17 citations · 19 across the 7 of their papers we have counts for
15 papers
Data-Efficient Networks for Multi-Contrast MRI Reconstruction based on a Generalized Content/Style Prior
Chinmay Rao, Efe Ilıcak, Matthias J. P. van Osch +5
Multi-contrast MR scans contain redundant structural information that can be leveraged during reconstruction and potentially accelerate acquisition times. This idea has inspired en…
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…
NerT-CA: Efficient Dynamic Reconstruction from Sparse-view X-ray Coronary Angiography
Kirsten W. H. Maas, Danny Ruijters, Nicola Pezzotti +1
Three-dimensional (3D) and dynamic 3D+time (4D) reconstruction of coronary arteries from X-ray coronary angiography (CA) has the potential to improve clinical procedures. However,…
UPCMR: A Universal Prompt-guided Model for Random Sampling Cardiac MRI Reconstruction
Donghang Lyu, Chinmay Rao, Marius Staring +4
Cardiac magnetic resonance imaging (CMR) is vital for diagnosing heart diseases, but long scan time remains a major drawback. To address this, accelerated imaging techniques have b…
Progressive Monitoring of Generative Model Training Evolution
Vidya Prasad, Anna Vilanova, Nicola Pezzotti
While deep generative models (DGMs) have gained popularity, their susceptibility to biases and other inefficiencies that lead to undesirable outcomes remains an issue. With their g…
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…