A Signal Processing Model for
Arterial Spin Labeling
Perfusion fMRI
Thomas Liu and Eric Wong
Center for Functional Magnetic Resonance Imaging
University of California, San Diego
Arterial Spin Labeling (ASL)Arterial Spin Labeling (ASL)
Tag by Magnetic Inversion
Wait
Acquire image
Control
Wait
Acquire image
1:
2:
Control - Tag CBF
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From C. Iadecola 2004
Goal: Accurately measure dynamic CBF response to neural activity
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Example:Perfusion and BOLD in primary and supplementary motor cortex. Measured with PICORE QII with dual-echo spiral readout.
Obata et al. 2004
ASL Data Processing
• CBF = Control - Tag• An estimate of the CBF time series is formed
from a filtered subtraction of Control and Tag images.
• Use of subtraction makes CBF signal more insensitive to low-frequency drifts and 1/f noise.
Surround subtraction
Control Tag ControlTag
ControlTagControl
+1/2 -1
Perfusion Time Series
TA = 1 to 4 seconds
+1/2 -1/2 1 -1/2
Questions
• What is the difference between the various processing schemes?
• How do they effect the estimate of CBF? • What are the noise properties of the estimate?
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1−α 1+(−1)n( )exp −TI /T1B( )
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q[ n]
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M[ n]€
b[ n]
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e[ n]
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y[ n]
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Perfusion
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1− β exp −TI p /T1( )
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×
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×
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×€
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Static Tissue€
BOLD Weighting
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Measurements
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Noise
is the inversion efficiency ideal inversion: =1
Tag : n evenControl: n odd
=1 presaturation applied = 0No presat
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(−1)n+1
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g[ n]
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ˆ q [ n]
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y[ n]
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×€
g[ n]
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ˆ b [n]
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Measurements
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Perfusion Estimate
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BOLD Estimate
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g[n] = 1 1[ ]
g[n] = 1 2 1[ ] /2
g[n] = sinc[n /2]
Tag : n evenControl: n odd
Pairwise SubtractionSurround SubtractionSinc Subtraction
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1−α 1+(−1)n( )exp −TI /T1B( )
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(−1)n+1
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q[ n]
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M[ n]€
b[ n]
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e[ n]€
g[ n]
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ˆ q [ n]
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y[ n]
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Perfusion
€
1− β exp −TI p /T1( )
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×
€
+
€
×
€
×€
+
€
×€
g[ n]
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ˆ b [n]
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Static Tissue€
BOLD Weighting
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Measurements
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Perfusion Estimate
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BOLD Estimate
DemodulateModulate
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ˆ q [n ] = qq[n ]+ qb[n ]+ qe[n ]Perfusion Estimate
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qq[n ] = αb[n ]q[n ]e−TI /T1B( ) ∗g[n ]
Demodulated and filtered perfusion component
Modulated and filtered BOLD component
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qb[n ] = b[n ] sMM[n ]+ sqq[n ]( )[ ] −1( )n +1∗g[n ]
Modulated and filtered noise component
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qe[n ] = (−1)n +1e[n ][ ] ∗g[n ]
Summary
• For block designs with narrow spectrum, use surround subtraction or sinc subtraction
• For randomized designs with broad spectrum, use pair-wise subtraction.
• To minimize noise autocorrelation use pair-wise or surround subtraction.
• General framework can be used to design other optimal filters.