Deep Learning-Based Image Reconstruction in Musculoskeletal MRI

MRI plays a vital role in obtaining high-quality images for evaluating the complex anatomical structures of the musculoskeletal system. However, its long acquisition time can lead to patient discomfort and motion artifacts, which degrade image quality. To overcome this limitation, parallel imaging techniques such as Sensitivity Encoding (SENSE) and GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) were developed, followed by compressed sensing, which reconstructs images from undersampled k-space data using iterative methods. More recently, deep learning-based image reconstruction techniques have emerged, offering improved signal-to-noise ratio and higher acceleration factors. Recent studies evaluating various joints-including the spine, knee, ankle, and shoulder-have shown that deep learning-based reconstruction significantly reduces scan times while maintaining image quality and diagnostic performance comparable to conventional methods, supporting broader clinical application. Additionally, ongoing research aims to enhance image resolution in low-field MRI systems and correct various artifacts, further expanding the potential of these advanced techniques.