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

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

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

7. Surround subtraction Control Tag Control Tag Control Tag Control +1/2 Perfusion Time Series T A = 1 to 4 seconds +1/2-1/2 1

8. Generalized Running Subtraction y tag +1 1.0 Upsample Low Pass Filter y perf y control

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

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

17. 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.