echo Multi slice (MEMS) High Performance fMRI at CFMRI

©CFMRI Last modified 4/11/2014 1

Multi‐echoMulti‐slice(MEMS)HighPerformancefMRIatCFMRI

TableofContentsMulti‐echo Multi‐slice (MEMS) High Performance fMRI at CFMRI ........................................................................... 1

Introduction ........................................................................................................................................................... 2

MEMS Protocols .................................................................................................................................................... 4

Run MEMS protocol ............................................................................................................................................... 5

Set up and prepare ............................................................................................................................................ 5

Scan MEMS protocol ......................................................................................................................................... 5

End exam and transfer MEMS Data ................................................................................................................... 8

Reconstruct and pre‐process MEMS data ............................................................................................................. 8

Data Requirements ............................................................................................................................................ 9

Running the pipeline ......................................................................................................................................... 9

Error Logging ..................................................................................................................................................... 9

Output ............................................................................................................................................................. 10

Appendix I ............................................................................................................................................................ 11

Appendix II: MEMS Pipeline Output files ............................................................................................................ 12

Appendix III: MEICA (More information will be added as they become available) ............................................ 15


IntroductionA major need in the analysis of Blood Oxygen Level Dependent (BOLD) functional MRI (fMRI) data is the ability to

distinguish BOLD related signals from non‐BOLD related signals, such as those due to physiological fluctuations

or head motion. Previous studies 1 have shown that the amplitude of the BOLD signal variations has a linear

dependence on echo time (TE), whereas the amplitude of the non‐BOLD signal variations does not (Figure 1).

1 Peltier SJ, Noll DC, T2* dependence of low frequency functional connectivity, Neruoimage, 2002; 16(4)

TE1=15.5

TE2=36.7

TE3=57.9

Echo Time (TE)

% Signal Chan

ge

% Signal Chan

ge

Echo Time (TE)

Non‐BOLD signal (noise)

BOLD signal

AB

C

Figure 1. A: fMRI data acquired at 3 different TEs. B: noise signal

amplitude does not depend on TE. C: BOLD signal amplitude has a

linear dependence on TE. (Figure credit: Valur Olaffson)


Kundu et al 2 3 extended this observation

to Independent Component Analysis (ICA)

of multi‐echo fMRI data, where ICA

components that display TE dependencies

are considered BOLD signals; ICA

components that do not display TE

dependencies are considered noise and

thus removed from the fMRI data. This

fMRI denoising method, known as multi‐

echo ICA (ME‐ICA), has been shown to

robustly detect motion and other non‐

BOLD related signals, and to significantly

improve signal to noise ratio and

functional connectivity estimates (Figure

2).

A potential disadvantage of multi‐echo

fMRI is the time cost associated with

acquiring multiple echoes. However

various acceleration techniques are

available to speed up fMRI data

acquisition, such as the simultaneous

multi‐slice (SMS) technique where multiple ( N>1) slices are excited at a time providing a factor of N reduction

in scan time, and parallel imaging methods (such as GRAPPA or SENSE) that shorten the time required for the

readout of each echo. At the Center for fMRI (CFMRI) we provide an accelerated multi‐echo multi‐slice (MEMS)

protocol that is capable of acquiring full brain multi‐echo fMRI data with a TR of ~ 1sec. The use of multi‐echo

data with shorter TRs has been shown to improve the ability to detect functional networks.

Note that the MEMS protocol requires the use of the Nova Medical 32 channel head coil.

2 Kundu P et al, Differentiating BOLD and non‐BOLD signals in fMRI time series using multi‐echo EPI. Neuroimage, 2012;60(3) 3 Kundu P et al, Integrated strategy for improving functional connectivity mapping using multi‐echo fMRI, PNAS, 2013; 110(40)

Figure 2. Comparison of the default mode network maps

after applying standard denoising method (i.e. motion

correction) and MEICA method (Figure credit: Kundu et al;

2013).

