**Course Flyer:**- FlyerPsy569,769.pdf
**Professor:**- Marty Sereno — email: msereno - AT - sdsu
- lecture recording times: Mon/Wed/Fri 9:00-9:50 AM (grad session: Fri 8:00-8:50 AM)
- lecture recording location: SSW 2667 (Learning Glass Studio, Student Services West)
- All lectures live-streamed on Zoom
**Resources:**- Learning Glass lecture recordings
- Sereno Lecture Notes (123-page PDF [19MB] — single-page links below, last update: 11 Dec 2020)
- Huettel, S., A.W. Song, G. McCarthy (2014)
*Functional Magnetic Resonance Imaging, 3rd ed.* - Additional background reading
**Assignments:**- Homework #0 (warm-up, no need to turn in)
- Homework #1 (due 10/12/2020, paper printout, incl. code and graphs)
- Homework #2 (due 11/23/2020, paper printout, incl. code and graphs, brain image is here)
- Final Paper: (ugrad=5pg, grad=10pg) 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 recently
taught 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 2020**(pdf)-

*Week of Aug 24 (Mon/Wed/Fri)*—**Introduction**

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

*Week of Aug 31 (Mon/Wed/Fri)*—**Bloch Equation**

- Dot/Cross/Complex Products
- Bloch Equation
- Precession Solution
- Effects of Change (M, B, Angle) On Precession Freq
- Initial-Value Solution to T2 Differential Equation
- Verify T1 Re-Growth Solution
- Simple Matrix Operations
- Bloch Equation, Solution — Matrix Version
- Bloch Equation: Excitation In Rotating Frame
- Bloch Equation: Summary

*Week of Sep 07 (Wed/Fri)*—**Signal Equation**

*[no class Mon, Sep 07]*- RF Excitation
- Signal Equation
- Phase-Sensitive Detection

*Week of Sep 14 (Mon/Wed/Fri)*—**Echoes**

- Free Induction Decay
- Spin Echo
- Spin Echo Equations
- Stimulated Echo, Spin Echo Trains
- Extended Phase Graphs
- 3-Pulse Echo Amplitudes
- HyperEcho Train
- Gradient Echo, Gradient Echo Trains

*Week of Sep 21 (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
- Magnetization Transfer Contrast
- SNR/tSNR/CNR (e.g., Gray/White)
- Signal-to-Noise

*Week of Sep 28 (Mon/Wed/Fri)*—**Fourier transform**

- Complex Algebra
- Fourier Transform
- Negative Exponents, Orthogonality
- Inverse Fourier as Correlation w/Cos,Sin
- Spatial Frequency Space (k-Space) — 3 Equivalent Representations
- One k-Space Point
- Simple Image (1 Freq), Shifted Simple Image
- Center of k-Space, Complex Image
- Kx plus Ky Rotates, Special Case Sum of Reals

*Week of Oct 05 (Mon/Wed/Fri)*—**Gradients, Slice Selection**

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

*Week of Oct 12 (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 19 (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
- Point-Spread Function
- General Linear Inverse for MRI Reconstruction

*Week of Oct 26 (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 (Intro, Other Methods)
- Quantitative T1 (2-Flip-Angle Method)
- Gradient Echo EPI
- Spin Echo Size Selectivity
- Spin Echo EPI
- 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 02 (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 09 (Mon/Fri)*—**Diffusion, Perfusion, and Spectroscopy**

- Diffusion-Weighted Imaging and Tract Tracing
- Practical Diffusion-Weighted Sequences
- Perfusion Imaging (Arterial Spin Labeling)
- pCASL
- Off-Resonance RF Excitation
*[no class Wed, Nov 11]*- Combining Spectroscopy with Imaging
- PRESS, MEGA-PRESS

*Week of Nov 16 (Mon/Wed/Fri)*—**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
- Why Use Surfaces?
- 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

*Week of Nov 23 (Mon-only)*—**Cortical Surface Based methods**

**2nd Take-Home Due**- Cortical Thickness Measurement
- Mapping Cortical Visual Areas
*[no class Wed/Fri, Nov 25/27]*

*Week of Nov 30 (Mon/Wed/Fri)*—**Source of EEG/MEG**

- Neural Source of EEG/MEG
- Intracortical Circuits and EEG/MEG
- Grad, Div, Curl
- 1D/2D/3D Current Source Density
- 1D/2D Current Source Density Experiments
- Intracortical Basis of EEG
- Maxwell Equations — Low Frequency Limit
- Why We Can Ignore Magnetic Induction
- Monopole, Dipole Current Sources

*Week of Dec 07 (Mon/Wed)*—**Neuroimaging EEG/MEG**

- Forward Solution — Analytic, Boundary Element (incomplete)
- Matrix Formulation of the Linear Forward Solution
- Why Localize?
- Derivation of Ill-Posed Minimum Norm Linear Inverse
- Minimum Norm: Why It Appropriately 'Devalues' Deeper Sources
- Two Equivalent Formulations — One Is Easier to Compute
- Surface-Normal Constraint and its Problems
- fMRI-weighted Inverse Solutions
- Noise-Sensitivity Normalization of Mininum Norm
- Noise-Sensitivity Normalization — Intuition
- Point-Spread/Crosstalk, Conclusions
- Using Spatiotemporal Covariance of Sensors (MUSIC)
- Sensor Covariance: Calculate Weights
- Sensor Covariance: Insert Weights into Inverse
- Sensor Covariance: How it Helps
- Non-linear Fitting of Moveable Dipoles
*[no class Fri, Dec 11]*

*Week of Dec 16*—**Final Paper/Exam**

**Final paper/exam due Dec 17**

website last modified:*03 Aug 2020*

Scanned/video'd class notes (pdf, links above) © 2020 Martin I. Sereno

Supported by NSF 0224321, NIH MH081990, Royal Society Wolfson