8 papers
Enhancing Implicit Neural Representations with Image Feature Embedding for Unsupervised Cardiac Cine MRI Reconstruction
Donghang Lyu, Marius Staring, Yiming Dong +3
Cardiac cine Magnetic Resonance Imaging (MRI) is a critical diagnostic tool that provides dynamic insights for radiologists. To accelerate acquisition, under-sampled k-space data i…
Deep Unrolled Networks in Representation Space Applied to MRI Reconstruction
Efe Ilıcak, Baris Imre, Chloé Najac +4
Deep unrolled networks (DUNs) integrate physical forward models with learned regularization in cascaded network architectures, achieving exceptional performance in inverse problems…
A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling
Chinmay Rao, Matthias van Osch, Nicola Pezzotti +8
Since the various MR contrasts of a given anatomy contain redundant information, one contrast can be used to guide the reconstruction of another undersampled contrast acquired subs…
CRUNet-MR-Univ: A Foundation Model for Diverse Cardiac MRI Reconstruction
Donghang Lyu, Marius Staring, Hildo Lamb +1
In recent years, deep learning has attracted increasing attention in the field of Cardiac MRI (CMR) reconstruction due to its superior performance over traditional methods, particu…
Physics-Informed Deep Unrolled Network for Portable MR Image Reconstruction
Efe Ilıcak, Chinmay Rao, Chloé Najac +6
Magnetic resonance imaging (MRI) is the gold standard imaging modality for numerous diagnostic tasks, yet its usefulness is tempered due to its high cost and infrastructural requir…
KP-INR: A Dual-Branch Implicit Neural Representation Model for Cardiac Cine MRI Reconstruction
Donghang Lyu, Marius Staring, Mariya Doneva +2
Cardiac Magnetic Resonance (CMR) imaging is a non-invasive method for assessing cardiac structure, function, and blood flow. Cine MRI extends this by capturing heart motion, provid…