Multi Coil

Multi-coil magnetic resonance imaging (MRI) leverages multiple receiver coils to acquire richer data, enabling faster scans and improved image quality through parallel imaging techniques. Current research focuses on developing deep learning models, including generative adversarial networks (GANs), diffusion models, and variational networks, to reconstruct high-quality images from undersampled multi-coil data, often incorporating self-supervised learning to reduce reliance on fully-sampled training datasets. These advancements aim to reduce scan times, improve image resolution, and enhance the robustness of MRI reconstruction, ultimately impacting clinical workflows and diagnostic capabilities.