Standard den

oising

MEICA den

oising


MEMSProtocolsThe MEMS protocol can be found on both the 3T West and 3T East scanners under ADULT‐>HEAD‐>MEMS.

CFMRI offers the protocol at two different resolutions, 3.75mm or 3mm isotropic. Table 1 below summarizes

some of the important fMRI parameters of the protocols.

Table 1. MEMS protocols

Protocol1 (3.75mm3) Protocol2(3mm3)

Resolution 3.75x3.75x3.75mm3 3x3x3mm3

Sliceorientation Sagittal (other slice orientations are in development)

#slices(mustbemultipleof3) 42 48

FOV 24cm 21.6cm

Matrix 64x64 72x72

Multi‐sliceFactorN N =3 N=3

AccelerationFactorR R=2 R=2

#ofEchoes 3 3

TR(mayincreaseif#slicesincreases)

1sec

1.1sec

TE [11.4 25.2 39]msec [13.2 30.3 47.4]msec

Bandwidth ±125KHz ±125Khz

Flipangle(adjustwithTR) 60 60

Scantime Adjustable by User (typically 5‐10 mins)

The MEMS protocol contains the following scans:

1. Localizer (~ 15sec)

2. ASSET calibration (~6 sec)

3. FSPGR (~10mins, adjustable) : T1 weighted high resolution scan

4. HOS(~30 sec): High order shim

5. fm_grass(1‐2mins): fieldmap

6. MEMS calibration 1(~18 sec): Calibration 1 for MEMS recon

7. MEMS calibration 2(~18 sec): Calibration 2 for MEMS recon

8. MEMS fMRI(~10mins, adjustable): functional or resting fMRI scans (can have multiple scans)

9. MEMS_topup_fwd(~6 sec): forward TOPUP scan


10. MEMS_topup_rvs(~6 sec): reverse TOPUP scan

The fm_grass scan and the calibration 1 and 2 scans are required for reconstructing the fMRI scan. If there is

more than one fMRI scan, the graphical prescriptions of all fMRI scans have to be matched exactly (use copyRx

on the scanner). If the prescription changes, a new set of the fm_grass, calibration 1 and 2, and the topup fwd

and rvs scans matching the new prescription will be required.

The topup_fwd and topup_rvs scan pair are used to measure a fieldmap which can be applied to the fMRI

images for correcting geometric distortions. They are typically scanned immediately before or after the fMRI

scans. In the case of a scan session containing several fMRI scans in a row where there is concern about subject

motion during the session, users may acquire one topup scan pair before the fMRI scans and another pair at the

end of the session. The first pair can be used for correcting the fMRI data before the motion occurs, and the

second pair for correcting the fMRI data after the motion.

RunMEMSprotocol

Setupandprepare1. Place the 32channel coil on the scanner table and plug it in. Make sure the coil is recognized by the

scanner by checking the information on the iROC monitor on top of the scanner.

2. Set up peripheral equipment such as the projector, screen, and stimulus laptop etc if needed.

3. Set up the subject on scanner patient table.

4. Setup physiological monitoring if needed.

5. On the console computer, click the downward arrow on the Tools icon. In the drop down menu, select

Command Window. In the command window type ‘RTctrl start’ to start realtime. Drag this window to

the lower right corner of the screen so it is easily accessible and not blocking the scan area of the screen.

(NOTE: this step must be done before Start Exam in step 6)

6. Register the subject and Start Exam.

ScanMEMSprotocol1. Localizer

Save Rx and Scan

2. Asset Cal

Setup, Prescribe Rx, Save Rx, and Scan

3. FSPGR T1

Setup, Prescribe Rx, Save Rx, and Scan.

IMPORTANT: While waiting for the FSPGR to finish, prescribe and save the “fm_grass” scan below the

HOS scan. This step MUST be done before running the next HOS scan.

4. HOS (high order shim)

Setup and Save Rx (no need to prescribe slices). Click OK in the popup window saying “Running high

order shim for clinical Protocol: fm_grass”. Then click Scan.


