Signal and Noise in fMRI

John VanMeter, Ph.D.

Center for Functional and Molecular ImagingGeorgetown University Medical Center

Outline

• 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

MRI 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

Measuring 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

MRI SNR – Example 1

S

N

S = 700N = 20SNR = 700 / 20

= 35

MRI SNR – Example 2

S

N

S = 300N = 50SNR = 300 / 50

= 6

MRI SNR - Side-by-Side

SNR = 35 SNR = 6

SNR 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

Affect 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?

Changes 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%

Measuring Percent Signal Change

MRI 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

MRI CNR – Example 1

SW = 700, SG = 200

N = 20CNRWG = (700 – 200) / 20

= 25 SW

N

SG

MRI CNR – Example 2

SW = 200, SG = 100N = 50CNRWG = (200 – 100) / 50

= 2

SW

N

SG

MRI CNR Side-by-Side

SW

N

SG

SW

N

SG

CNRWG = 35 CNRWG = 6

Functional 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

• Functional SNR is a dependent on differences in signal across time

• Ability to distinguish differences between different conditions - effect size

Differences 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

Sources 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

Thermal 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

Signal 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!

Signal Drift

Affect of Signal Drift

Effect of Nonlinear Drifts

410

420

430

440

450

460

470

480

490

1 22 43 64 85 106

Physiological 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

Subject Motion

Pulsatile Motion of Brain

• Influx of blood into brain induces movement especially around base of brain - why there?

• Short TR’s can also pick-up noise due to respiration (TR<2500ms) and cardiac (TR<500ms) cycle

• 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”

Power Spectrum

Other 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)

Genetic Based Differences

• ApoE risk factor for Alzheimer’s 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)

Noise 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 - what’s for dinner, going over a to-do list, wondering what the experiment is testing (grad students), etc

Behavioral 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

Inter-Subject Variability

Inter-Session Variability

Intra-Session Variability

99-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

33 Motor Sessions

McGonigle, et al., Neuroimage, 2000

33 Cognitive Sessions

Strategies 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

Post-processing

Pre

Post

Smith, et al., Human Brain Mapping, 2005

Signal 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

Increasing 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?)

Susceptibility Distortion Increases with Field Strength

1.5T

4.0T

Rules 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?

Functional SNR Linearly Increases with Field Strength?

Functional 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

Upsides 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