Artificial intelligence in MRI image Reconstruction: A Comprehensive Review

Abstract

Magnetic resonance imaging (MRI) is an essential diagnostic imaging modality that provides excellent soft-tissue contrast without ionizing radiation; however, prolonged acquisition time remains a major limitation that may reduce patient comfort, increase motion artifacts, and restrict clinical workflow. Conventional reconstruction techniques, including Fourier reconstruction, parallel imaging, and compressed sensing, have improved imaging efficiency but remain constrained by noise amplification, reconstruction artifacts, and limited performance at higher acceleration factors. Recent advances in artificial intelligence (AI), particularly deep learning, have transformed MRI image reconstruction by enabling rapid generation of high-quality images from under sampled k-space data. AI-based reconstruction techniques improve image quality through effective noise suppression, artifact reduction, preservation of anatomical details, and shortened reconstruction time. This review summarizes the fundamental principles of MRI image reconstruction, the evolution from conventional reconstruction methods to AI-assisted approaches, and the major deep learning architectures used in contemporary MRI reconstruction, including convolutional neural networks, U-Net, unrolled optimization networks, generative adversarial networks, and transformer-based models. Furthermore, current clinical applications of neuroimaging, musculoskeletal, cardiovascular, abdominal, and oncologic MRI are discussed alongside existing challenges and future research directions. Overall, AI-assisted MRI reconstruction represents a significant advancement in medical imaging and has considerable potential to improve diagnostic accuracy, clinical workflow, and patient-centred MRI practice by enabling faster, more reliable image reconstruction.