Abstract Multi-contrast MRI (MC-MRI) captures multiple complementary imaging modalities
to aid in radiological decision-making. Given the need for lowering the time cost of multiple …

Abstract Magnetic Resonance Imaging (MRI) is now a widely used modality for providing
multimodal, high-quality soft tissue contrast images with good spatiotemporal resolution but …

Low magnetic field magnetic resonance imaging (MRI)(B 0 B _0< 1 T) is regaining interest in
the magnetic resonance (MR) community as a complementary, more flexible, and cost …

Self-supervised learning (SSL) is an efficient pre-training method for medical image
analysis. However current research is mostly confined to certain modalities consuming …

Reconstruction methods based on deep learning have greatly shortened the data
acquisition time of magnetic resonance imaging (MRI). However, these methods typically …

Magnetic resonance imaging (MRI) is extensively utilized in clinical practice for diagnostic
purposes, owing to its non-invasive nature and remarkable ability to provide detailed …

Purpose To investigate motion compensated, self‐supervised, model based deep learning
(MBDL) as a method to reconstruct free breathing, 3D pulmonary UTE acquisitions. Theory …

Compressed sensing (CS) is a promising tool for reducing sampling costs. Current deep
neural network (NN)-based CS approaches face the challenges of collecting labeled …

Abstract Magnetic Resonance Spectroscopic Imaging (MRSI) is a non-invasive imaging
technique for studying metabolism and has become a crucial tool for understanding …

Objective. The implementation of deep learning in magnetic resonance imaging (MRI) has
significantly advanced the reduction of data acquisition times. However, these techniques …