Title: Signal and Noise in fMRI
1Signal and Noise in fMRI John VanMeter,
Ph.D. Center for Functional and Molecular
Imaging Georgetown University Medical Center
2Outline
- Definition of SNR and CNR in context of anatomic
imaging - Definition of functional SNR
- Sources of noise in MRI
- Source of noise in fMRI
- Changes in MRI SNR and functional SNR with
increased magnetic field strength
3MRI Signal and Noise
- Signal is primarily dependent on number of
protons in the voxel - Noise can come from RF energy leaking into the
scanner room, random fluctuations in electrical
current, etc. - The body creates noise in the MR signal via
changes in current in the body producing small
changes in the magnet field breathing can change
homogeneity
4Measuring MRI Signal-to-Noise Ratio (SNR)
- Signal is the intensity (brightness) of one or
more pixels in the object of interest. - Noise is the intensity of one or more pixels in
the air (i.e. outside the object of interest). - SNR Signal (low SNR grainy, fuzzy images)
- Noise
- Fundamental measure of image quality
5MRI SNR Example 1
- S 700
- N 20
- SNR 700 / 20
- 35
6MRI SNR Example 2
- S 300
- N 50
- SNR 300 / 50
- 6
7MRI SNR - Side-by-Side
SNR 35 SNR 6
8SNR in Terms of fMRI
- MRI SNR is not the most important issue with
regard to functional MRI - Functional SNR is contingent on ability to detect
changes in BOLD signal between conditions (across
time) - Underlying MRI SNR still important in terms of
providing base for signal in functional SNR but
several other factors affect signal and noise in
fMRI data
9Affect of MRI SNR on Functional SNR
- Increase in MRI signal due to BOLD affect rides
on top of signal of in MRI scan - Imagine 2 increase in signal between these two
fMRI scans - In which image will the 2 change be more
detectable?
10Changes in BOLD Signal are Small
- Visual and sensorimotor areas percent change
might be as high 5 - For most other cortical areas expected percent
change is on the order of 1-3
11Measuring Percent Signal Change
12MRI Contrast-to-Noise Ratio (CNR)
- Measure of separation in terms of average
intensity between two tissues of interest - Defined as difference between the SNR of the two
tissues (A B) - CNR SignalA SignalB
- Noise
13MRI CNR Example 1
- SW 700, SG 200
- N 20
- CNRWG (700 200) / 20
- 25
14MRI CNR Example 2
- SW 200, SG 100
- N 50
- CNRWG (200 100) / 50
- 2
15MRI CNR Side-by-Side
CNRWG 35 CNRWG 6
16Functional CNR vs Functional SNR
- Generally CNR is unimportant in fMRI as there is
little contrast between tissues - Some researchers refer to difference between On
and Off as dynamic CNR or functional CNR - Probably more accurate to refer to ability to
detect changes related to activity as functional
SNR
17- Functional SNR is a dependent on differences in
signal across time - Ability to distinguish differences between
different conditions - effect size
18Differences Between Two Conditions
- Typically compare BOLD signal in the same area
under different conditions - Example fusiform face area responds to both
faces and tools but about 0.2 more to faces
19(No Transcript)
20Sources of Noise in fMRI Data
- System noise
- Thermal noise
- Signal drift
- Subject dependent noise
- Physiological noise
- Variability in BOLD response
- Variability across sessions within subject
- Variability across subjects
21Thermal Noise
- Intrinsic noise due to thermal motion of
electrons - In subject
- In RF equipment
- Increases with temperature - atoms move faster
more collisions greater loss of energy - Unfortunately increases with field strength
approximately linearly - Effects limited to temporal fluctuations and is
equally likely to add or subtract thus roughly
Gaussian (i.e. normally) distributed
22Signal Drift Across Time
- Magnetic field has slight drifts in strength over
time produces drift in signal - Gradually, over time the MRI signal in a voxel
drifts - This drift can vary from one voxel to the next
both in degree and direction!
