6 papers
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…
Physics-Guided Dual-Domain Network with Attention-Based Fusion for Portable MRI Reconstruction
Efe Ilıcak, Baris Imre, Chloé Najac +4
Portable low-field magnetic resonance imaging (MRI) systems have gained renewed interest owing to their cost effectiveness and point-of-care imaging capabilities. Yet, portable MRI…
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…
Subject grounding to reduce electromagnetic interference for MRI scanners operating in unshielded environments
Beatrice Lena, Bart de Vos, Teresa Guallart-Naval +6
Purpose. Portable low-field (< 0.1 T) MRI is increasingly used for point-of-care imaging, but electromagnetic interference (EMI) presents a significant challenge, especially in uns…
Design and performance of a Toroidal RF Volume Coil with Intrinsic Electromagnetic Interference Rejection for low-field Portable Halbach-Based MRI Systems
Jules Vliem, Najac Chloe, Beatrice Lena +2
Purpose: One of the intrinsic limitations of low-field MRI is low signal-to-noise ratio (SNR), which can be further reduced by electromagnetic interference (EMI) due to the lack of…
Deep learning of personalized priors from past MRI scans enables fast, quality-enhanced point-of-care MRI with low-cost systems
Tal Oved, Beatrice Lena, Chloé F. Najac +4
Magnetic resonance imaging (MRI) offers superb-quality images, but its accessibility is limited by high costs, posing challenges for patients requiring longitudinal care. Low-field…