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A Signal Processing Model for Arterial Spin Labeling Perfusion fMRI

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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  1. 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

  2. Wait Tag by Magnetic Inversion Acquire image Wait Control Acquire image Arterial Spin Labeling (ASL) 1: 2: Control - Tag µ CBF

  3. Goal: Accurately measure dynamic CBF response to neural activity From C. Iadecola 2004

  4. Example: Perfusion and BOLD in primary and supplementary motor cortex. Measured with PICORE QII with dual-echo spiral readout. Obata et al. 2004

  5. 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.

  6. Pairwise subtraction example Control Tag +1 -1 +1

  7. 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

  8. Generalized Running Subtraction ycontrol +1 yperf Low Pass Filter Upsample ytag 1.0

  9. 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?

  10. =1 presaturation applied • = 0 No presat Tag : n even Control: n odd  is the inversion efficiency ideal inversion:  =1

  11. Tag : n even Control: n odd Pairwise Subtraction Surround Subtraction Sinc Subtraction

  12. Demodulate Modulate

  13. Perfusion Estimate Demodulated and filtered perfusion component Modulated and filtered BOLD component Modulated and filtered noise component

  14. Perfusion Component BOLD Component

  15. 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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