Analyzing ME data on phantom

AFNI version info (afni -ver): Version AFNI_24.1.00 'Publius Septimius Geta'

Dear AFNI gurus,

We acquired ME data on 7T on a phantom to run some technical tests on our functional sequence. We would like to run afni_proc.py with optimally_combined on this functional data. We could not manage to run the command properly to make it work.
Please help!

Thanks,
Meytal

Hi, Meytal-

It would help if you please provided the command you wanted to run, as a start?

Note that Rick has created a special "simple" version of afni_proc.py for running either single echo ("ap_run_simple_rest.tcsh") or now multi-echo ("ap_run_simple_rest_me.tcsh") FMRI processing, for just such testing. The ME-FMRI version does indeed use -combine_method OC to combine the echoes.

Here's an example of running the command with 2 runs of 3 echoes each (you could just remove line 4 if you have only a single run, or add more similar commands if you have more; also, line 6 is not necessary here if you don't want to remove any initial time points from input runs, like if you don't have pre-steady state):

ap_run_simple_rest_me.tcsh                                       \
    -subjid sub-005                                              \
    -epi_me_run ../func/sub-*_task-rest_run-1_echo-*_bold.nii.gz \
    -epi_me_run ../func/sub-*_task-rest_run-2_echo-*_bold.nii.gz \
    -echo_times 12.5 27.6 42.7                                   \
    -nt_rm 2                                                     \
    -run_proc

This command was just added very recently, so you miiight still need to update your AFNI binaries still.

--pt

ps: if you want to see the full command it creates, it fills in this with variables (where $opt_me_run is the input EPI datasets), but if it were me I would just use the very convenient version above:

afni_proc.py                                                        \
    -subj_id                   $subjid \
    -blocks                    tshift align tlrc volreg mask        \
                               combine blur scale regress           \
    -radial_correlate_blocks   tcat volreg regress                  \
    -copy_anat                 $anat \
    $opt_me_run \
    -echo_times                $echo_times \
    -combine_method            OC \
    -reg_echo                  $reg_echo \
    -tcat_remove_first_trs     $nt_rm \
    -tshift_interp             -wsinc9                              \
    -align_unifize_epi         local                                \
    -align_opts_aea            -cost lpc+ZZ -giant_move -check_flip \
    -tlrc_base                 $template \
    -volreg_align_to           MIN_OUTLIER                          \
    -volreg_align_e2a                                               \
    -volreg_tlrc_warp                                               \
    -volreg_warp_final_interp  wsinc5                               \
    -volreg_compute_tsnr       yes                                  \
    -mask_epi_anat             yes                                  \
    -blur_size                 4                                    \
    -regress_censor_motion     0.25                                 \
    -regress_censor_outliers   0.05                                 \
    -regress_motion_per_run                                         \
    -regress_apply_mot_types   demean deriv                         \
    -regress_est_blur_epits                                         \
    -regress_est_blur_errts                                         \
    -regress_make_ideal_sum    sum_ideal.1D                         \
    -html_review_style         pythonic

amazing! it seems to be working with my data, thanks!

Rockin', good to know. Please just let us know if there are any issues.

--pt