Method 1: Manual conversion of data

The CFMM DICOM Server

MRI datasets acquired at CFMM are stored in the CFMM DICOM server. To download the data, go to CFMM Study Managment, then click on the link to “DICOM Data Browser”. To access the data of a specific project, type PRINCIPAL^PROJECT-ID (e.g., DIEDRICHSEN^SMP) in the “Study Description” search field, then click Submit. You will see the list of datasets (i.e., participants, sessions…) collected for your study. Each dataset is identified by a Patient’s Name with a standard format YYYY_MM_DD_PROJECT-ID_SUBJ-ID (e.g., 2024_03_13_SMP1_100). In each dataset, MRI data are stored in the DICOM format (.dcm). **You don’t need to download the datasets from the CFMM DICOM Server to perform the conversion to Nifti (.nii).

First step: cfmm2tar

As of February 2026, singularity run /srv/containers/cfmm2tar_v1.0.0.sif is no longer available due to a certificate change in the DICOM server. For this step, it is therefore necessary to use the new version of cfmm2tar. Please follow the installation instructions at https://github.com/khanlab/cfmm2tar.

Note that pixi global install cfmm2tar -c khanlab could fail with the following error log:

No global environments found. WARN Couldn't install cfmm2tar 
× failed to solve the environment 
╰─▶ Cannot solve the request because of: cfmm2tar * cannot be installed because there are no viable options: 
└─ cfmm2tar v2.1.0 | 2.0.2 | 2.1.0 | 2.2.0 would require 
└─ python >=3.11, for which no candidates were found.

In this case, please try pixi global install -c conda-forge -c khanlab "python=3.11" cfmm2tar instead.

After cfmm2tar is installed, DICOM to Nifti conversion involves two steps (cfmm2tar and tar2bids). Both can be run from the terminal of the CBS server.

1) Log in into the CBS server. See this guide to access the CSB server for the first time.

2) Open the Terminal

3) Create a new temporary folder:

mkdir -p ~/tmp
export TMPDIR=~/tmp

This is strongly advised to avoid cfmm2tar from running out of space. Note that cfmm2tar deletes the folder when it is done importing the data from the server.

4) Create an output directory for cfmm2tar:

OUTPUT_DIR=/local/scratch/

5) Run cfmm2tar:

cfmm2tar -n 'PARTICIPANT-ID' --temp-dir ~/tmp  ${OUTPUT_DIR}

With the flag -n <PARTICIPANT-ID> you can input a specific dataset by using the “Patient’s Name” field in the CFMM DICOM Server. More cfmm2tar functionalities can be found with cfmm2tar --help or at https://github.com/khanlab/cfmm2tar.

cfmm2tar will create a .tar and a .uid file in the output directory.

Second step: tar2bids

After you have created the .tar and .uid file with cfmm2tar, the second step involves creating the BIDS repository with the .nii file. Run the following command in the Terminal:

singularity run /srv/containers/tar2bids_v0.2.4.sif -h <project-id>_heuristic.py -o <out_dir> <filename>.tar

<filename> is the full path of the .tar file created by cfmm2tar in the previous step. <out_dir> is the directory where tar2bids puts the Nifti files. One possibility is to set /local/scratch/BIDS as <out_dir>. <project-id>_heuristic.py is a Python script with the information needed to convert functional runs from DICOM to Nifti. Usually, <project-id>_heuristic.py is located in /cifs/diedrichsen/data/your_data_directory. If your project is new and you don’t have a heuristic file yet, see the next paragraph to learn how to create one.

Note that sometimes tar2bids may run out of memory. To avoid this, create a new temporary folder before running tar2bids:

mkdir -p ~/tmp
export TMPDIR=~/tmp

Heuristic files

This is an example code for a heuristic file:

import os
import numpy
from cfmm_base import infotodict as cfmminfodict
from cfmm_base import create_key


def infotodict(seqinfo):
    """Heuristic evaluator for determining which runs belong where
    allowed template fields - follow python string module:
    item: index within category
    subject: participant id
    seqitem: run number during scanning
    subindex: sub index within group
    """

    info = cfmminfodict(seqinfo)

    task = create_key('{bids_subject_session_dir}/func/{bids_subject_session_prefix}_task-task_run-{item:02d}_bold')
    task_sbref = create_key(
        '{bids_subject_session_dir}/func/{bids_subject_session_prefix}_task-task_run-{item:02d}_sbref')

