A signal processing model for arterial spin labeling perfusion fmri
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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. Wait. Tag by Magnetic Inversion. Acquire image. Wait. Control. Acquire image. Arterial Spin Labeling (ASL).

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A Signal Processing Model for Arterial Spin Labeling Perfusion fMRI

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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


Wait

Tag by Magnetic Inversion

Acquire image

Wait

Control

Acquire image

Arterial Spin Labeling (ASL)

1:

2:

Control - Tag µ CBF


Goal: Accurately measure dynamic CBF response to neural activity

From C. Iadecola 2004


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.


Pairwise subtraction example

Control

Tag

+1

-1

+1


Surround subtraction

TA = 1 to 4 seconds

Control

Control

Control

Control

Tag

Tag

Tag

+1/2

-1

+1/2

-1/2

1

-1/2

Perfusion Time Series


Generalized Running Subtraction

ycontrol

+1

yperf

Low Pass

Filter

Upsample

ytag

1.0


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?


  • =1 presaturation applied

  • = 0

    No presat

Tag : n even

Control: n odd

 is the inversion efficiency ideal inversion:  =1


Tag : n even

Control: n odd

Pairwise Subtraction

Surround Subtraction

Sinc Subtraction


Demodulate

Modulate


Perfusion Estimate

Demodulated and filtered perfusion component

Modulated and filtered BOLD component

Modulated and filtered noise component


Perfusion Component

BOLD Component


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.


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