Signal and Noise in fMRI John VanMeter, Ph.D. Center for Functional and Molecular Imaging Georgetown University Medical

1. Signal and Noise in fMRI • John VanMeter, Ph.D. • Center for Functional and Molecular Imaging • Georgetown University Medical Center

2. Outline • Definition of SNR and CNR in context of anatomic imaging • Definition of functional SNR • Sources of noise in MRI • Source of noise in fMRI • Changes in MRI SNR and functional SNR with increased magnetic field strength

3. MRI Signal and Noise • Signal is primarily dependent on number of protons in the voxel • Noise can come from RF energy leaking into the scanner room, random fluctuations in electrical current, etc. • The body creates noise in the MR signal via changes in current in the body producing small changes in the magnet field; breathing can change homogeneity

4. Measuring MRI Signal-to-Noise Ratio (SNR) • Signal is the intensity (brightness) of one or more pixels in the object of interest. • Noise is the intensity of one or more pixels in the ‘air’ (i.e. outside the object of interest). SNR = Signal (low SNR = grainy, fuzzy images) Noise • Fundamental measure of image quality

5. S N MRI SNR – Example 1 • S = 700 • N = 20 • SNR = 700 / 20 • = 35

6. S N MRI SNR – Example 2 • S = 300 • N = 50 • SNR = 300 / 50 • = 6

7. MRI SNR - Side-by-Side SNR = 35 SNR = 6

8. SNR in Terms of fMRI • MRI SNR is not the most important issue with regard to functional MRI • Functional SNR is contingent on ability to detect changes in BOLD signal between conditions (across time) • Underlying MRI SNR still important in terms of providing base for signal in functional SNR but several other factors affect signal and noise in fMRI data

9. Affect of MRI SNR on Functional SNR • Increase in fMRI signal due to BOLD affect “rides on top” of signal of in MRI scan • Imagine 2% increase in signal between these two fMRI scans • In which image will the 2% change be more detectable?

10. Changes in BOLD Signal are Small • Visual and sensorimotor areas percent change might be as high 5% • For most other cortical areas expected percent change is on the order of 1-3%

11. Measuring Percent Signal Change

12. MRI Contrast-to-Noise Ratio (CNR) • Measure of separation in terms of average intensity between two tissues of interest • Defined as difference between the SNR of the two tissues (A & B): CNR = SignalA – SignalB Noise

13. SW SG N MRI CNR – Example 1 • SW = 700, SG = 200 • N = 20 • CNRWG = (700 – 200) / 20 • = 25

14. SW SG N MRI CNR – Example 2 SW = 200, SG = 100 N = 50 CNRWG = (200 – 100) / 50 = 2

15. SW SW SG SG N N MRI CNR Side-by-Side CNRWG = 35 CNRWG = 6

16. Functional CNR vs Functional SNR • Generally CNR between tissues is unimportant in fMRI as expect activation only in gray matter • Some researchers refer to difference between “On” and “Off” as dynamic CNR or functional CNR • Probably more accurate to refer to ability to detect changes related to activity as functional SNR

17. Functional SNR is a dependent on differences in signal across time • Ability to distinguish differences between different conditions - effect size

18. Differences Between Two Conditions • Typically compare BOLD signal in the same area under different conditions • Example fusiform face area; responds to both faces and tools but about 0.2% more to faces

20. Sources of Noise in fMRI Data • System noise • Thermal noise • Signal drift • Subject dependent noise • Physiological noise • Variability in BOLD response • Variability across sessions within the same subject • Variability across subjects

21. Thermal Noise • Intrinsic noise due to thermal motion of electrons • In subject • In RF equipment • Increases with temperature - atoms move faster; more collisions; greater loss of energy • Unfortunately increases with field strength approximately linearly • Affects temporal fluctuations and is equally likely to add or subtract thus roughly Gaussian (i.e. normally) distributed

22. Signal Drift Across Time • Magnetic field has slight drifts in strength over time produces drift in signal most likely leading to partial volume effects • Gradually, over time the MRI signal in a voxel drifts • This drift can vary from one voxel to the next both in degree and direction!

