Parallel Magnetic Resonance Imaging

Parallel magnetic resonance imaging (MRI) aims to accelerate MRI scans by simultaneously acquiring data from multiple receiver coils, reducing scan times and improving patient comfort. Current research heavily focuses on developing robust deep learning-based reconstruction methods, employing architectures like U-Nets and implicit neural representations, often incorporating self-supervised or unsupervised learning strategies to overcome the limitations of limited fully-sampled training data. These advancements address the inherent ill-posed nature of the reconstruction problem, leading to improved image quality and enabling faster acquisitions, with significant implications for clinical applications and research across various medical imaging domains.