fMRI Data Quality Assuranceand Preprocessing

http://www.fmri4newbies.com/

Last Update: January 18, 2012Last Course: Psychology 9223, W2010, University of Western Ontario

Jody CulhamBrain and Mind Institute

Department of PsychologyUniversity of Western Ontario

The Black Box

• The danger of automated processing and fancy images is that you can get blobs without every really looking at the real data

• The more steps done at without quality assurance, the greater the chance of wonky results

RawData

Big Black Box of automated

software

Pretty pictures

Culham’s First Commandment:Know Thy Data

• Look at raw functional images– Where are the artifacts and distortions?

– How well do the functionals and anatomicals correspond?

• Look at the movies– Is there any evidence of head motion?

– Is there any evidence of scanner artifacts (e.g., spikes)?

• Look at the time courses– Is there anything unexpected (e.g., abrupt signal changes at the start of

the run)?

– What do the time courses look like in the unactivatable areas (ventricles, white matter, outside head)?

• Look at individual subjects• Double check effects of various transformations

– Make sure left and right didn’t get reversed

– Make sure functionals line up well with anatomicals following all transformations

• Think as you go. Investigate suspicious patterns

Sample ArtifactsGhosts

Spikes

Metallic Objects (e.g., hair tie)Hardware Malfunctions

Contrast and Contrast:Noise

T1 T2 HighContrast:

Noise

LowContrast:

Noise

Why SNR MattersNote: This SNR level is not based on the formula given

Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

Sources of Noise

Physical noise• “Blame the magnet, the physicist, or the laws of physics”

Physiological noise• “Blame the subject”

How Can You Tell the Difference?

• Test a phantom -- No physiological noise!

A Map of Noise

• voxels with high variability shown in white

Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

Effect of Field Strength on Signal and Noise

• Although raw SNR goes up with field strength, so does thermal and physiological noise

• Thus there are diminishing returns for increases in field strength

Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

Effect of Field Strength on Signal

Effect of Field Strength on Vascular Signals

Effect of Field Strength on Susceptibility

1.5 T

4.0 T

Coils

Head coil• homogenous signal• moderate SNR

Surface coil• highest signal at hotspot• high SNR at hotspot

Photo source: Joe Gati

Phased Array Coils• SNR of surface coils with the coverage of head coils• OR… faster parallel imaging• modern scanners come standard with 8- or 12-channel head coils and

capability for up to 32 channels

Photo Source: Technology Review

90-channel prototypeMass. General Hospital

Wiggins & Wald

12-channel coil 32-channel coil

32-channel head coilSiemens

Phased Array Coils

Source: Huettel, Song & McCarthy, 2004,Functional Magnetic Resonance Imaging

Voxel Size• Bigger is better… to a point• Increasing voxel size signals summate, noise

cancels out• “Partial voluming”: If tissue is of different types, then

increasing voxel size waters down differences– e.g., gray and white matter in an anatomical– e.g., activated and unactivated tissue in a functional

Sampling Time

• More samples More confidence effects are real

Head Motion: Main ArtifactsHead motion Problems

time1 time2

1) Rim artifacts• hard to tell activation from artifacts• artifacts can work against activation

2) Region of interest moves•lose effects because you’re sampling outside ROI

Looking at the negative tail can help you identify artifacts

Playing a movie of slices over time helps you detect head motion

Head Motion: Good, Bad,…

Slide from Duke course

… and really, really ugly!

Slide from Duke course

Motion Correction Algorithms

• Most algorithms assume a rigid body (i.e., that brain doesn’t deform with movement)

• Align each volume of the brain to a target volume using six parameters: three translations and three rotations

• Target volume: the functional volume that is closest in time to the anatomical image

x translation

z tr

ansl

atio

n

y tr

ansl

atio

n

pitch roll yaw

BVQX Motion Correction Options

Align each volume to the volume closest in time to the anatomical– Why?

Analysis/fMRI 2D data preprocessing menu

Mass Motion Artifacts• motion of any mass in the magnetic field, including the head,

is a problem

headcoil

arm brace

gazegrasparatus

brace

Grasping and reaching data from

block designscirca 1998

Mass Motion Artifacts

Time Course:

% S

ign

al C

ha

ng

e

-4

0

7

Time (seconds)15030 60 90 1200

Left Right Left Right Left

.60-.60

1.0

-1.0

r value

-0.4

0

0.6

Time (seconds)15030 60 90 1200

Mo

tion

De

tect

ed

(m

m o

r d

eg

ree

s)

Motion Correction Parameters

Even in the absence of head motion,

mass motion creates huge problems

30 s 30 s

Where is the signal correlated with the

mass position?

phantom(fluid-filled

sphere)

Culham, chapter in Cabeza & Kingstone, Handbook of Functional Neuroimaging of Cognition (2nd ed.), 2006

00

900

Mass Motion Distort Magnetic Field

Barry et al., in press, Magnetic Resonance Imaging

Motion Correction Algorithms

• Existing algorithms correct two of our three problems:1. Head motion leads to spurious activation

2. Regions of interest move over time

3. Motion of head (or any other large mass) leads to changes to field map

• Sometimes algorithms can introduce artifacts that weren’t there in the first place (Friere & Mangin, 2001, NeuroImage)

√

√

X

The Fridge Rule

• When it doubt, throw it out!