23. Signal Drift

24. Affect of Signal Drift

25. Effect of Nonlinear Drifts

26. Physiological Noise • Subject movement during scan • Single largest source of noise in fMRI data • Extremely problematic if motion is timed with task • Makes studies with overt speech during the scan quite difficult • Motion within a volume acquisition most problematic than between volumes

27. Subject Motion

28. Pulsatile Motion of Brain • Influx of blood into brain induces movement especially around base of brain - why there? • Short TR’s can also pick-up noise due to respiration (TR<2500ms) and cardiac (TR<500ms) cycle

29. Map showing standard deviation of intensity over time • Two sources of noise evident • Why do edges of brain show large effect? • Often referred to as “ringing”

30. Power Spectrum Task

31. Other Sources of Physiological Noise • Change in CO2 - hyperventilation produces change in O2 content of blood; blood flow increases to compensate • Drug affects - antihistamines, etc • Smokers vs. Non-smokers • Hypoactivation on attentional task after abstaining for 1hr reversed following nicotine patch (Lawrence et al, 2002)

32. Physiological Baseline • BOLD response changes (up for hypercapnia, down for hypocapnia) • But CBF, CMRO2 unchanged (probably)(Brown et al JCBFM 2003) Cohen et al. JCBFM 2002

33. Genetic Based Differences • ApoE risk factor for Alzheimer’s disease • Study of asymptomatic carriers • Reduced activation in hippocampus on a memory task for high risk carriers (AS Fleisher, et al, Neurobiology of Aging, 2008)

34. Noise from Neural Activity Not of Interest • Eye movements - results in activation of the frontal eye-fields • Noise of the scanner - activates auditory cortices • Usually not a problem as noise common to both conditions • Auditory experiments difficult though • Other thoughts - what’s for dinner, going over a to-do list, wondering what the experiment is testing (grad students), etc

35. Behavioral and Cognitive Variability • Passive tasks are prone to drift in subject attention and/or arousal • Difficult to identify performance on tasks and compare across subjects • Tasks with responses can lead to variations in reaction/response time • Speed-accuracy trade-off • Task strategies used can differ • Task difficulty especially between groups of subjects very problematic

36. Inter-Subject Variability

37. Inter-Session Variability

38. Intra-Session Variability

39. 99-Scanning Sessions • Same subject participated in 99 identical scanning sessions • 33 each for motor task, visual task, and a cognitive task • Everything kept exactly the same • Considerable variability was observed

40. 33 Motor Sessions McGonigle, et al., Neuroimage, 2000

41. 33 Cognitive Sessions

42. Strategies for Dealing with Noise & Improving Signal • MRI Center Steps • Measure stability of signal over time • Ensure stability of equipment • Eliminate RF-noise • Researcher • Formalize instructions (use scripts) • Train subjects ahead of time • Instruct subjects to use same strategy • Stress importance of staying still, focus, etc. • Use better post-processing techniques • Increase field strength

43. Post-processing of 33 Motor Sessions Pre Post Smith, et al., Human Brain Mapping, 2005

44. Signal Averaging • Averaging across multiple trials greatly helps to improve SNR • Each graph shows 20 traces of 1 trial, average of 4 trials, average of 9 trials, etc

45. Increasing MRI Signal with Stronger Magnets • Increase magnetic field strength • Plus: • more protons pulled into alignment thus greater net magnetization resulting in increased MRI signal • Minus: • shortens T2* resulting in larger spatial distortions with gradient echo sequences • Requires larger RF pulses thus SAR goes up (why?)

46. Susceptibility Distortion Increases with Field Strength 1.5T 4.0T

47. Changes in Signal with Field Strength • Quadratic increase in MRI signal with increase in field strength • Thermal noise scales linearly with field strength • Raw MRI SNR thus only scales linearly • What about functional SNR?

48. Functional SNR Linearly Increases with Field Strength?

49. Functional SNR vs Field Strength • MRI signal goes up quadratically • Thermal noise goes up linearly • Physiological noise goes up quadratically • Eventually functional SNR expected to plateau

50. Upsides to Field Strength for Functional SNR • Increase in number of voxels activated presumably improves detectability • T2* of blood much shorteras field strength increases thus signal drops off in larger vessels • Linear increase in large vessels • Quadratic increase in small vessels • Thus, spatial specificity increases