Non-BOLD Methods: Arterial Spin Labeling

Transcription

1 Non-BOLD Methods: Arterial Spin Labeling Instructor: Luis Hernandez-Garcia, Ph.D. Associate Research Professor FMRI Laboratory, Biomedical Engineering

2 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

3 The BOLD Effect (Figure courtesy of Doug Noll)

4 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

5 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), , 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.

6 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

7 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): , 2003 May.

8 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

9 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: , 1992.) Apply what we know from PET, etc.

10 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.

11 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

12 Quick Review: The MR signal The MR signal is proportional to the net magnetization vector S =

13 Flow contrast Inflowing spins are inverted! S =

14 A generic ASL pulse sequence Difference Label Acq. 1 Control Acq. 2 Some Options Pulsed vs. Continuous Delays Saturation Pulses 2D, 3D

15 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

16 Simplest case: Continuous ASL Inversion & Control At the same location

17 Example: Baseline Perfusion Image

18 The Inversion Label 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: Image Plane Inversion Plane magnitude phase

19 Flow Driven Adiabatic Pulses Δω/γ B eff = Δω/γ + B 1 Effective field (B eff ) in Rotational Frame of Reference Frequency Sweep Effective Field: B eff = Δω/γ + B 1 Spin Locking Flow: B 1 z(t) = v * t + z 0 Δω/γ = z(t)*g z 19

20 Inversion and Decay (different velocities) velocity Inversion plane

21 Side effect at the imaging slice: Magnetization Transfer (MT) K f M f = K b M b On-Resonance spins affected by off-resonance pulses Can be used as a form of contrast

22 MT solution: two-coil AST scheme

23 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, Wu: Magn Reson Med 58, , 2007.

24 Pulsed ASL schemes EPISTAR, QUIPSS

25 Velocity Selective ASL Luis Hernandez-Garcia 2014, UM FMRI Laboratory 25

26 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

27 Dispersion: (t e -kt ) Relaxation: (e -t/t1a ) The ASL signal Arterial signal Parenchy mal signal Venous signal Input Function (Label) Capillary Bed Retention (f e -(f/l)t ) Relaxation (e -t/t1 )

28 Quantification of perfusion dm tissue (t) dt (the model) = f M art (t) f λ M tissue (t) M tissue (t) T 1 M tissue accumulated tissue magnetization tag (subtraction of control and tagged images) M art incoming arterial magnetization tagmust account for inversion efficiency and decay (2a e -(D/T1art) M art (0)) l: blood brain partition coefficient f: perfusion rate T1 : longitudinal relaxation rate of the tissue Williams DS, Proc. Natl. Acad. Sci., 89: , 1992.) Another way to look at it M tissue (t) = f M art (t) *[r(t)m(t)] r(t) : tissue retention function ( e -ft ) m(t): T1 decay of tag in the tissue (e -t/t1 ) Buxton, MRM, 40: (1998)

29 Quantification of perfusion 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: , 1992 Kim, MRM, 34: , Buxton, MRM, 40: (1998) Yang, MRM, 39, 1998, p.825 f = λ T 1app M ss1 ss2 ( b M b ) M ss2 b + 2 α$ 1 ( ) M b ss1 ( f = M M control tag)λ ΔR 1 2M 0 1 e (t τ )/ ΔR 1

30 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

31 Example: Functional ASL Raw (unsubtracted) time course

32 A Linear Model for ASL Base image BOLD effects ASL at rest ASL during activation

33 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., Estimation efficiency and statistical power in arterial spin labeling fmri. Neuroimage 33,

34 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 pairwise y 2 [m] = y c [m] y t [m] " $ D 2 = $ $ # $ % ' ' ' & ' running y 3 [n] = ( 1) n (y[n] y[n + 1]) surround " $ $ D 3 = $ $ $ # y 4 [n] = ( 1) n (2y[n] y[n + 1] y[n + 2]) " $ $ D 4 = $ $ $ # % ' ' ' ' ' & % ' ' ' ' ' &

35 Effect of Differencing ASL Data (frequency content)

36 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

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

38 Quantifying dynamic perfusion Traditional Approach: changes 1. Calculate perfusion from each subtraction pair using tracer kinetics Wang J, et al. (2002). Magn. Reson. Med., vol. 48, no. 2, pp 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 variance Hernandez-Garcia L, et al. (2010), Magn. Res. Imaging, 28 (7), Pages

39 Quantifying dynamic perfusion changes ˆf effect f = λ ΔM ˆβ effect R 1app R 1app %% Mˆβ ( & ' 1 e TR R 1 ) * α e δ R 1a (δ w) R 1app (δ τ w) R 0 ( 1app ' 1 e TR R 1 * & ) 2 α e δ R 1a e (δ w) R 1app e (δ τ w) R 1app (( ) f effect is the perfusion change due to the effect of interest, a is the inversion efficiency, b effect is the parameter estimate of the regressor representing the effect of interest, l is the blood brain partition coefficient, R 1, R 1a, R 1app 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.

