rtfmri weiskopf nikolaus050616e - organization for … materials/rtfmri... · 0d[ 3odqfn ,qvwlwxwh...

05.06.2016

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Max Planck Institute for Human Cognitive and Brain Sciences

Department of Neurophysics,Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

Technical Aspects of Real-Time fMRI

Nikolaus Weiskopf

For Participants of OHBM Educational Course:“Real-time fMRI: Fundamental Principles for Clinical Applications”


• What is Real-Time fMRI (rtfMRI)?• Applications• Technical Challenges• Prospective Motion Correction• Fast and Robust Data Acquisition• Discussion and Outlook

Overview



Overview


rtfMRI: Experimental SetupBOLD

Processing time < Data acquistion time

Cox et al., Magn. Reson. Med. 1995

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Max Planck Institute for Human Cognitive and Brain Sciences Max Planck Institute for Human Cognitive and Brain Sciences


Overview


Applications of rtfMRI• Online quality assurance (QA) and subject compliance• Monitoring anaesthesia effects Wibral et al., Neuroimage 2007• Functional localizers• Piloting new paradigms• Teaching fMRI

Weiskopf et al., MRI 2007

Students in fMRI practical

Functional neuroanatomy:finger tapping


Online Quality Assurance

Weiskopf et al., MRI 2007

EPI

Differenceand varianceimages

Head motion

Meanvariance

Backgroundnoise levelfor eachslice

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Neurofeedback: Closing the LoopBOLD

Weiskopf et al., Neuroimage 2012Max Planck Institute for Human Cognitive and Brain Sciences

Neurofeedback: Closing the LoopBOLD

Weiskopf et al., Neuroimage 2012


The Classic Neuroimaging Experiment

Behavior/Stimulus NeuronalActivity

Painful electric stimulus ACC BOLD activity is increased

Davis et al., J Neurophys 1997Max Planck Institute for Human Cognitive and Brain Sciences

Neurofeedback: Swapping the Variables


Pain rating changes ACC BOLD activity is increased

deCharms et al., PNAS 2005

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Neurofeedback: Swapping the Variables


Pain rating changes

Weiskopf et al., Neuroimage 2012Sources: Wassermann, T. Brain Stim.; T. VarcoMax Planck Institute for Human Cognitive and Brain Sciences


Overview


rtfMRI: Timing

Processing time < Data acquistion time

Data Analysis

Image ReconstructionAcquisition Time


Challenges of rtfMRI

• Fast processing at a high data rate• Data accumulating over time, i.e., few data/low SNR initiallyand increasing memory demand• Fast data acquisition and image reconstruction• Stability, robustness – e.g., experiment cannot be repeated• Real-time control and time stamping• Usability: on-line display, assessment of data, definition ofparadigm• Access to (raw) data on vendor‘s platform

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Overview


Real-time Motion Correction

3D motion correction

Image time-series

- =Spurious signal change

Mathiak and Posse, MRM 2001

w/o correction

with correction


Scanner Host Computer

Camera System

Imaging Gradients

FOV Marker pose

XPACE Libraries†

FOV realigned

*Kineticor, HI†Maclaren et al. PLOS One, 7(11), 2012

Ultrafast Prospective Motion Correction (PMC)

Gx

Gy

Gz



Camera System

Imaging Gradients

FOV Marker pose

XPACE Libraries†

FOV realigned


Gx

Gy

Gz*Kineticor, HI†Maclaren et al. PLOS One, 7(11), 2012

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Camera System

Imaging GradientsGx

Gy

Gz

FOV Marker pose

XPACE Libraries†

FOV realigned


*Kineticor, HI†Maclaren et al. PLOS One, 7(11), 2012

Max Planck Institute for Human Cognitive and Brain SciencesTodd et al, Neuroimage 2015

Optical Prospective Motion Correction (PMC)


PMC detects cardio-respiratory effects

Image volume

Compliant volunteer with minimal head motion and some deliberate movement for comparison

~0.5 mm

cardiac ~3 mm~1 mm


Motor Task and Head MotionPMC OffNo Motion PMC OnNo Motion PMC OffMotion PMC OnMotion

Displayed at p < 0.001, uncorrected Todd et al., Neuroimage 2015

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Visual Stimulation and Head Motion

Displayed at p < 0.001, uncorrected Todd et al., Neuroimage 2015

PMC OffNo Motion PMC OnNo Motion PMC OffMotion PMC OnMotion



Overview


Optimal EPI Parameters for Maximal BOLD Sensitivity• Optimal parameters determined from field map database by modeling of susceptibility artifacts• Gains > 80%

