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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.
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Hardcover. Zustand: new. Hardcover. This book develops a rigorous, end-to-end theory of intelligent magnetic resonance imaging by unifying the physics of spin systems, the mathematics of inverse problems, and the modern machinery of artificial intelligence. Beginning from the quantum and semiclassical foundations of nuclear magnetic resonance, it builds systematically through Bloch dynamics, signal formation, Fourier encoding, image reconstruction, parameter estimation, uncertainty quantification, and the geometry of learned representations. Across the chapters, MRI is treated not simply as an imaging modality but as a layered computational-physical system in which measurements, models, and inference are inseparably linked. The book shows how classical tools such as Hilbert-space analysis, regularization theory, stochastic processes, and optimization evolve naturally into contemporary methods involving deep neural networks, graph models, generative priors, diffusion reconstruction, Bayesian inference, and operator learning. At the same time, the book argues that the future of MRI lies in systems that are not merely automated, but mathematically grounded, adaptive, and scientifically interpretable. It examines how AI can guide acquisition, reconstruction, multimodal fusion, motion correction, artifact suppression, quantitative imaging, and even agentic workflow orchestration across the entire MRI pipeline. Throughout, the emphasis remains on preserving physical consistency, clinical trustworthiness, and theoretical clarity while extending MRI into a new era of intelligent imaging. The result is a comprehensive research-level treatment of how magnetic resonance imaging is being transformed from a sequence of handcrafted procedures into a unified framework of physics-constrained, data-driven, and increasingly autonomous inference. A rigorous research-level book on the physics, mathematics, and artificial intelligence of MRI, showing how spin dynamics, inverse problems, and modern AI come together to create intelligent imaging systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798904174897
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Hardcover. Zustand: new. Hardcover. This book develops a rigorous, end-to-end theory of intelligent magnetic resonance imaging by unifying the physics of spin systems, the mathematics of inverse problems, and the modern machinery of artificial intelligence. Beginning from the quantum and semiclassical foundations of nuclear magnetic resonance, it builds systematically through Bloch dynamics, signal formation, Fourier encoding, image reconstruction, parameter estimation, uncertainty quantification, and the geometry of learned representations. Across the chapters, MRI is treated not simply as an imaging modality but as a layered computational-physical system in which measurements, models, and inference are inseparably linked. The book shows how classical tools such as Hilbert-space analysis, regularization theory, stochastic processes, and optimization evolve naturally into contemporary methods involving deep neural networks, graph models, generative priors, diffusion reconstruction, Bayesian inference, and operator learning. At the same time, the book argues that the future of MRI lies in systems that are not merely automated, but mathematically grounded, adaptive, and scientifically interpretable. It examines how AI can guide acquisition, reconstruction, multimodal fusion, motion correction, artifact suppression, quantitative imaging, and even agentic workflow orchestration across the entire MRI pipeline. Throughout, the emphasis remains on preserving physical consistency, clinical trustworthiness, and theoretical clarity while extending MRI into a new era of intelligent imaging. The result is a comprehensive research-level treatment of how magnetic resonance imaging is being transformed from a sequence of handcrafted procedures into a unified framework of physics-constrained, data-driven, and increasingly autonomous inference. A rigorous research-level book on the physics, mathematics, and artificial intelligence of MRI, showing how spin dynamics, inverse problems, and modern AI come together to create intelligent imaging systems. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Bestandsnummer des Verkäufers 9798904174897
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Hardcover. Zustand: new. Hardcover. This book develops a rigorous, end-to-end theory of intelligent magnetic resonance imaging by unifying the physics of spin systems, the mathematics of inverse problems, and the modern machinery of artificial intelligence. Beginning from the quantum and semiclassical foundations of nuclear magnetic resonance, it builds systematically through Bloch dynamics, signal formation, Fourier encoding, image reconstruction, parameter estimation, uncertainty quantification, and the geometry of learned representations. Across the chapters, MRI is treated not simply as an imaging modality but as a layered computational-physical system in which measurements, models, and inference are inseparably linked. The book shows how classical tools such as Hilbert-space analysis, regularization theory, stochastic processes, and optimization evolve naturally into contemporary methods involving deep neural networks, graph models, generative priors, diffusion reconstruction, Bayesian inference, and operator learning. At the same time, the book argues that the future of MRI lies in systems that are not merely automated, but mathematically grounded, adaptive, and scientifically interpretable. It examines how AI can guide acquisition, reconstruction, multimodal fusion, motion correction, artifact suppression, quantitative imaging, and even agentic workflow orchestration across the entire MRI pipeline. Throughout, the emphasis remains on preserving physical consistency, clinical trustworthiness, and theoretical clarity while extending MRI into a new era of intelligent imaging. The result is a comprehensive research-level treatment of how magnetic resonance imaging is being transformed from a sequence of handcrafted procedures into a unified framework of physics-constrained, data-driven, and increasingly autonomous inference. A rigorous research-level book on the physics, mathematics, and artificial intelligence of MRI, showing how spin dynamics, inverse problems, and modern AI come together to create intelligent imaging systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798904174897
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