Head Restraint

Head Vise(more comfortable than it

sounds!)

Bite Bar

Often a whack of foam padding works as well as anything

Vacuum Pack

Thermoplastic mask

Prospective Motion Correction

• Siemens Prospective Acquisition CorrEction (PACE)

• shifts slices on-the-fly so that slice planes follow motion

• Siemens claims it improves data quality• Caution: unlike retrospective motion

correction algorithms, you can never get “raw” data

Source: Siemens

Prevention is the Best Remedy• Tell your subjects how to be good subjects

– “Don’t move” is too vague

• Make sure the subject is comfy going in– avoid “princess and the pea” phenomenon

• Emphasize importance of not moving at all during beeping– do not change posture– if possible, do not swallow– do not change posture– do not change mouth position– do not tense up at start of scan

• Discourage any movements that would displace the head between scans

• Do not use compressible head support

• For a summary of info to give first-time subjects, seehttp://defiant.ssc.uwo.ca/Jody_web/Subject_Info/firsttime_subjects.htm

Mock “0 T” Scanners

Disdaqs• Discarded data acquisitions: trashed volumes at the beginning of a run

before the magnet has reached a steady state• Sometimes it can take awhile for the subject to reach a steady state too --

Startle response!

BV Preprocessing Options

Slice Scan Time Correction

The first slice is collected almost a full TR (e.g., 3 s) before the last slice

Source: Brain Voyager documentation

Non-Interleaved

Slice Scan Time CorrectionInterleaved

Source: Brain Voyager documentation

Slice Scan Time Correction

Slice Scan Time Correction

Source: Brain Voyager documentation

• interpolates the data from each slice such that is is as if each slice had been acquired at the same time

BV Preprocessing Options

Spatial Smoothing

Gaussian kernel• smooth each voxel by a Gaussian or normal function, such that the nearest neighboring voxels have the strongest weighting

Maximum

Half-Maximum

Full Width at Half-Maximum (FWHM)

FWHM Values• some smoothing: 4 mm• typically smoothing: 6-8 mm• heavy duty smoothing: 10 mm

3D Gaussian smoothing kernel

Effects of Spatial Smoothing on Activity

No smoothing 4 mm FWHM 7 mm FWHM 10 mm FWHM

Should you spatially smooth?

• Advantages– Increases Signal to Noise Ratio (SNR)

• Matched Filter Theorem: Maximum increase in SNR by filter with same shape/size as signal

– Reduces number of comparisons• Allows application of Gaussian Field Theory

– May improve comparisons across subjects• Signal may be spread widely across cortex, due to

intersubject variability

• Disadvantages– Reduces spatial resolution – Challenging to smooth accurately if size/shape of

signal is not known

Slide from Duke course

“Why would you spend $4 million to buy an MRI scanner and then blur the data till it looked like PET?”

-- Ravi Menon

BV Preprocessing Options

Linear Drift

Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

Components of Time Course Data

Source: Smith chapter in Functional MRI: An Introduction to Methods

BV Preprocessing Options

Before LTR:

After LTR:

BV Preprocessing Options

High pass filter•pass the high frequencies, block the low frequencies•a linear trend is really just a very very low frequency so LTR may not be strictly necessary if HP filtering is performed (though it doesn’t hurt)

Before High-pass

linear drift

~1/2 cycle/time course

~2 cycles/time course

After High-pass

BV Preprocessing Options

• Gaussian filtering– each time point gets averaged with adjacent time points– has the effect of being a low pass filter

• passes the low frequencies, blocks the high frequencies

– for reasons we will discuss later, I recommend AGAINST doing this

After Gaussian (Low Pass) filteringBefore Gaussian (Low Pass) filtering

Find the “Sweet Spots”

Respiration• every 4-10 sec (0.3 Hz)• moving chest distorts susceptibility

Cardiac Cycle• every ~1 sec (0.9 Hz)• pulsing motion, blood changes

Solutions• gating• avoiding paradigms at those frequencies

You want your paradigm frequency to be in a “sweet spot” away from

the noise

Macro- vs. micro- vasculature

Macrovasculature:vessels > 25 m radius(cortical and pial veins) linear and oriented cause both magnitude and phase changes