Once the scan finishes, an HOS window will appear. In the HOS window, adjust the size and location of

the ROIs to enclose the whole brain (see figure below);

Click Calculate Shim, then Done. Make a note of the current RMS and predicted RMS values reported in

the lower left of the window. The difference between the two values is indicative of HOS efficacy.

Run the HOS a second time by clicking Scan and choose Same Series in the popup window. Once the

HOS window appears, click Calculate Shim (do not modify the ROIs). Verify that the new current and

predicted RMS values are converging and consistent with the previously predicted value (See figure

below). If yes, click Done to finish.

The HOS scan can be repeated a third time if needed. We have typically seen RMS value convergence in

two iterations. In case the RMS value does not converge in three iterations, please click Quit to skip HOS.

Please alert the CFMRI staff of the HOS malfunction, or report the problem using the online web‐

schedule program at your earliest convenience.

5. fm_grass

Scan

fm_grass


6. mems cal 1(see NOTE below)

Copy Rx from fm_grass, save Rx, download, and scan.

7. mems cal 2 (see NOTE below)


8. mems_rest_rvs (see NOTE below)


9. mems_topup_fwd (see NOTE below)

Copy Rx from fm_grass , save Rx, download and scan.

10. mems_topup_rvs (see NOTE below)

Copy Rx from fm_grass, save Rx, download and scan.

NOTE:

Each of the mems scans (scan 6‐10) needs to be downloaded prior to the respective scan.

The download step ensures that RDS client is started to receive the acquired MRI data. If

the RDS client is not ON, no MRI data will be saved.

Below are some useful tools:

o Type ck in the command window to check if the RDS client is ON. This should be

checked after each download to make sure RDS client is ON before Scan.

o Type memslist in the command window to list the raw data files. Anytime during an

hcp scan, use memslist to check if data is being saved.

If an MEMS scan has to be stopped before it finishes, for example when subject activates

the emergency squeeze ball, please do the following:

o Type kk in the command window to kill the RDS client then press Stop Scan button.

After the emergency situation or errors are cleared, copy & paste the scan, download

and Scan.

o If the Stop Scan button is pressed before the RDS client is killed, a TPS reset must be

performed before the scan can continue. A TPS reset usually takes 2‐3 minutes. After

the TPS reset, copy & paste the scan, download and Scan.

Due to limitations with the RDS server software, after a mems scan finishes, the status of

the scan shows “Action Failed”. You can ignore this status. Additionally, as soon as the

next scan is downloaded, the previous mems scan is pushed downward in the scan list as

if it has not been scanned. Please pay attention to which scans have already been done

and which have not.

Appendix I lists all command‐line tools available for use with the MEMS protocols.


EndexamandtransferMEMSData On the computer console, click on End ‐> End Exam

In the command window, type RTctrl stop to stop realtime.

In the command window, type memscopy to transfer mems data (P files).

Usage: memscopy ‐s server –r raid# ‐d studyfolder login

example: memscopy –s fmrimems –r raid16 –d myhcpfolder mylogin

Transfer Dicom data using gecopy.

Usage: gecopy ‐s server ‐r raid# ‐d studyfolder login

example: gecopy –s fmrimems –r raid16 –d myhcpfolder mylogin

Transfer physio data using physiocopy as needed.

Usage: physiocopy ‐s server ‐d studyfolder starttime endtime login

example: physiocopy –s fmrimems –d myhcpfolder 14:00 15:00 mylogin

After data transfer completes, close all command windows and clean up the scanner suite.

Reconstructandpre‐processMEMSdataWe provide a Pipeline for reconstructing the MEMS data. The pipeline consists of four functional modules:

1. Data validation: checks if all required data files are present.

2. Data reconstruction: reconstructs the fMRI images.

3. Quality control: calculates temporal SNR and estimates T2* maps.

4. Pre‐processing (optional): performs motion correction, registration and distortion correction (see the

diagram below). Due to the use of in‐plane acceleration in the protocol, the in‐plane image distortions

are typically small, and so this step may be skipped at the discretion of the user.

