Principles of Magnetic Resonance Imaging: A Signal Processing Perspective
In 1971 Dr. Paul C. Lauterbur pioneered spatial information encoding principles that made image formation possible by using magnetic resonance signals. Now Lauterbur, "father of the MRI", and Dr. Zhi-Pei Liang have co-authored the first engineering textbook on magnetic resonance imaging. This long-awaited, definitive text will help undergraduate and graduate students of biomedical engineering, biomedical imaging scientists, radiologists, and electrical engineers gain an in-depth understanding of MRI principles.
The authors use a signal processing approach to describe the fundamentals of magnetic resonance imaging. You will find a clear and rigorous discussion of these carefully selected essential topics:
- Mathematical fundamentals
- Signal generation and detection principles
- Signal characteristics
- Signal localization principles
- Image reconstruction techniques
- Image contrast mechanisms
- Image resolution, noise, and artifacts
- Fast-scan imaging
- Constrained reconstruction
Why Read This Book
You should read this book if you want a rigorous, engineering-oriented treatment of MRI that frames image formation, sampling, and reconstruction directly in signal-processing terms. You will gain the mathematical intuition and practical tools to analyze k-space, design reconstruction filters, and evaluate SNR and artifact trade-offs in MRI systems.
Who Will Benefit
Graduate students, biomedical engineers, and DSP engineers working on medical imaging or reconstruction algorithms who need a principled, mathematical treatment of MRI signal formation and processing.
Level: Advanced — Prerequisites: Signals and systems (Fourier transforms), linear algebra, basic probability/noise concepts, multivariable calculus, and introductory NMR/physics (helpful but not strictly required).
Key Takeaways
- Describe MRI signal generation and spatial encoding in k-space using Fourier analysis.
- Apply FFT-based and gridding/nonuniform reconstruction methods to Cartesian and non-Cartesian sampled data.
- Analyze the effects of sampling, resolution, and filtering on image quality and artifacts.
- Quantify SNR, noise propagation, and measurement trade-offs in MRI acquisitions.
- Interpret pulse sequence design and relaxation effects from a signal-processing perspective.
Topics Covered
- Mathematical and signal-processing fundamentals for MRI (Fourier transforms, sampling, linear systems)
- Basic NMR physics and magnetization dynamics
- Signal generation, RF detection, and coil sensitivity
- Spatial encoding and k‑space: trajectories and interpretation
- Image formation and Fourier-based reconstruction
- Non-Cartesian sampling and gridding/NUFFT reconstruction
- Resolution, sampling theory, and aliasing/artifact analysis
- Noise, SNR, and statistical considerations in MRI
- Pulse sequences and contrast mechanisms (T1, T2, etc.) from a signal view
- Practical artifacts and correction strategies
- Advanced topics and extensions (spectroscopic imaging, basic functional imaging concepts)
- Appendices: mathematical tools and implementation notes
Languages, Platforms & Tools
How It Compares
More explicitly signal-processing and reconstruction-focused than Haacke et al.'s MRI physical-principles texts; complements clinical/sequence-focused books by emphasizing k-space, FFT/NUFFT methods, and noise analysis.
