Non-BOLD Methods: Arterial Spin Labeling · Associate Research Professor FMRI Laboratory,...

Non-BOLD Methods: Arterial Spin Labeling

Instructor: Luis Hernandez-Garcia, Ph.D.Associate Research Professor

FMRI Laboratory, Biomedical Engineering

Lecture Goals• Other non-BOLD techniques (T2 weighted, Mn contrast

agents, SSFP, Dynamic Diffusion, ASL)

• Understand Basic Principles in Spin labeling : spin inversion, flow vs. perfusion,

• ASL variations

• Quantification of perfusion from ASL data

• Functional ASL analysis: Detection and Quantification

• Some Examples

• The niche for functional ASL

The BOLD Effect(Figure courtesy of Doug Noll)

Drawbacks of BOLD imaging

• Not a quantitative physiological parameter

• Non-linear function of neuronal activity

• Signal drifts (“1/f noise”)

• Susceptibility artifacts

• Small contrast / noise ratio

Alternatives to BOLD

• Truong, T.-K., Song, A. W., Finding neuroelectric activity under magnetic-field oscillations (NAMO) with magnetic resonance imaging in vivo, Proc. Natl. Acad. Sci. USA 103(33), 12598-12601, 2006.

• Pautler RG. Koretsky AP. Tracing odor-induced activation in the olfactory bulbs of mice using manganese-enhanced magnetic resonance imaging. [Journal Article] Neuroimage. 16(2):441-8, 2002 Jun.

• Song AW. Harshbarger T. Li T. Kim KH. Ugurbil K. Mori S. Kim DS. Functional activation using apparent diffusion coefficient-dependent contrast allows better spatial localization to the neuronal activity: evidence using diffusion tensor imaging and fiber tracking. [Journal Article] Neuroimage. 20(2):955-61, 2003 Oct.

Alternatives to BOLD• D. Le Bihan, T. Aso, S. Urayama, T. Hanakawa, H. Fukuyama: Swelling of

slow water diffusion pool precedes hemodynamic response during activation of human visual cortex . Proc ISMRM 2006, p. 898

Alternatives to BOLD

• Duong TQ. Yacoub E. Adriany G. Hu X. Ugurbil K. Vaughan JT. Merkle H. Kim SG. High-resolution, spin-echo BOLD, and CBF fMRI at 4 and 7 T, Magnetic Resonance in Medicine. 48(4):589-93, 2002

• Wang J. Aguirre GK. Kimberg DY. Roc AC. Li L. Detre JA. Arterial spin labeling perfusion fMRI with very low task frequency. Magnetic Resonance in Medicine. 49(5):796-802, 2003 May.



• Understand Basic Principles in Spin labeling : spin inversion, flow vs. perfusion

• ASL variations



• Some Examples


Enter Arterial Spin Labeling:

• Traditional Perfusion measurements are based on the injection of a tracer.

• Why not leverage the magnetic properties of water to use it as a tracer? (Williams DS, Detre JA, Leigh JS, Koretsky AP: Magnetic Resonance Imaging of Perfusion using Spin Inversion of Arterial Water. Proc. Natl. Acad. Sci., 89:212-216, 1992.)

• Apply what we know from PET, …etc.

Obvious advantages(fine print: there are drawbacks too)

• Completely non-toxic• No injections!• Easy to deliver a good input function to the

tissue of interest.• Quick decay = Quickly repeatable.

Arterial Spin Labeling

Inflow of labeled blood reduces signal intensity at the target tissue

Label is “injected” upstream of the target tissue: RF pulses produce inversion of the spins

Quick Review: The MR signal

The MR signal is proportional to the net magnetization vector

S =

Flow contrast

Inflowing spins are inverted!

S =

Label

Some Options •Pulsed vs. Continuous• Delays• Saturation Pulses•2D, 3D …

A generic ASL pulse sequence

Acq. 1 Acq. 2

Difference

Control




• ASL variations



• Some Examples


Inversion&ControlAt the same location

Simplest case: Continuous ASL

Example: Baseline Perfusion Image

The Inversion Label

magnitude phase

Hernandez-Garcia, L., D. Lewis, et al. (2007). "Magnetization transfer effects on the efficiency of flow-driven adiabatic fast passage inversion of arterial blood." NMR Biomed 20: 733-742.

Image Plane

Inversion Plane

Flow Driven Adiabatic Pulses

• Effective field (Beff) in Rotational Frame of Reference

• Frequency Sweep• Effective Field:

Beff = Δω/γ + B1

• “Spin Locking”• Flow:

z(t) = v * t + z0

Δω/γ = z(t)*Gz

Δω/γ

B1

Beff = Δω/γ + B1

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Inversion and Decay(different velocities)

Inversionplane

velocity

Side effect at the imaging slice: Magnetization Transfer (MT)

Kf Mf = Kb Mb

• On-Resonance spins affected by off-resonance pulses• Can be used as a form of contrast

MT solution:two-Coil AST scheme

MT Solutions:Pseudo-continuous inversion

• Tag is achieved by a train of RF pulses: steady state inversion• Control case: steady state is disrupted • Both cases produce the same amount of MT

Garcia: ISMRM, Miami, FLA, p. 37, 2005. Wu: Magn Reson Med 58, 1020-1027, 2007.