Motion correction and

Registration to one of the

TOPUP pair that has the

same phase encoding

direction

( align_epi_anat.py)

fMRI data Echo 1

Apply distortion correction

( TOPUP)

fMRI data Echo 2 fMRI data Echo 3

Motion correction

(3dvolreg)

Apply the transformation

matrix from Echo 2

(3dAllineate)


(TOPUP)

Motion correction

(3dvolreg)

Apply the transformation

matrix from Echo 2

(3dAllineate)


(TOPUP)


5. post‐processing using MEICA (optional, see Appendix III) : Performs MEICA denoising.

SystemandDataRequirements Add the following path to your ~/matlab/startup.m file. Create the file if the file does not exist.

All data must be located in the same folder. Use unix command ls to check if all the data are present (see

example below). The data required include dicom files of the fmap scan and the T1 structural scan

(stored in exam/subject directory), and pfiles from the calibration scan 1 and 2 , one or more fMRI

scans, and two TOPUP scans

Runningthepipeline1. Login into your assigned server (either fmrimems.ucsd.edu or fmrimems2.ucsd.edu).

2. Change to the directory where the data are located.

3. Type at the Linux prompt: domems.py [‐topup] [‐meica]

Options:

‐topup: to apply EPI distortion correction

‐meica: to apply MultiEcho ICA (see Appendix III)

Your job will be queued. Type qstat to check queue status, or qdel followed by the job number to remove from

the queue.

ErrorLoggingA log file is automatically saved in the current data directory under the log folder. Automatic email notifications

will also be sent upon job success or failure to the email address registered with the server account (usually the

PI’s email address).

path(path,'/apps/matlabcode/spiralfmap2');

path(path,'/apps/matlabcode/domems');

path(path,'/apps/afni_matlab/matlab');

path(path,'/apps/matlabcode/fmritools');

fmrimems2.ucsd.edu:mems_data>> ls e207 (contains the s‐folders for the fmap_grass scan and the T1 structural scan) P12288_spep_mems_110829_0917.7 (Pfile for calibration scan 1) P12800_spep_mems_110829_0918.7 (Pfile for calibration scan2 ) P13312_spep_mems_110829_0918.7 (Pfile for fMRI scan) P13824_spep_mems_110829_0919.7 (Pfile for topup foward) P14336_spep_mems_110829_0920.7 (Pfile for topup reverse)


OutputAll output is saved under the processed folder. The pre‐processed fMRI data (motion and distortion corrected)

are: myhifipa0<study num>_e0<echo num>_afni_al.nii.gz

Example: myhifipa01_e01_afni_al.nii.gz

For a more complete description of the output files, please see Appendix II.

myhifipa01_e01_afni_al.nii.gz




AppendixI

Table 1: Summary of command‐line tools for running MEMS protocol

Command Usage Description

RTctrlstart RTctrl Start Start Realtime (Must be done before “Start Exam”)

RTctrlstop RTctrl Stop End Realtime (Must be done after “End Exam”)

ck ck Check if RDS client is ON

kk kk Kill all active RDS clients

memslist memslist List raw data files of the current MEMS scan session.

memscopy memscopy ‐s server ‐d studyfolder login Transfer MEMS raw data files to server.

gecopy gecopy ‐s server ‐r raid# ‐d studyfolder login

Transfer DICOM files to server.

physiocopy physiocopy ‐s server –d studyfolder starttime endtime login

(*starttime and endtime format: hh:mm)

Transfer physio files to fmrimems server.

cdtail

cd /export/home/sdc/RTafni/var/log tail –f <last log file name>

Check realtime status and log file


AppendixII:MEMSPipelineOutputfilesAll outputs are saved under “processed” folder. Below is a list of selected outputs that users may examine for

sanity check purpose or for trouble shooting when there are errors in the reconstruction process. Users should

contact CFMRI ([email protected]) for questions regarding these files.

