This document offers a comprehensive overview of Magnetic Resonance Imaging (MRI), beginning with its fundamental physical principles, such as nuclear magnetic resonance and the behavior of protons in magnetic fields, and explaining how signals are generated and detected through excitation and relaxation processes. It then details the complex engineering of an MRI scanner, outlining the function of components like the main magnet, gradient coils, and RF systems, and elucidating how raw signals are transformed into images through spatial encoding and Fourier transforms using k-space. The text further explores various clinical pulse sequences like spin-echo, gradient-recalled echo, and inversion recovery, demonstrating how their parameters are manipulated to create specific tissue contrasts for diagnostic purposes. Finally, it discusses advanced MRI applications such as functional MRI (fMRI) and diffusion-weighted imaging (DWI), delves into the historical development of MRI, compares it with other imaging modalities, and concludes by addressing future trends and advancements, including the role of artificial intelligence and the evolution of hardware towards both ultra-high-field and portable systems.

Research done with the help of artificial intelligence, and presented by two AI-generated hosts.