Pulsed ASL schemesEPISTAR, QUIPSS

Velocity Selective ASL

Luis Hernandez-Garcia 2014, UM FMRI Laboratory

25




• ASL variations



• Some Examples


TheASLsignalArterialsignal

Input Function (Label)

Dispersion: (t e-kt)Relaxation: (e-t/T1a)

Venoussignal

Parenchymalsignal

Capillary BedRetention (f e-(f/l)t)Relaxation (e-t/T1)

Quantification of perfusion (the model)

• Mtissue accumulated tissue magnetization tag (subtraction of control and tagged images)

• Mart incoming arterial magnetization tag-must account for inversion efficiency and decay (2a e-(D/T1art) Mart(0))

• l: blood brain partition coefficient• f: perfusion rate• T1 : longitudinal relaxation rate of the

tissue

• r(t) : tissue retention function ( e-ft )

• m(t): T1 decay of tag in the tissue (e-t/T1 )

€

dMtissue (t)dt

= f ⋅ Mart (t) −fλ⋅ Mtissue (t) −

Mtissue (t)T1

Williams DS, Proc. Natl. Acad. Sci.,89:212-216, 1992.)

€

Mtissue (t) = f ⋅ Mart (t) *[r(t)m(t)]Another way to look at it

Buxton, MRM, 40: 383-396 (1998)

• Continuous ASL: steady state solution yields a “simple” equation

• Pulsed ASL : not as simple. You really need fit the equation for the different stages of the passage... There are approximations, such as assuming that you sample during the uptake portion of the passage:

• references– Williams Proc. Natl. Acad. Sci., 89:212-216,

1992– Kim, MRM, 34: 293-301, 1995.– Buxton, MRM, 40: 383-396 (1998)– Yang , MRM, 39, 1998, p.825

Quantification of perfusion

€

f =Mcontrol −Mtag( )λ

2M0

⋅ΔR1

1− e−( t−τ ) /ΔR1

f = λT1app

⋅Mb

ss1 − Mbss2( )

Mbss2 + 2 $α −1( )Mb

ss1




• ASL variations



• Some Examples


Example: Functional ASL

Raw (unsubtracted) time course

Base image

BOLD effects

ASL at rest

ASL during activation

A Linear Model for ASL

Analyzing ASL based FMRI signals

• It is very common to difference the data and/or apply filters.

• To difference or not to difference?– Warning: AR noise is still present– Differencing Amplifies (high frequency noise)

transitions.– Short Answer: It is preferable to model the sources than

to modify the data. Greater estimation efficiency and power.

Mumford, J.A., Hernandez-Garcia, L., Lee, G.R., Nichols, T.E., 2006. Estimation efficiency and statistical power in arterial spin labeling fMRI. Neuroimage 33, 103-114.

D4 =

1 −2 1−1 2 −1

1 −2 1

−1 2 −1

"

#

$$$$$

%

&

'''''

y4[n] = (−1)n ⋅ (2y[n]− y[n +1]− y[n + 2])⇔

surround

Analyzing ASL based FMRI signals

• Differencing the timecourse: perfusion is proportional to the difference between control and tag

• Differencing schemes help you clean up the data

D2 =

1 −1 01 −1

0 1 −1

"

#

$$$$

%

&

''''

y2[m] = yc[m]− yt[m]⇔pairwise

D3 =

1 −1 0−1 1

1 −1

0 1 −1

"

#

$$$$$

%

&

'''''

y3[n] = (−1)n ⋅ (y[n]− y[n +1])⇔

running

Effect of Differencing ASL Data(frequency content)

The GLM Equations in ASL

• Simple GLM:Y = Xb + e

• Differencing the data (and the design matrix)

DY = DXb + De• Prewhitening : Generalized Least Squares

WY = WXb + We

GLS Analysis of un-differenced data vs. OLS Analysis of

differenced data

Quantifying dynamic perfusion changes

Traditional Approach: 1. Calculate perfusion from each subtraction pair using tracer

kinetics Wang J, et al. (2002). Magn. Reson. Med., vol. 48, no. 2, pp. 242-254.

2. Calculate difference in perfusion between conditions.3. Calculate means and variances

GLM Approach:1. Translate GLM parameters (betas) into perfusion.2. Translate variance estimates (sigmas) into perfusion effects

varianceHernandez-Garcia L, et al. (2010), Magn. Res. Imaging, 28 (7), Pages 919-927.

f =λ ⋅ ΔM ⋅ R1app

M 0

1− e−TR⋅R1%&'

()*⋅2 ⋅α ⋅ e−δ ⋅R1a e(δ −w)⋅R1app − e(δ −τ −w)⋅R1app( )

Quantifying dynamic perfusion changes

f̂effect =λ ⋅ β̂effect ⋅ R1app

β̂01− e−TR⋅R1%

&'

(

)* ⋅2 ⋅α ⋅ e

−δ ⋅R1a e(δ −w)⋅R1app − e(δ −τ −w)⋅R1app( )

feffect is the perfusion change due to the effect of interest,• a is the inversion efficiency, • beffect is the parameter estimate of the regressor representing the effect of interest, • l is the blood brain partition coefficient, • R1, R1a, R1app are longitudinal relaxation rates of arterial blood, tissue, and tissue in the

presence of perfusion. • d is the arterial transit time, • TR, w, and t are repetition time, post labeling delay, and labeling duration.

