1. MR Image Formation FMRI Graduate Course (NBIO 381, PSY 362)
Dr. Scott Huettel, Course Director
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

2. Introductory Exercise
Write down the major steps involved in the generation of MR signal
Just write an outline, not an essay
Note what scanner component contributes to each step
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

3. Generation of MR Signal
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

4. T1 T2
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

5. Relaxation Times and Rates
Net magnetization changes in an exponential fashion
Constant rate (R) for a given tissue type in a given magnetic field
R = 1/T, leading to equations like e–Rt
T1 (recovery): Relaxation of M back to alignment with B0
Usually ms in the brain (lengthens with bigger B0)
T2 (decay): Loss of transverse magnetization over a microscopic region ( 5-10 micron size)
Usually ms in the brain (shortens with bigger B0)
T2*: Overall decay of the observable RF signal over a macroscopic region (millimeter size)
Usually about half of T2 in the brain (i.e., faster relaxation)
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

6. T1 Recovery fmri-fig-04-02-0.jpg
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

7. T2 Decay fmri-fig-04-03-0.jpg
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

8. T1 and T2 parameters
By selecting appropriate pulse sequence parameters (Week 4’s lecture), images can be made sensitive to tissue differences in T1, T2, or a combination.
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

9. I
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FMRI – Week 3 – Image Formation Scott Huettel, Duke University

10. FMRI – Week 3 – Image Formation Scott Huettel, Duke University

11. Gradients change the Strength, not Direction of the Magnetic Field
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

12. Parts of 2D Image Formation
Slice selection
Linear z-gradient
Tailored excitation pulse
Spatial encoding within the slice
Frequency encoding
Phase encoding
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

13. Slice Selection
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

14. FMRI – Week 3 – Image Formation Scott Huettel, Duke University

15. Linear z-gradient fmri-fig-04-09-0.jpg
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

16. Why can’t we just use an excitation pulse of a single frequency?
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

17. Selecting a Band of Frequencies
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FMRI – Week 3 – Image Formation Scott Huettel, Duke University

18. Choosing a Slice fmri-fig-04-11-1.jpg
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

19. Changing Slice Thickness
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FMRI – Week 3 – Image Formation Scott Huettel, Duke University

20. Changing Slice Location
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(Note: manipulating gradient is simpler than changing slice bandwidth.)
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

21. Interleaved Slice Acquisition… 12 13 … 3 2 1
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

22. FMRI – Week 3 – Image Formation Scott Huettel, Duke University

23. Spatial Encoding
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

24. How not to do spatial encoding…
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

25. … a better approach
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

26. Temporal Signal = Combination of Frequencies
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FMRI – Week 3 – Image Formation Scott Huettel, Duke University

27. Effects of Gradients on Phase
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FMRI – Week 3 – Image Formation Scott Huettel, Duke University

28. Core Concept: k-space coordinate = Integral of Gradient Waveform
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FMRI – Week 3 – Image Formation Scott Huettel, Duke University

29. Inverse Fourier Transform
Image space x y
Final Image
k-space kx ky
Acquired Data
Fourier Transform
Inverse Fourier Transform
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

30. Spatial Image = Combination of Spatial Frequencies
fmri-fig jpg
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

31. k Space fmri-fig-04-08-0.jpg
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

32. Image space and k space fmri-fig-04-06-0.jpg
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

33. Parts of k space fmri-fig-04-07-0.jpg
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

34. What would happen if we turned on both gradients simultaneously?
So, we know that two gradients are necessary for encoding information in a two-dimensional image?
What would happen if we turned on both gradients simultaneously?
fmri-fig jpg
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

35. Frequency Encoding During readout (or data acquisition, DAQ)
Uses gradient perpendicular to slice-selection gradient
Signal is sampled & digitized about once every few microseconds
Readout window ranges from 5–100 milliseconds
Why not longer than this?
Fourier transform converts signal S(t) into frequency components S(f )
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

36. Phase Encoding
Apply a gradient perpendicular to both slice and frequency gradients
The phase of Mxy (its angle in the xy-plane) signal depends on that gradient
Fourier transform measures phase  of each S(f) component of S(t)
By collecting data with many different amounts of phase encoding strength, we can assign each S(f) to spatial locations in 3D
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

37. fmri-fig jpg
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

38. Echo-Planar Imaging (EPI)
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

39. Sampling in k-space Dk K FOV FOV = 1/Dk, Dx = 1/K
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

40. Problems in Image Formation
FMRI – Week 3 – Image Formation Scott Huettel, Duke University

41. Magnetic Field Inhomogeneity
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FMRI – Week 3 – Image Formation Scott Huettel, Duke University

42. Gradient Problems fmri-fig-04-16-0.jpg
FMRI – Week 3 – Image Formation Scott Huettel, Duke University