    fmap_sbref = create_key('{bids_subject_session_dir}/fmap/{bids_subject_session_prefix}_dir-{dir}_epi')

    info[task] = []
    info[task_sbref] = []
    info[fmap_sbref] = []

    for idx, s in enumerate(seqinfo):

        if ('1.8iso_AP' in (s.series_description).strip()):
            if (s.dim4 == 1 and 'SBRef' in (s.series_description).strip()):
                info[task_sbref].append({'item': s.series_id})
            elif (s.dim4 > 200):
                info[task].append({'item': s.series_id})

        elif ('1.8iso_PA' in (s.series_description).strip()):
            if (s.dim4 == 1):
                # if 'SBRef' in (s.series_description).strip():
                info[fmap_sbref].append({'item': s.series_id, 'dir': 'PA'})

    return info

The variable s points to a spreadsheet that you can find in /local/scratch/BIDS/code/tar2bids_YYYY-MM-DD_XXhXXm_ZZZZ/heudiconv/PARTICIPANT-ID/info/dicominfo.tsv. To create the heuristic file for your project, you need to edit the example above by using the information contained in the spreadsheet. The spreadsheet contains a row for each .dcm file included in the dataset. Each column contains information about .dcm files some. For example, in the case of the project to which the above heuristics belong, the column “series_description” contains the substring “1.8iso_AP” in functional runs, in which the column “dim4” contains a number more than 200 (which corresponds to the number of volumes collected in each functional run). You can create the spreadsheet by running tar2bids without the -h flag on the first dataset you collect:

singularity run /srv/containers/tar2bids_v0.2.4.sif -o <out_dir> <filename>.tar

This will create the dicominfo.tsv spreadsheet inside <out_dir> in the path specified above.

Gradcorrect (highly recommended for 7T scans)

After you have a BIDS repository with the Nifti files, run the following command:

singularity run /srv/containers/khanlab_gradcorrect_v0.0.3a.sif <bids_dir> <out_dir> participant --grad_coeff_file 
/srv/software/gradcorrect/coeff_AC84.grad

<bids_dir> is the directory where your BIDS data that is stored. This directory contains the BIDS data before the gradient corrections. The participant argument must be passed as it is, it is NOT the PARTICIPANT-ID. As <out_dir> in gradcorrect you can put /local/scratch/BIDS_gradcorrect. Inside <out_dir> gradcorrect puts several files and folder, including a sub-XX folder with three folders inside: anat, func and fmaps. These three folders contains the Nifti files output by gradcorrect. <template_imana.m> from <spmj> needs these three folders to be into /project_directory/BIDS/sub-XX/.

Note that sometimes gradcorrect fails silently with the following output:

running participant level analysis
/local/scratch/BIDS/participants.tsv

In this case, check the participants.tsv file in the BIDS dir from tar2bids. It needs to contain a column named participant_id specifying sub-XX.

Method 2: automatic conversion through autobids (Currently unavailable)

Autobids is a pipeline maintained by the Khan lab that transforms the raw imaging data from DICOM (Properiatary Siemens format) to nifti files, renames them in BIDS standard, and applies gradient nonlinearity correction to the images as appropriate (gradcorrect).

Gradcorrect which calls gradunwarp, adapted from the workflow used in HCP data correction.

Steps for getting your data into BIDS format:

Study Registration:
- Register your study through the autobids new study form.
Autobids Autherization:
- Have your PI add ‘bidsdump’ to the list of users who can access the study’s data on the CFMM dicom server
Globus Setup:
- Sign up for a Globus account. You can use your UWO credentials to sign up.
- Install the Globus Connect Personal on your computer.
Data Downlaod:
- Once your study is registered with autobids and you collect new data, your study specific pipeline will be applied and the data will be available on Globus for you to download.
- Login to globus and navigate to the “COLLECTIONS” tab. You should see your BIDS converted data shared with you.
- “autobids_study_{name}type{type}” is the folder that contains the BIDS converted data.
- Collection type is provided in both “rawdata” (DICOM to Nii converted and renamed to BIDS format) or “derivedata” (DICOM to Nii converted, renamed to BIDS format and gradcorrect applied).