313 citations · 354 across the 23 of their papers we have counts for
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Towards Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge
Fanwen Wang, Zi Wang, Yan Li +60
Cardiovascular health is vital to human well-being, and cardiac magnetic resonance (CMR) imaging is considered the {clinical reference standard} for diagnosing cardiovascular disea…
Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxiliary Refinement Network
George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke +1
Cardiac MRI (CMRI) is a cornerstone imaging modality that provides in-depth insights into cardiac structure and function. Multi-contrast CMRI (MCCMRI), which acquires sequences wit…
Deep End-to-end Adaptive k-Space Sampling, Reconstruction, and Registration for Dynamic MRI
George Yiasemis, Jan-Jakob Sonke, Jonas Teuwen
Dynamic MRI enables a range of clinical applications, including cardiac function assessment, organ motion tracking, and radiotherapy guidance. However, fully sampling the dynamic k…
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023
Jun Lyu, Chen Qin, Shuo Wang +47
Cardiac MRI, crucial for evaluating heart structure and function, faces limitations like slow imaging and motion artifacts. Undersampling reconstruction, especially data-driven alg…
End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRI
George Yiasemis, Jan-Jakob Sonke, Jonas Teuwen
Accelerating dynamic MRI is vital for advancing clinical applications and improving patient comfort. Commonly, deep learning (DL) methods for accelerated dyn…
Joint Supervised and Self-supervised Learning for MRI Reconstruction
George Yiasemis, Nikita Moriakov, Clara I. Sánchez +2
Magnetic Resonance Imaging (MRI) represents an important diagnostic modality; however, its inherently slow acquisition process poses challenges in obtaining fully-sampled -space…
Deep Cardiac MRI Reconstruction with ADMM
George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke +1
Cardiac magnetic resonance imaging is a valuable non-invasive tool for identifying cardiovascular diseases. For instance, Cine MRI is the benchmark modality for assessing the cardi…
vSHARP: variable Splitting Half-quadratic Admm algorithm for Reconstruction of inverse-Problems
George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke +1
Medical Imaging (MI) tasks, such as accelerated parallel Magnetic Resonance Imaging (MRI), often involve reconstructing an image from noisy or incomplete measurements. This amounts…
On Retrospective k-space Subsampling schemes For Deep MRI Reconstruction
George Yiasemis, Clara I. Sánchez, Jan-Jakob Sonke +1
Acquiring fully-sampled MRI -space data is time-consuming, and collecting accelerated data can reduce the acquisition time. Employing 2D Cartesian-rectilinear subsampling scheme…
Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction
George Yiasemis, Jan-Jakob Sonke, Clarisa Sánchez +1
Magnetic Resonance Imaging can produce detailed images of the anatomy and physiology of the human body that can assist doctors in diagnosing and treating pathologies such as tumour…
Deep MRI Reconstruction with Radial Subsampling
George Yiasemis, Chaoping Zhang, Clara I. Sánchez +2
In spite of its extensive adaptation in almost every medical diagnostic and examinatorial application, Magnetic Resonance Imaging (MRI) is still a slow imaging modality which limit…
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction
Matthew J. Muckley, Bruno Riemenschneider, Alireza Radmanesh +20
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reco…
Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations
Youssef Beauferris, Jonas Teuwen, Dimitrios Karkalousos +20
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific communi…
i-RIM applied to the fastMRI challenge
Patrick Putzky, Dimitrios Karkalousos, Jonas Teuwen +4
We, team AImsterdam, summarize our submission to the fastMRI challenge (Zbontar et al., 2018). Our approach builds on recent advances in invertible learning to infer models as pres…
Learned SIRT for Cone Beam Computed Tomography Reconstruction
Roeland J. Dilz, Lukas Schröder, Nikita Moriakov +2
We introduce the learned simultaneous iterative reconstruction technique (SIRT) for tomographic reconstruction. The learned SIRT algorithm is a deep learning based reconstruction m…