Microvasculature:vessels < 25 m radius(venuoles and capillaries) randomly oriented cause only magnitude changes

Capillary beds within the cortex

Source: Duvernoy, Delon & Vannson, 1981, Brain Research Bulletin

“Vein, vein, go away”

Source: Menon, 2002, Magn Reson Med

• large vessels tend to be consistently oriented (with respect to the cortex) whereas capillaries are randomly oriented • Ravi’s algorithm uses this fact to estimate and remove the contribution of large vessels in the signal• this was verified by examining the time course of a voxel in a vein and a voxel in gray matter, with and without vessel suppression

voxel in vein

voxel in gray matter

raw data vessel suppression vessel selection

Order of Preprocessing Steps is Important

• Thought question: Why should you run motion correction before temporal preprocessing (e.g., linear trend removal)?

• If you execute all the steps together, software like Brain Voyager will execute the steps in the appropriate order

• Be careful if you decide to manually run the steps sequentially. Some steps should be done before others.

Take-Home Messages

• Look at your data• Work with your physicist to minimize physical noise• Design your experiments to minimize physiological

noise• Motion is the worst problem: When in doubt, throw it

out• Preprocessing is not always a “one size fits all”

exercise

EXTRA SLIDES

What affects SNR?Physical factors

PHYSICAL FACTORS SOLUTION & TRADEOFFThermal Noise (body & system) Inherent – can’t change

Magnet Strength

e.g. 1.5T 4T gives 2-4X increase in SNR

Use higher field magnet– additional cost and maintenance– physiological noise may increase

Coil

e.g., head surface coil gives ~2+X increase in SNR

Use surface coil– Lose other brain areas– Lose homogeneity

Voxel size

e.g., doubling slice thickness increases SNR by root-2

Use larger voxel size– Lose resolution

Sampling time Longer scan sessions– additional time, money and subject discomfort

Source: Doug Noll’s online tutorial

Head Motion: Main Artifacts

1. Head motion can lead to spurious activations or can hinder the ability to find real activations. • Severity of problem depends on correlation between motion and

paradigm

2. Head motion increases residuals, making statistical effects weaker.

3. Regions move over time– ROI analysis: ROI may shift– Voxelwise analyses: averages activated and nonactivated voxels

4. Motion of the head (or any other large mass) leads to changes to field map

5. Spin history effects• Voxel may move between excitation pulse and readout

A B C

Motion Intensity Changes

Slide modified from Duke course

Motion Spurious Activation at Edges

time1 time2

lateralmotion in x direction

motion in z direction

(e.g., padding sinks)

time 1 > time 2

time 1 < time 2

brainposition

statmap

Spurious Activation at Edges

• spurious activation is a problem for head motion during a run but not for motion between runs

Motion Increased Residuals

fMRI Signal

× 1

× 2

=

ResidualsDesign Matrix

++

“what we CAN

explain”

“what we CANNOT explain”

= +Betasx

“how much of it we CAN explain”

“our data” = +x

Statistical significance is basically a ratio of explained to unexplained variance

Regions Shift Over Time

• A time course from a selected region will sample a different part of the brain over time if the head shifts

• For example, if we define a ROI in run 1 but the head moves between runs 1 and 2, our defined ROI is now sampling less of the area we wanted and more of adjacent space

• This is a problem for motion between runs as well as within runs

time1 time2

Problems with Motion Correction

• lose information from top and bottom of image– possible solution: prospective motion correction

• calculate motion prior to volume collection and change slice plan accordingly

we’re missing data here

we have extra data here

Time 1 Time 2

Why Motion Correction Can Be Suboptimal

1. Parts of brain (top or bottom slices) may move out of scanned volume (with z-direction motion or rotations)

2. Motion correction requires spatial interpolation, leads to blurring– fast algorithms (trilinear interpolation) aren’t as good as slow ones

(sinc interpolation) – Motion correction

Why Motion Correction Algorithms Can Fail

• Activation can be misinterpreted as motion– particularly problematic for least squares algorithms (Friere

& Mangin, 2001)

• Field distortions associated with moving mass (including mass of the head) can be misinterpreted as motion

Simulated activation

Spurious activation created by motion correction in SPM (least squares)

Mutual information algorithm in SPM has fewer problems

Friere & Mangin, 2001

Head Motion: Solution to Susceptibility

Solution:• one trial every 10 or 20 sec• fMRI signal is delayed ~5 sec

distinguish true activity from artifacts

IMPORTANT: Subject must remain in constant configuration between trials

0 5 10

Time (Sec)

fMRISignal

action

activityartifact

Different motions; different effects

Drift within run Movement between runs

Uncorrelated abrupt movement within run

Correlated abrupt movement within a run

Motion correction

Spurious activations okay, corrected by LTR

okay minor problem huge problem can reduce problems

Increased residuals okay, corrected by LTR

okay problem problem can reduce problems; may be improved by including motion parameters as predictors of no interest

