in medical modalities are common phenomena due to a multitude of factors such as metallic implants or limited field of views in magnetic resonance imaging (MRI).…
Organ-based Chronological Age Estimation based on 3D MRI Scans
Karim Armanious, Sherif Abdulatif, Anish Rao Bhaktharaguttu +4
Individuals age differently depending on a multitude of different factors such as lifestyle, medical history and genetics. Often, the global chronological age is not indicative of…