activity
20242026
collaborators

10 papers

eess.IV2026

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…

cs.CV2026

Conditional Flow Matching for Cross-Field MRI Harmonisation

Baris Imre, Aram Salehi, Levente Baljer +3

Magnetic resonance images of the same subject look markedly different across field strengths, which complicates the comparison and pooling of data across sites. We address cross-fi…

cs.CV2026

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…

eess.IV2026

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…

cs.CV2026

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.med-ph2025

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…

cs.CV2025

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…

physics.med-ph2025

An imageless magnetic resonance framework for fast and cost-effective decision-making

Alba González-Cebrián, Pablo García-Cristóbal, Fernando Galve +5

Magnetic Resonance Imaging (MRI) is the gold standard in countless diagnostic procedures, yet hardware complexity, long scans, and cost preclude rapid screening and point-of-care u…

cs.CV2025

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…

eess.IV2024

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…