Weiskopf et al., Neuroimage 2006Volz et al., under revisionMax Planck Institute for Human Cognitive and Brain Sciences

Multi-echo EPI: Distortion and Dropout Reduction

ORIGINAL UNDISTORTEDTE 28 ms TE 46 ms CNR-OPTIMIZED

L R

PE PEz-ShimOriginal

Dropoutin amgydalaPosse et al., Neuroimage 2003

Dropout compensation Distortion correction

Weiskopf et al., Neuroimage 2003

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2D EPI 2D Multiband EPI 3D EPISlice excitation

~50 msper slice

~3 secpervolume

Simple iFFT


2D EPI 2D Multiband EPI 3D EPIMulti-Slice excitation

~50 msper slice

~3 secper volume

Parallel imaging

~50 msper slice group

~1.5 secper volume


2D EPI 2D Multiband EPI 3D EPIVolumeexcitation

~50 msper slice

~3 secper volume

~50 msper slice

~1.5 secper volume

Simple iFFT

~3 secper volume

~3 secper volume

~50 ms


2D EPI 2D Multiband EPI 3D EPIVolumeexcitation

~50 msper slice

~3 secper volume

~50 msper slice

~1.5 secper volume

~1.5 secper volume

~1.5 secper volume

Parallel imaging

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2D Multi-band EPI with TR from 1.1 s–6.6 s

MB 1

MB 2

MB 4

MB 6

Slice-GRAPPA Split Slice-GRAPPA

Displayed at p < 0.001, uncorrectedTodd et al., Neuroimage 2016


2D Multi-band EPI: False Positive Activations

Slice

Phase

MB 6, Slice-GRAPPA

Simultaneously excited slices

CAIPI-shift

MB 1 MB 2 MB 4

Todd et al., Neuroimage 2016


3D Multi-Echo EPI with Z-Shim• Multi-shot acquisition• Z-shim gradient compensates dropout• Two-directional parallel imaging allows TR ~ 1s for whole brain

Dropoutin orbito-frontalcortex

Single echo3D EPI

Dual echo3D EPIwith z-shim

tSNR


*

-0.10

0.10.20.30.4

Visit 1 Visit 2 Visit 3 Visit 4Contr

ast E

stimate

s

NF-Training: Upregulation - Baseline

-0.20-0.100.000.100.200.30

1 2 3 4 5 6 7 8 9 10Gro

up

Comp

osite

score

Patient

Post-training Visit - Baseline Visit

Bette

r Pe

rform

ance

Example Application:SMA Training in Huntington’s Disease

Papoutsi et al., in preparation

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Multi-Slab Echo-Volumar Imaging (EVI)

Posse et al., Neuroimage 2012

EVI (TR = 286 ms) 2D EPI (TR = 2s)


3D EPI: Fast Acquisition Resolves Respiratory Movement, Reducing Physiological Noise

Dy, m

m/pit

ch, de

g/resp

, a.u.

Respiration belt Translation Rotation



Overview


Discussion• Main applications: neurofeedback, QA, functional localizers• Many off-line methods are available for rtfMRI• More off-line methods will become available with increasing computational power (Moore’s Law) and dedicated rtfMRI methods development (e.g., in Braintrain project).• fMRI acquisitions can be tailored to rtfMRI due to various techniques, e.g., multi-band, 3D/2D EPI.• Sampling speeds are faster than physiological noise and BOLD response – no limitation for neurofeedback any more.• PMC promises to addresses motion and physiological artifacts.

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Outlook• Simplified access to proven advanced methods• Leverage RF coil and high field technology• Deployment in clinical trials• Better understand the effects of data quality on neurofeedback learning: speed, artifacts, SNR


Supported by MPG, EU FP7, MRC DPFS,ERC, UCL, Horizon 2020, Siemens, Wellcome Trust

Thank you

N. Todd M. CallaghanJ. Magerkurth

O. Josephs

S. Volz

Neurophysics @ MPI-CBS

Physics @ WTCN, UCL

M. Papoutsi, G. Rees, S. Tabrizi (UCL)R. Goebel, B. Sorger (Maastricht)F. Scharnowski (Zuerich) N. Birbaumer, R. Veit (Tuebingen)C. Hutton (Siemens UK)K. Mathiak (Aachen) M. Zaitsev, M. Herbst (Freiburg)E. Yacoub, S. Moeller, E. Auerbach(Minnesota)

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