Foundations of Intelligent MRI: From Spin Physics to AI Models

Foundations of Intelligent MRI: From Spin Physics to AI Models offers a comprehensive exploration of the physics and mathematics underpinning modern magnetic resonance imaging. The manuscript begins with a rigorous derivation of quantum mechanical foundations, covering spin-1/2 systems, Hilbert space formalism, and the Zeeman Hamiltonian. By detailing the Liouville-von Neumann equation and density matrix formalism, the text establishes a precise theoretical framework for medical physics, providing the necessary groundwork to understand how nuclear spins evolve and are measured within a magnetic field.Building upon these physical foundations, the book transitions into the cutting-edge intersection of medical imaging and artificial intelligence. It introduces a framework for "intelligent" MRI reconstruction, integrating concepts from causal representation learning, Bayesian inverse problems, and robust AI geometry. By bridging the gap between classical signal processing and modern machine learning—specifically referencing causal inference and disentangled representations—the manuscript outlines a future for medical diagnostics where image reconstruction is not only data-driven but also physically consistent and causally aware. Read more