Quantifying the variance of those dynamic perfusion changes

σ̂ 2feffect

= σ̂β̂0

2 ∂f̂effect∂β̂0

$

%&

'

()

2

+ σ̂β̂effect

2 ∂f̂effect∂β̂effect

$

%&

'

() + 2 ⋅COV β̂0 , β̂effect( )

∂f̂effect∂β̂effect

=λ ⋅ R1app

β̂01− e−TR⋅R1&

'(

)

*+ ⋅2 ⋅α ⋅ e


∂f̂effect∂β̂0

= −λ ⋅ β̂effect ⋅ R1app

β̂02

1− e−TR⋅R1&

'(

)

*+ ⋅2 ⋅α ⋅ e


Maps of Perfusion Effects and their Variance

Estimates and their Variance

The wholeASL Processing

Stream

Software you can use

BASIL:http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BASIL

f-ASL http://www.eecs.umich.edu/~hernan/Public/Programs

ASLtbxhttps://cfn.upenn.edu/~zewang/ASLtbx.php




• ASL variations



• Some Examples


Real Time F-ASL (pCASL)Motor Visual Paradigm

Current Subtracted imageCurrent Raw image

Statistical Map Time courses

Example: Working memory training study

M. Buschkuehl, et al “Neural effects of short-term training on working memory.” Cogn. Affect. Behav. Neurosci., pp. 147–160, 2014.

• How does cognitive training affect performance?• What happens to your brain when you practice?

• Resting• Activation

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Working memory training study:the experiment

Paradigm: blocks of rest, 4-back and 1-backImaging with pseudo-continuous ASLWorking Memory Load: 4-back – 1-back

1. Test Performance (while collecting ASL images)2. Train for one week, 20 mins. per day3. Test Performance (while collecting ASL images)

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Working memory training study: Effect of training: bigger activations

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Working memory training study

Effect of training: Resting Perfusion Change

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Note: there was a correlation between performance and resting CBF in frontal regions!




• ASL variations



• Some Examples


Why ASL? Quantification.

• Quantification: – BOLD : a complicated mix of parameters– Perfusion: Single physiological parameter

• Real Time neuro-feedback : How do you evaluate whether subject’s brain is doing the “right thing”?1. Brain activity matches a specific spatial pattern2. Brain activity matches a specific temporal pattern3. Amount of activity in a given region

Why ASL? The BOLD Signal Drift

• Autoregressive structure• BOLD signals are inherently “drifty” because:

– Physiological effects• Respiration• Heart beat

– Scanner effects– Temperature

ASL Perfusion vs. BOLDVery Low Task Frequency

Treatment effects START in this

range!

Wang, J., G. Aguirre, et al. (2003). "Arterial spin labeling perfusion fMRI with very low task frequency." Magn Reson Med 49(5): 796-802.

Event Related BOLD

Blocked Design BOLD: Slow Paradigm

Blocked Design FASL: Slow Paradigm

ASL Perfusion fMRI vs. BOLD (sensitivity)

Single Subject Group Analysis (Random Effects)

Aguirre, G., J. Detre, et al. (2002). "Experimental design and the relative sensitivity of BOLD and perfusion fMRI." Neuroimage 15(3): 488-500.

ASL’s nicheApplications• Drug Studies• Attention• Cognitive Training• Slow paradigms• Mental State studies• Population Studies• … anything SLOW or

requiring Quantification!

• Current ASL techniques have lower SNR and Speed than BOLD

• BOLD breaks down in slow paradigms because of autoregressive drifts

Some References• D. C. Alsop, et al. , Magn. Reson. Med., vol. 73, no. 1, pp. 102–116, Apr. 2014.

• Aguirre, G., Detre, J., Zarahn, E., & Alsop, D. (2002) Neuroimage, 15(3), 488-500.

• Mumford, J. A., Hernandez-Garcia, L., Lee, G. R., & Nichols, T. E. (2006). Neuroimage, 33(1),

103-114.

• Liu, T., & Wong, E. (2005). Neuroimage, 24(1), 207-215.

• Parkes, L. M., Rashid, W., Chard, D. T., & Tofts, P. S. (2004). Magnetic Resonance in Medicine,

51(4), 736-743.

• Petersen, E. T., Mouridsen, K., & Golay, X. (2010). Neuroimage, 49(1), 104-113.

• Murphy, K., Harris, A. D., Diukova, A., Evans, C. J., Lythgoe, D. J., Zelaya, F., et al. (2011).

Magnetic Resonance Imaging, 29(10), 1382-1389.

Non-BOLD Methods: Arterial Spin Labeling · Associate Research Professor FMRI Laboratory,...

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