SDSU Foundations of Neuroimaging (PSY0596, PSY0569, PSY0769)

FALL 2018

Course Flyer:

Marty Sereno — email: msereno - AT - sdsu
time: Mon/Wed/Fri 9:00-9:50 PM (grad session: Fri 8:00-8:50)
location: SSW 2667 (Learning Glass Studio, Student Services West)

Learning Glass lecture recordings
Sereno Lecture Notes (89-page PDF [14MB] — single-page links below, last update: 2 Sep 2018)
Huettel, S., A.W. Song, G. McCarthy (2014) Functional Magnetic Resonance Imaging, 3rd ed.
Additional background reading

Homework #1 (due 10/15/2018, paper printout, incl. code and graphs)
Homework #2 (due 11/26/2018, paper printout, incl. code and graphs, brain image is here)
Final Paper: 10 page literature review on narrow methodological topic (start search in
    Magnetic Resonance in Medicine, Neuroimage, Human Brain Mapping)

Learning Objectives:
Students will be able to do the following:
   (1) explain precession/excitation/recording/contrast of magnetic resonance signals and echoes using the Bloch equation
   (2) compute Fourier transform, use to explain RF stim, gradients, signals generate k-space data, how recon. works
   (3) diagram main classes anatomical/functional pulse sequences
   (4) describe diffusion, perfusion, and spectroscopic imaging
   (5) describe origin/localization of EEG/MEG signals, cortical surface-based methods, and how to combine w/fMRI
   N.B.: consult with me if a disability hinders your performance so we can use University resources to maximize learning

Description and Prerequisites:
This course covers the physical and mathematical foundations of structural, functional, diffusion, and perfusion MRI, fMRI time series analysis, cortical-surface based reconstruction and data analysis, and the neural basis, recording, and localization of EEG and MEG signals. Although this course does not assume a background in linear algebra, vector calculus, differential equations, electromagnetism, the Fourier transform, and convolution, students will be expected to develop a solid grasp of a number of key equations underlying the different neuroimaging methods and solve simple Matlab problems. We will go slower than a typical engineering class and there will be plenty of time for questions. Here are two neuroimaging courses at UCSD for reference. Tom Liu's Bioengineering 280A — Principles of Biomedical Imaging (Fall 2015), provides a stronger mathematical foundation in the fundamentals of the Fourier transform and linear systems theory and covers ultrasound and CT in addition to MRI. David Dubowitz and Rick Buxton teach a two-quarter SOMI 276 — School of Medicine fMRI Course (2016/2017) in the Radiology Department.

Lecture Topics — Fall 2018 (pdf)

Week of Aug 27 (Mon/Wed/Fri)Introduction

  • Introduction to Neuroimaging — MRI, fMRI, EEG, MEG
  • MRI hardware
  • Spin and Precession

    Week of Sep 03 (Wed/Fri)Bloch Equation

  • [no class Mon, Sep 03]
  • Bloch Equation
  • Dot/Cross/Complex Products
  • Precession Solution
  • Effects of Change (M, B, Angle) On Precession Freq
  • T1, T2 Solutions
  • Simple Matrix Operations
  • Initial-Value Solutions to Differential Equation
  • Bloch Equation, Solution — Matrix Version

    Week of Sep 10 (Mon/Wed/Fri)Signal Equation

  • RF Excitation
  • Signal Equation
  • Phase-Sensitive Detection

    Week of Sep 17 (Mon/Wed/Fri)Echoes

  • Free Induction Decay
  • Spin Echo
  • Spin Echo Equations
  • Stimulated Echo, Spin Echo Trains
  • Extended Phase Graphs
  • HyperEcho Train
  • Gradient Echo, Gradient Echo Trains

    Week of Sep 24 (Mon/Wed/Fri)Using the Bloch Equation

  • Saturation-Recovery Signal
  • Imperfect 90 deg Approach to State State
  • Inversion Recovery Signal
  • Spin Echo Signal
  • Gradient Echo Signal
  • Generalized MDEFT Signal
  • SNR/tSNR/CNR (e.g., Gray/White)
  • Signal-to-Noise

    Week of Oct 01 (Mon/Wed/Fri)Fourier transform

  • Complex Algebra
  • Fourier Transform
  • Negative Exponents, Orthogonality
  • Inverse Fourier as Correlation w/Cos,Sin
  • Spatial Frequency Space (k-Space)
  • One k-Space Point
  • One k-Space Point — 3 representations
  • Center of k-Space, Complex Image

    Week of Oct 08 (Mon/Wed/Fri)Gradients, Slice Selection

  • Gradient Fields
  • Gradient Combination
  • Slice Selection
  • RF Pulse Details
  • Frequency-Encoding Intro —It's A Misnomer
  • Frequency-Encoding—Avoid This Intuition
  • Frequency-Encoding—Correct Intuition (=Phase-Encoding)

