Multi echo Multi slice (MEMS) High Performance fmri at CFMRI... 1

Multi echo Multi slice (MEMS) High Performance fmri at CFMRI Table of Contents Multi 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 1

Introduction A 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). A B Non BOLD signal (noise) TE1=15.5 TE2=36.7 TE3=57.9 % Signal Change % Signal Change C Echo Time (TE) BOLD signal Echo Time (TE) 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) 1 Peltier SJ, Noll DC, T2* dependence of low frequency functional connectivity, Neruoimage, 2002; 16(4) 2

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 multiecho 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). Standard denoising MEICA denoising 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 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). 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) 3

MEMS Protocols The 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 Protocol 1 (3.75mm 3 ) Protocol 2 (3mm 3 ) Resolution 3.75x3.75x3.75mm 3 3x3x3mm 3 Slice orientation Sagittal (other slice orientations are in development) # slices (must be multiple of 3) 42 48 FOV 24cm 21.6cm Matrix 64x64 72x72 Multi slice Factor N N =3 N=3 Acceleration Factor R R=2 R=2 # of Echoes 3 3 TR (may increase if # slices increases) 1sec 1.1sec TE [11.4 25.2 39]msec [13.2 30.3 47.4]msec Bandwidth ±125KHz ±125Khz Flip angle (adjust with TR) 60 60 Scan time 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 4

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. Run MEMS protocol Set up and prepare 1. 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. Scan MEMS protocol 1. 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. 5

fm_grass 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 webschedule program at your earliest convenience. 5. fm_grass Scan 6

6. mems cal 1(see NOTE below) Copy Rx from fm_grass, save Rx, download, and scan. 7. mems cal 2 (see NOTE below) Copy Rx from fm_grass, save Rx, download, and scan. 8. mems_rest_rvs (see NOTE below) Copy Rx from fm_grass, save Rx, download, and scan. 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 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. 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 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. 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. 7

End exam and transfer MEMS Data 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. Reconstruct and pre process MEMS data We 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. fmri data Echo 1 fmri data Echo 2 fmri data Echo 3 Motion correction (3dvolreg) Apply the transformation matrix from Echo 2 (3dAllineate) Motion correction and Registration to one of the TOPUP pair that has the same phase encoding direction ( align_epi_anat.py) Motion correction (3dvolreg) Apply the transformation matrix from Echo 2 (3dAllineate) Apply distortion correction (TOPUP) Apply distortion correction ( TOPUP) Apply distortion correction (TOPUP) 8

5. post processing using MEICA (optional, see Appendix III) : Performs MEICA denoising. System and Data Requirements Add the following path to your ~/matlab/startup.m file. Create the file if the file does not exist. path(path,'/apps/matlabcode/spiralfmap2'); path(path,'/apps/matlabcode/domems'); path(path,'/apps/afni_matlab/matlab'); path(path,'/apps/matlabcode/fmritools'); 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 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) Running the pipeline 1. 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. Error Logging A 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). 9

Output All 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 myhifipa01_e01_afni_al.nii.gz myhifipa01_e02_afni_al.nii.gz myhifipa01_e03_afni_al.nii.gz For a more complete description of the output files, please see Appendix II. 10

Appendix I Table 1: Summary of command line tools for running MEMS protocol Command Usage Description RTctrl start RTctrl Start Start Realtime (Must be done before Start Exam ) RTctrl stop 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 physiocopy cd tail gecopy s server r raid# d studyfolder login physiocopy s server d studyfolder starttime endtime login (*starttime and endtime format: hh:mm) cd /export/home/sdc/rtafni/var/log tail f <last log file name> Transfer DICOM files to server. Transfer physio files to fmrimems server. Check realtime status and log file 11

Appendix II: MEMS Pipeline Output files All 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 (cfmri@ucsd.edu) 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). mems01_e01.nii.gz mems01_e02.nii.gz mems01_e03.nii.gz 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. PhaseOne.nii.gz PhaseTwo.nii.gz 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) 12

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). 13

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 preprocessing steps carried out to the fmri dataset. 14

Appendix III: MEICA (More information will be added in a future version of this manual) 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 ) 15