23Signal Drift
24Affect of Signal Drift
25Effect of Nonlinear Drifts
26Physiological Noise
- Subject movement during scan
- Single largest source of noise in fMRI data
- Extremely problematic if motion is timed with
task - Makes studies with overt speech during the scan
quite difficult - Motion more problematic across time points
27Subject Motion
28Pulsatile Motion of Brain
- Influx of blood into brain induces movement
especially around base of brain - why there? - Short TRs can also pick-up noise due to
respiration (TRlt2500ms) and cardiac (TRlt500ms)
cycle
29- Map showing standard deviation of intensity over
time - Two sources of noise evident
- Why do edges of brain show large effect?
- Often referred to as ringing
30Power Spectrum
31Other Sources of Physiological Noise
- Change in CO2 - hyperventilation produces change
in O2 content of blood blood flow increases to
compensate - Drug affects - antihistamines, etc
- Smokers vs. Non-smokers
- Hypoactivation on attentional task after
abstaining for 1hr reversed following nicotine
patch (Lawrence et al, 2002)
32Genetic Based Differences
- ApoE risk factor for Alzheimers disease
- Study of non-symptomatic carriers
- Reduced activation in hippocampus on a memory
task for high risk carriers (AS Fleisher, et al,
Neurobiology of Aging, 2008)
33Noise from Neural Activity Not of Interest
- Eye movements - results in activation of the
frontal eye-fields - Noise of the scanner - activates auditory
cortices - Usually not a problem as noise common to both
conditions - Auditory experiments difficult though
- Other thoughts - whats for dinner, going over a
to-do list, wondering what the experiment is
testing (grad students), etc
34Behavioral and Cognitive Variability
- Passive tasks are prone to drift in subject
attention and/or arousal - Difficult to identify performance on tasks and
compare across subjects - Tasks with responses can lead to variations in
reaction/response time - Speed-accuracy trade-off
- Task strategies used can differ
- Task difficulty especially between groups of
subjects very problematic
35Inter-Subject Variability
36Inter-Session Variability
37Intra-Session Variability
3899-Scanning Sessions
- Same subject participated in 99 identical
scanning sessions - 33 each for motor task, visual task, and a
cognitive task - Everything kept exactly the same
- Considerable variability was observed
3933 Motor Sessions
McGonigle, et al., Neuroimage, 2000
4033 Cognitive Sessions
41Strategies for Dealing with Noise Improving
Signal
- MRI Center Steps
- Measure stability of signal over time
- Ensure stability of equipment
- Eliminate RF-noise
- Researcher
- Formalize instructions (use scripts)
- Train subjects ahead of time
- Instruct subjects to use same strategy
- Stress importance of staying still, focus, etc.
- Use better post-processing techniques
- Increase field strength
42Post-processing
Pre Post
Smith, et al., Human Brain Mapping, 2005
43Signal Averaging
- Averaging across multiple trials greatly helps to
improve SNR - Each graph shows 20 traces of 1 trial, average of
4 trials, average of 9 trials, etc
44Increasing MRI Signal with Stronger Magnets
- Increase magnetic field strength
- Plus
- more protons pulled into alignment thus greater
net magnetization resulting in increased MRI
signal - Minus
- shortens T2 resulting in larger spatial
distortions with gradient echo sequences - Requires larger RF pulses thus SAR goes up (why?)
45Susceptibility Distortion Increases with Field
Strength
46Rules of Thumb
- Quadratic increase in MRI signal with increase in
field strength - Thermal noise scales linearly with field strength
- Raw MRI SNR thus only scales linearly
- What about functional SNR?
47Functional SNR Linearly Increases with Field
Strength?
48Functional SNR vs Field Strength
- MRI signal goes up quadratically
- Thermal noise goes up linearly
- Physiological noise goes up quadratically
- Eventually functional SNR expected to plateau
49Upsides to Field Strength for Functional SNR
- Increase in number of voxels activated and
presumably detectability - T2 of blood much shorter thus signal drops off
in larger vessels - Linear increase in large vessels
- Quadratic increase in small vessels
- Thus, spatial specificity increases