Regions move problem minor-major problem depending on size of movement

problem problem can reduce problems; if algorithm is fooled by physics artifacts, problem can be made worse by MC

Physics artifacts not such a problem because effects are gradual

okay problem huge problem can’t fix problem; may be misled by artifacts

Motion Correction Output

gradual motions are usually well-corrected

abrupt motions are more of a problem (esp if related to paradigm

SPM output

raw data

linear trend removal

motion corrected in SPM

Caveat: Motion correction can cause artifacts where there were none

Effect of Temporal Filtering

before

after

Source: Brain Voyager course slides

Trial-to-trial variabilitySingle trials

Average of all trials from 2 runs

Time Course Filtering

Spatial Distortions

Iso

cent

reIs

oce

ntre

+ 1

2 cm

Lengthwise Cross-sectionBefore Correction

Lengthwise Cross-SectionAfter Correction

Core Cross-sectionBefore Correction

Top

Bottom

A B C

D E F

Homogeneity Correction

Data Preprocessing Optionsreconstruction from raw k-space data

• frequency space real space

artifact screening• ensure the data is free from scanner and subject artifacts

vessel suppression• reduce the effects of large vessels (which are further away from activation than capillaries)

slice scan time correction• correct for sampling of different slices at different times

motion correction• correct for sampling of different slices at different times

spatial filtering • smooth the spatial data

temporal filtering • remove low frequency drifts (e.g., linear trends)• remove high frequency noise (not recommended because it increases temporal autocorrelation and artificially inflates statistics)

spatial normalization • put data in standard space (Talairach or MNI Space)

A Brief Primer on Fourier Analysis• Sine waves can be characterized by frequency and

amplitude

peak: high pointtrough: low point

frequency: number of cycles within a certain time or space (e.g., cycles per sec = Hz, cycles per cm)

amplitude: height of wave

phase: starting point

• (b) has same frequency as (a) but lower amplitude• (c) has lower frequency than (a) and (b)• (d) has same frequency and amplitude as (c) but different phase

peak

trough

amplitude

Source: DeValois & DeValois, Spatial Vision, 1990

Fourier Decomposition• Any wave form can be decomposed into a series of

sine waves

Frequency spectrum

Source: DeValois & DeValois, Spatial Vision, 1990

Temporal and Spatial Analysis

Temporal waveforms• e.g., sound waves• e.g., fMRI time courses

Spatial waveforms• can be one dimensional

(e.g., sine wave gratings in vision) or two dimensional (e.g., a 2D image)

• e.g., image analysis• e.g., an fMRI slice (k-

space)

Source: DeValois & DeValois, Spatial Vision, 1990

Fourier Synthesis

• centre = low frequencies

• periphery = high frequencies

• You can see how the image quality grows as we add more frequency information

Source: DeValois & DeValois, Spatial Vision, 1990

K-Space

Source: Traveler’s Guide to K-space (C.A. Mistretta)

What affects SNR?Physiological factors

Source: Doug Noll’s online tutorial

PHYSIOLOGICAL FACTORS SOLUTION & TRADEOFFHead (and body) motion Use experienced or well-warned subjects

– limits useable subjects

Use head-restraint system– possible subject discomfort

Post-processing correction– often incompletely effective– 2nd order effects– can introduce other artifacts

Single trials to avoid body motion

Cardiac and respiratory noise Monitor and compensate– hassle

Low frequency noise Use smart design

Perform post-processing filtering

BOLD noise (neural and vascular fluctuations) Use many trials to average out variability

Behavioral variations Use well-controlled paradigm

Use many trials to average out variability

Slice Scan Time Correction

• Slice scan time correction adjusts the timing of a slice corrected at the end of the volume so that it is as if it had been collected simultaneously with the first slice

original time courseshifted time course

Source: Brain Voyager documentation

Calculating Signal:Noise Ratio

Pick a region of interest (ROI) outside the brain free from artifacts (no ghosts, susceptibility artifacts). Find mean () and standard deviation (SD).

Pick an ROI inside the brain in the area you care about. Find and SD.

SNR = brain/ outside = 200/4 = 50

[Alternatively SNR = brain/ SDoutside = 200/2.1 = 95(should be 1/1.91 of above because /SD ~ 1.91)]

Head coil should have SNR > 50:1

Surface coil should have SNR > 100:1

When citing SNR, state which denominator you used.

Source: Joe Gati, personal communication

e.g., =4, SD=2.1

e.g., = 200

WARNING!: computation of SNR is complicated for phased array coils

WARNING!: some software might recalibrate intensities so it’s best to do computations on raw data