MRI Physics Flashcards
T1 in different tissues
Spin-Lattice
Water (slowest)
Muscle
Fat (fastest due to long chains have complex thermally-induced flexions and rotations)
T2 in different tissues
Spin-Spin - density related:
Water (slowest)
Fat
Muscle (fastest)
How TE affects T2 contrast
T1 Weighted sequence
TR - weight long enough the spins have dephased and the transverse vector is net zero
If you apply an RF pulse AGAIN
- The net longitudinal vector that was T1 recovery will be flipped 90 degrees
So fat would have :
- relaxed more, have a higher net longitudinal vector
- when flipped 90 degrees, have a higher magnitude transverse vector
- Use a short TE to negate T2 so now you’re measuring T1 differences in the transverse plane!
T1 weighted scan
Negate T2 differences: SHORT TE
Maximise T1 contrast: SHORT TR
Proton density scan
Negates
T1 differences - LONG TR
T2 differences - VERY SHORT TE (before they begin dephasing)
(Water + Fat have a higher proton density)
T2 Weighted Scan
Negate T1 differences - LONG TR
Maximise T2 contrast - Short TE (but allowing some dephasement)
T1 and T2 recovery
T2 Decay Vs T1 signal gain
T2 happens A LOT FASTER
Long TR value range
1500-2000’s
Long TE value range
80-160ms
Short is 10’s
Lamour Frequency = B0 x Gyromagnetic ratio
Slice selection using RF
- Apply a gradient along B0 creating a gradient of precessional frequencies
- Apply an RF pulse of a bandwidth for your slice
Slice selection by changing the strength of Bo
Increase slice thickness by increasing bandwidth
Decrease slice thickness by increasing field strength
Higher max Bo - greater range, so a set bandwidth of RF would resonate with a narrower plane.
With a slice thickness, there’s a gradient of B0, which creates a slice phase.
So after RF pulse, apply a rephasing gradient.
Equal and opposite gradient to Z axis
Slice is now completely in phase
Slice selection sequence
- 90 degree flip
- Rephasing flip/dip
- 180 flip to account for T2* (free induction decay)
You need multiple TEs to infer what’s happening to net magnetisation over time.
And receiver coil has a bandwidth as well.
Visualising T2* correction
So you need to factor this in when using multiple TEs
Frequency encoding gradient applied along x-axis AT THE TIME OF READ OUT
So frequency differs depending on x-axis location
But if you apply frequency encoding gradient, they would lose phase and transverse magnetisation vector / signal.
So you apply an equal and opposite frequency encoding gradient prior to read out.
So more IN PHASE despite DIFFERENT FREQUENCIES during readout.
You can now sample at multiple TEs during frequency-encoded readout.
Every read-out is a numerical number for the whole image. Frequencies and their amplitudes can be teased out to give you x.
The Fourier transformation converts this time-based based data set into a frequency-based data set.
Also the more you sample, the more frequencies you can tease out.
You do a one dimension inverse Fourier transformation
Phase encoding gradient:
Apply a gradient between the 90 and 180 degree RF pulses
Only applied for a short period to cause a phase change, but then stopped so frequency is still with larmour frequency Bo
Phase encoding gradient:
Loss of transverse magnetisation because of phase differences
So each coloumn has the same frequency but in the y axis there is phase variability but with null in the middle in terms of phase change
Phase encoding gradient:
Increasing phase encoding gradients and also in opposite direction
Calculate amplitude without and then applying each.
Do a one dimensional inverse Fourier transform for each
Once you have enough phase encoding spaces you have k space
No. rows = No. phase encoding steps
Each line of k space is the net magnetisation vector over a period of time
Do a one dimensional inverse Fourier transform for each and transform it into frequency based or x axis location based data
K space data = TIME based data
EACH POINT along x axis in k space
represents net magnetisation vector of the ENTIRE SLICE at a given period of time.