    Week of Oct 15 (Mon/Wed/Fri)MRI Image Formation

  • 1st Take-Home Due
  • Imaging Equation (1D)
  • Phase Encoding
  • 3D Imaging (2nd Phase-Encode)
  • Spin Phase in Image Space
  • Gradients Move Signal in k-Space

    Week of Oct 22 (Mon/Wed/Fri)Image Reconstruction

  • Image Space and k-space (311K MPEG, R.-S. Huang)
  • Image Reconstruction
  • Aliasing and FOV
  • Under/Over Sample
  • Replicas, FTs
  • General Linear Inverse for MRI Reconstruction

    Week of Oct 29 (Mon/Wed/Fri)Practical Pulse Sequences

  • Fast Spin Echo
  • 3D Multi-Slab Fast Spin Echo
  • 3D Single-Slab Fast Spin Echo (SPACE)
  • Fast Gradient Echo
  • Quantitative T1 (2-Flip-Angle Method)
  • Gradient Echo EPI
  • Spin Echo EPI
  • Spin Echo Size Selectivity
  • Coil Fall-Off, Undersampling, GRAPPA, SENSE
  • Simultaneous Multi-Slice (blipped CAIPI)
  • SMS cont.: slice-GRAPPA, split-slice-GRAPPA
  • 3D Echo Volume Imaging
  • Single-Shot Spiral
  • 3D Spiral Inversion-recovery FSE

    Week of Nov 05 (Mon/Wed/Fri)Image Artifacts

  • Fourier Shift Artifacts
  • EPI vs. Spiral Artifacts
  • Image-space View Localized B0 Defect
  • Effect Local B0 Defect on Reconstruction
  • Shimming and B0-Mapping
  • Gradient Non-linearities
  • Motion-detection with Navigator Echoes
  • RF Field Inhomogeneities

    Week of Nov 12 (Wed/Fri)Diffusion and Perfusion Imaging

  • [no class Mon, Nov 12]
  • Diffusion-Weighted Imaging and Tract Tracing
  • Practical Diffusion-Weighted Sequences
  • Perfusion Imaging (Arterial Spin Labeling)
  • Off-Resonance RF Excitation
  • Combining Spectroscopy with Imaging

    Week of Nov 19 (Mon-only)Block Design, Phase-Encoded Design

  • Phase-Encoded Stimulus and Mapping
  • Convolution
  • General Linear Model and Solution
  • General Linear Model — Geometric Picture
  • Cluster Correction — 3D and Surface-Based
  • [no class Wed/Fri, Nov 21/23]

    Week of Nov 26 (Mon/Wed/Fri)Cortical Surface Based methods

  • Normalize, Strip Skull
  • Spring Force in Detail
  • Non-isotropic Filter, Region-Growing
  • Tessellation: 3D -> 2D
  • Energy Functional for Smooth/Inflate/Final
  • Cortical Unfolding and Flattening
  • Automatic Topology Repair
  • Sulcus-Based Alignment
  • Cortical Thickness Measurement
  • Mapping Cortical Visual Areas
  • 2nd Take-Home Due

    Week of Dec 03 (Mon/Wed/Fri)Source of EEG/MEG

  • Source of EEG/MEG (rough draft)
  • Grad, Div, Curl
  • 1D/2D/3D Current Source Density
  • Intracortical Circuits (rough draft)
  • Intracortical Basis of EEG (rough draft)
  • Maxwell Equations — Low Frequency Limit
  • Why We Can Ignore Magnetic Induction
  • Monopole, Dipole (rough draft)

    Week of Dec 10 (Mon/Wed)Neuroimaging EEG/MEG

  • Forward Solution — Analytic, Boundary Element (incomplete)
  • Matrix Formulation of the Linear Forward Solution
  • Derivation of Ill-Posed Minimum Norm Linear Inverse
  • Two Equivalent Formulations — One Is Easier to Compute
  • Using Spatiotemporal Covariance of Sensors (MUSIC)
  • Noise-Sensitivity Normalization of Mininum Norm (rough draft)
  • Noise-Sensitivity Normalization — Intuition
  • Point-Spread/Crosstalk, Conclusions
  • Surface-Normal Constraint and its Problems
  • Sensor Covariance: Calculate Weights
  • Sensor Covariance: Insert Weights into Inverse
  • Sensor Covariance: How it Helps
  • fMRI-weighted Inverse Solutions
  • Non-linear Fitting of Moveable Dipoles
  • [no class Fri, Dec 14]

    Week of Dec 17 Final Paper/Exam

    Final paper/exam due Dec 19

    last modified: Aug 21, 2018
    Scanned/video'd class notes (pdf, links above) © 2018 Martin I. Sereno
    Supported by NSF 0224321, NIH MH081990, Royal Society Wolfson