40 Quantifying the variance of those dynamic perfusion changes ˆσ 2 f effect $ ˆf 2 = effect ˆσ ˆβ0 & % ˆβ 0 ' ) ( 2 $ ˆf 2 + effect ˆσ ˆβeffect & % ˆβ effect ' ) + 2 COV ˆβ 0, ˆβ effect ( ( ) ˆf effect ˆβ effect = & ˆβ 0 ( 1 e TR R 1 ' λ R 1app ) + * 2 α e δ R 1a e (δ w) R 1app e (δ τ w) R 1app ( ) ˆf effect ˆβ 0 = ˆβ 0 2 & ( 1 e TR R 1 ' λ ˆβ effect R 1app ) + * 2 α e δ R 1a e (δ w) R 1app e (δ τ w) R 1app ( )

41 Maps of Perfusion Effects and their Variance

42 Estimates and their Variance

43 The whole ASL Processing Stream

44 Software you can use BASIL: f-asl ASLtbx

45 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

46 Real Time F-ASL (pcasl) Motor Visual Paradigm Current Raw image Current Subtracted image Statistical Map Time courses

47 Example: Working memory training study M. Buschkuehl, et al Neural effects of short-term training on working memory. Cogn. Affect. Behav. Neurosci., pp , How does cognitive training affect performance? What happens to your brain when you practice? Resting Activation 47

48 Working memory training study: the experiment Paradigm: blocks of rest, 4-back and 1-back Imaging with pseudo-continuous ASL Working Memory Load: 4-back 1-back 1. Test Performance (while collecting ASL images) 2. Train for one week, 20 mins. per day 3. Test Performance (while collecting ASL images) 48

49 Working memory training study: Effect of training: bigger activations 49

50 Working memory training study Effect of training: Resting Perfusion Change Note: there was a correlation between performance and resting CBF in frontal regions! 50

51 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

52 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 pattern 2. Brain activity matches a specific temporal pattern 3. Amount of activity in a given region

53 Why ASL? The BOLD Signal Drift Autoregressive structure BOLD signals are inherently drifty because: Physiological effects Respiration Heart beat Scanner effects Temperature

54 ASL Perfusion vs. BOLD Very Low Task Frequency Wang, J., G. Aguirre, et al. (2003). "Arterial spin labeling perfusion fmri with very low task frequency." Magn Reson Med 49(5): Treatment effects START in this range!

55 Event Related BOLD

56 Blocked Design BOLD: Slow Paradigm

57 Blocked Design FASL: Slow Paradigm

58 Blocked Design FASL: Slow Paradigm

59 ASL Perfusion fmri vs. BOLD (sensitivity) Aguirre, G., J. Detre, et al. (2002). "Experimental design and the relative sensitivity of BOLD and perfusion fmri." Neuroimage 15(3): Single Subject Group Analysis (Random Effects)

60 ASL s niche Current ASL techniques have lower SNR and Speed than BOLD BOLD breaks down in slow paradigms because of autoregressive drifts Applications Drug Studies Attention Cognitive Training Slow paradigms Mental State studies Population Studies anything SLOW or requiring Quantification!

61 Some References D. C. Alsop, et al., Magn. Reson. Med., vol. 73, no. 1, pp , Apr Aguirre, G., Detre, J., Zarahn, E., & Alsop, D. (2002) Neuroimage, 15(3), Mumford, J. A., Hernandez-Garcia, L., Lee, G. R., & Nichols, T. E. (2006). Neuroimage, 33(1), Liu, T., & Wong, E. (2005). Neuroimage, 24(1), Parkes, L. M., Rashid, W., Chard, D. T., & Tofts, P. S. (2004). Magnetic Resonance in Medicine, 51(4), Petersen, E. T., Mouridsen, K., & Golay, X. (2010). Neuroimage, 49(1), Murphy, K., Harris, A. D., Diukova, A., Evans, C. J., Lythgoe, D. J., Zelaya, F., et al. (2011). Magnetic Resonance Imaging, 29(10),