You can convert that to x axis location based data
Do a one dimensional inverse Fourier transform for each and transform it into
Signal gets weaker at the start and end because of the phasing and dephasing
Signal weaker at the periphery because the signal is weaker when you apply stronger phase encoding steps
You can use k space and the one dimension fourier transform data to create your image
The specific net magnisation of the
Entire slice: From K Space
Entire data acquisition period from frequency based data
This is the 2D Fourier transformation where you combine these two data points that give you the axis data
Use simultaneous equations to work you the y contributions
K Space: Net magnetisation vector of the whole space over time
Spin echo sequence: Flipping 180 and measuring at TE when they come back into phase. So you’re measuring T2 rather than T2*
Central region of K space:
- Lose phase encoding and time acquisitions around it.
- Strong signal - able to see differences in signal easily
SO GIVES CONTRAST
- Lose spatial encoding
Any bright signal in T1 weight - likely come from FAT
Lower signal from H20
If you isolate the periphery of K space
- Multiple frequency and phase encoding steps
- GIVES SPATIAL RESOLUTION AND EDGES
Periphery of k space contains higher frequency info - the rate of phase change is much higher with the stronger gradients
The two halves of k space have conjugate symmetry
You use a mathematic formula to work out other half
However
- Not a perfect machine
- Magnetic field inhomogeneities
MRI Field of View
By narrowing field of view you have the same matrix size but pixel size is smaller
So better spatial resolution over a smaller field of view
Bandwidth
Range of frequencies within FOV in the frequency encoding plane
In the middle - its just B0 so they will have the Lamour frequency
Reducing FOV will also reduce the bandwidth
Reducing frequency encoding gradient increases the bandwidth for a particular thickness
So bandwidth is a function of both gradient and field of view
Rotational frame of reference. You’re looking at the differences in frequenecy
Processional frequency from null or centre will be different by half the bandwidth to either end
Aliasing
If you don’t sample high enough for a frequency
Nyquist limit = Sampling rate / 2
So you need to sample double the frequency you’re measuring
Sampling rate = bandwidth
Sampling interval is the inverse of this:
1/ sampling rate
aka DWELL TIME
Summary
Laboratory frame - when you look at the absolute value in terms of precessional frequency
Rotating frame - relative differences in precessional frequency relative to the centre
This is what it looks like when it is the RELATIVE difference in spins.
Frequency encoding gradient is then inverted so it comes back into phase as you record
If you pick a larger bandwidth by applying a stronger gradient
Stronger gradients will mean the dephasing and rephasing will happen much quicker.
So higher sampling frequency.
30Hz Bandwidth
Vs…
60Hz Bandwidth
Narrower bandwidth = Longer to acquire as sampling rate does not have to be as high
So narrower bandwidth = BETTER SNR
Weaker gradient = more time to acquire data
Higher bandwidth = POORER SNR
SNR = 1 / squareroot of BW
Benefits and drawbacks of narrow bandwith
Pros: Better SNR
Cons:
- More metal artefact
- More chemical shift artefact
- Can’t use short TE sequences
Aliasing - wrap around - when you are not sampling high enough
This can happen when you pick a smaller field of view from things outside of your range.
With phase encoding gradients, you could flip them 370 degrees - but it will only look like 10!
Ideally you should not be applying more than 180 degrees because adjacent spins could be completely out of phase.
This will cause aliasing in the phase encoding gradient.
The smaller you make the difference in phase encoding steps = greater you can make your FOV
Larger phase steps = smaller possible FOV as you’ll reach you phase shift limit quicker and risk aliasing.
Higher risk of aliasing at the edges
Soo….
You reduced your FOV with the same matrix size for better resolution
But now you get more aliasing from everything outside of your FOV
So reduce aliasing by…
- Remove tissue or arms outside FOV!
- Increase FOV but you lose resolution if you keep same matrix size
- Oversampling - taking more samples so frequencies or phase steps outside the FOV but don’t include it in the image.
Machines will often oversample 2-4x anyway in the frequency encoding direction because it doesn’t add any additional time to the sequence.
So reduce aliasing by…
However, over sampling in Y requires MORE phase encoding steps.
OR you can change the direct of your frequency and phase directions but you can’t as chemical shift only affects frequency encoding direction
So reduce aliasing by…
Parallel imaging
In parallel imaging you can have TWO RECEIVER COILS.
Different signal intensities depending on proximity to coils
Removing phase encoding aliasing though parallel imaging
So the aliased portions of the image will be the ones differing in intensity between the two coils, can be calculated and removed.
So no need for additional phase encoding steps!