Hardware
Magnet Hardware
- Superconducting magnets generating Bo field
- Gradient coils for spatial encoding
- Body RF (Radio Frequency) transmit and receive coils
- RF receive-only and transmit coils (head and body)
- Shim coils embedded in gradients for magnetic field uniformity
- Key parameters:
- Maximum gradient: [80 mT/m, 200 T/m/sec]
- SC coils in liquid helium (no power needed after current injected)
- Bo = 1.5T corresponds to approximately 63 MHz
Spin and Precession
Spin & Precession
- Summary of nuclei behaving as spinning spheres with a charge generating a magnetic field.
- Spin relaxation processes:
- T1 (longitudinal) and T2 (transverse) relaxation in the presence of B0 and B1 fields.
- Precessional motion characterized by:
- Angular velocity = gyromagnetic ratio $
u = rac{g eta}{h}$.
- Precession frequency $
u = rac{g}{ au}$ where g = gravitational force.
Bloch Equation
Bloch Equation
- Describes the time-dependent behavior of the magnetization vector (M) in response to an applied magnetic field:
-
- Solutions depict process of precession and relaxation, leading to M regrowth, described by:
- M(t) = M_o (1 - e^{−t/T}) where T is a time constant.
- Generalization across different dimensions and integrations.
Vector Addition and Multiplication
Vector Operations
- Addition:
- Dot product and cross product formalism with examples:
- Dot product =
- Cross product forms vectors orthogonal to the plane defined by original vectors.
Effects of M, B, and T on Precession Frequency
Effects of Magnetic Fields on Frequency
- Analyzing effects of changing magnitude and orientation of M and B fields.
- Theoretical results show that for vertical alignment, precession frequency remains largely unchanged.
Simple Matrix Operations
Matrix Operations in Spin and Gradient Processing
- Matrix representations for turning and scaling vectors in 3D.
- Importance of maintaining orthogonality in transformation for no scaling. Operations revolve around physical rotation and translation.
Solutions to Simple Differential Equations
Solving Differential Equations
- Deriving solutions for Bloch-like equations governing time-dependent magnetization behaviors.
Summary of Bloch Equations and Their Matrix Representation
Matrix Form of Bloch Equations
- Convert physical intuition to computational models, focusing on rotations and scalings using matrices to simplify processes in imaging.
Excitation in the Rotating Frame
Understanding Excitation
- Analyzing excitation under the influence of B1 in an orthogonal system.
Signal Equation and Phase-sensitive Detection
RF Signal Processing
- Faraday's law of induction as the basis for signal collection from spatially-variable magnetization vectors.
Concepts of Echoes (Spin Echo, Gradient Echo, and Their Dynamics)
Echo Concepts in Imaging
- Spin echoes and gradient echoes described fundamentally as sequences leading to re-phased signals in MRI applications.
Image Contrast and Signal-to-Noise Ratio (SNR)
Image Contrast
- Image contrast explained via T1, T2, and proton density weighting + their interaction with SNR across MRI modalities.
Fourier Transform, Slicing, and Encoding Techniques
Quantitative Techniques in Imaging
- Fourier transforms analyzed as a central mathematical framework for image processing and cycling through spatial encodings in MRI setups.
Artifacts in Imaging and Techniques for Correction
Artifacts Recognition and Management
- Common imaging artifacts and methods for their mitigations such as shimming techniques, navigator processes, and utilization of redundancy in acquisition strategies.
Perfusion and Diffusion Imaging Techniques
Understanding Different Imaging Modalities
- Details on arterial spin labeling methods, basic perfusion principles, as well as diffusion tensor imaging approaches.
Spectroscopy in Imaging
Spectroscopic Techniques
- Discussion of chemical shifts and how resonant frequencies due to chemical environments are measured through RF signal processing algorithms in MRI.
Statistical Processing and Data Analysis Techniques
Statistical Analysis in Imaging
- General Linear Models (GLM) discussed within contexts of pre-processing steps to obtain robust statistical measures from functional imaging data such as fMRI and EEG applications.