bcaipi_<orientation>_<phase encoding dir>_mems0<study num>brik_e0<echo num>+orig.BRIK Example: bcaipi_sag_rev_mems01brik_e01+orig.BRIK

mems<study num>_e0<echo num>.nii.gz Example: mems01_e01.nii.gz

Reconstructed fMRI data (all 3 echoes) in BRIK and NIFTI format before motion correction and distortion correction. These images have visible distortions, but should be free of obvious artifacts, and have typical T2* BOLD contrast (the example below shows four brain slices).

topup_sag_fwdbrik+orig.BRIK and topup_sag_revbrik+orig.BRIK PhaseOne.nii.gz and PhaseTwo.nii.gz

Reconstructed images from the TOPUP scan pair in BRIK and NIFTI format. Verify that the distortions in these two datasets are opposite in direction to each other, and there are no obvious imaging artifacts.

coilmap_sag_rev_sess_fmapbrik+orig.BRIK

This is the reconstructed data from calibration 1 which will be used to estimate coil sensitivity profile. This scan is a spin echo, single band and single echo scan. The images should be free of artifacts, and have minimal signal dropout in the OFC area of the brain (see right).

anat.nii.gz

T1 Structure dataset in NIFTI format (if provided)

mems01_e01.nii.gz

mems01_e03.nii.gz

mems01_e02.nii.gz

PhaseOne.nii.gz

PhaseTwo.nii.gz


bcaipi_<orientation>_<phase encoding dir>_mems0<study num>brik _T2s.jpg bcaipi_<orientation>_<phase encoding dir>_mems0<study num>brik _T2s_hist.jpg bcaipi_<orientation>_<phase encoding dir>_mems0<study num>brik _e01_tSNR.jpg bcaipi_<orientation>_<phase encoding dir>_mems0<study num>brik _e01_tSNR_hist.jpg bcaipi_<orientation>_<phase encoding dir>_mems0<study num>brik _e02_tSNR.jpg bcaipi_<orientation>_<phase encoding dir>_mems0<study num>brik _e02_tSNR_hist.jpg bcaipi_<orientation>_<phase encoding dir>_mems0<study num>brik _e03_tSNR.jpg bcaipi_<orientation>_<phase encoding dir>_mems0<study num>brik _e03_tSNR_hist.jpg

The T2* and tSNR maps generated for Quality Assurance (see examples below).


b02b0.cnf acqparams5r.txt BothPhases.topup_log Coefficents_fieldcoef.nii.gz Coefficents_movpar.txt TopupField.nii.gz Magnitudes.nii.gz

Topup configuration and intermediate files (for more information about the TOPUP distortion correction method please refer to the FSL web site).

mems0<study num>_e0<echo num>_vr.nii.gz mems0<study num>_e0<echo num>_afni_al.nii.gz mems0<study num>_e0<echo num>.nii.gz_vr_motion.1D mems0<study num>_e0<echo num>.nii.gz_al_mat.aff12.1D mems0<study num>_e0<echo num>.nii.gz_al_reg_mat.aff12.1D

Motion corrected and reregistered fmri data (all three echoes). The 1D files are the motion parameters and transformation matrices. Please see Reconstruct and pre‐process MEMS data section for information on registration steps.

script_<timestamp>.sh Example: script_2013‐10‐24‐18:59:34.sh

Pre‐processing script containing motion and distortion correction steps. User can look up the pre‐processing steps carried out to the fMRI dataset.


AppendixIII:MEICA(Moreinformationwillbeaddedinafutureversionofthismanual)

The Pipeline also supports the MEICA denoising method. To invoke MEICA denoising automatically after the

pipeline finishes reconstructing data, use the ‐‐meica option when calling domems.py. For example

domems.py ‐‐meica

The script requires the following python path to be included in your .cshrc file:

set path=( $path . ~/bin /usr/local/bin /apps/Enthought/Canopy_64bit/User/bin )

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