Tutorial

BIDS introduction and BIDScoin demo

A good starting point to learn more about BIDS and BIDScoin is to watch this presentation from the OpenMR Benelux 2020 meeting (slides). The first 14 minutes Robert Oostenveld provides a general overview of the BIDS standard, after which Marcel Zwiers presents the design of BIDScoin and demonstrates hands-on how you can use it to convert a dataset to BIDS (the video is old but still somewhat useful).

BIDScoin tutorial

The tutorial below was written with the DCCN user in mind that wants to convert DICOM MRI data to BIDS. Nevertheless, the main principles also apply to other datasets, and you are encouraged to try out the assignments

1. Setting up the environment

Depending on how BIDScoin was installed, you may have to set your Python environment settings before you can run BIDScoin commands from your command-line interface/shell. This is already done for you when you run a BIDScoin play instance in the cloud. In the DCCN compute cluster example below it is assumed that an environment module is used to load your Linux Anaconda Python installation and that BIDScoin is installed in a conda environment named “bidscoin”. Run or adjust these commands to your computer system if needed:

$ module add bidscoin                # Load the DCCN bidscoin module with the PATH settings and Anaconda environment
$ source activate /opt/bidscoin      # Activate the Python virtual environment with the BIDScoin Python packages

Now you should be able to execute BIDScoin commands. Test this by running bidscoin to get a general workflow overview. Can you generate a list of all BIDScoin tools? What about the plugins? Test the bidscoin installation and make sure everything is OK

2. Data preparation

Create a tutorial playground folder by executing these shell commands:

$ bidscoin --download .              # Download the tutorial data (use a "." for the current folder or a pathname of choice to save it elsewhere)
$ cd ./bidscointutorial              # Go to the downloaded data (replace "." with the full pathname if your data was saved elsewhere)

The new bidscointutorial folder contains a raw source-data folder and a bids_ref reference BIDS folder, i.e. the intended end product of this tutorial. In the raw folder you will find these DICOM Series folders (aka “runs”):

bidscointutorial/raw/sub-001/ses-01/
├─ 001-localizer_32ch-head               A localizer scan that is not scientifically relevant and can be left out
├─ 002-AAHead_Scout_32ch-head            A localizer scan that is not scientifically relevant and can be left out
├─ 007-t1_mprage_sag_ipat2_1p0iso        An anatomical T1-weighted scan
├─ 047-cmrr_2p4iso_mb8_TR0700_SBRef      A single-band reference scan of the subsequent multi-band fMRI scan
├─ 048-cmrr_2p4iso_mb8_TR0700            A multi-band fMRI scan
├─ 049-field_map_2p4iso                  The field-map "magnitude1" images (intended for the previous fMRI scan)
├─ 050-field_map_2p4iso                  The field-map phase difference image
├─ 059-cmrr_2p5iso_mb3me3_TR1500_SBRef   A single-band reference scan of the subsequent multi-echo fMRI scan
├─ 060-cmrr_2p5iso_mb3me3_TR1500         A multi-band multi-echo fMRI scan
├─ 061-field_map_2p5iso                  Idem, the field-map "magnitude1" images (intended for the previous fMRI scan)
├─ 062-field_map_2p5iso                  Idem, the field-map phase difference image
└─ behavioural                           NeuroBS Presentation log files

Let’s begin with inspecting this new raw data collection:

Are the DICOM files for all the bids/sub-* folders organized in series-subfolders (e.g. sub-001/ses-01/003-T1MPRAGE/0001.dcm etc)? Use dicomsort if this is not the case (hint: it’s not the case). A help text for all BIDScoin tools is available by running the tool with the -h or --help flag (e.g. dicomsort -h)
Use the rawmapper command to print out the values of the “EchoTime”, “PatientSex” and “AcquisitionDate” DICOM fields (see rawmapper -h. Hint: use -f) of the CMRR fMRI series in the raw folder (hint: also use -w). You should find this result (NB: unfortunately in this tutorial sub-001 and sub-002 are identical phantoms):
```
subid    sesid   seriesname                        EchoTime  PatientSex  AcquisitionDate
sub-001  ses-01  047-cmrr_2p4iso_mb8_TR0700_SBRef  39        O           20200428
sub-002  ses-01  047-cmrr_2p4iso_mb8_TR0700_SBRef  39        O           20200428
```

3. BIDS mapping

Now we can make a study bidsmap, i.e. the mapping from DICOM source-files to BIDS target-files. To that end, scan all folders in the raw data collection by running the bidsmapper command:

$ bidsmapper raw bids

Mapping DICOM data

In the first tab of the bidseditor window that now opened, you see a participant table (top) and a samples table with a list of DICOM run-items being mapped to BIDS (bottom). Edit these tables as follows:

By default, the participant label is parsed from the filepath with a regular expression pattern that extracts the substring between /raw/sub- and the first / character. Change the pattern to extract the substring between /raw/s and the first / character. Can you understand why the subject label is now sub-ub001 instead of sub-001 (if not, ask it to your favorite AI-assistant)? Go back to the original settings by clicking the reset button.
We only have one session per subject, so in the main GUI that appears (when all raw data has been scanned), remove the session_id label. Note how the output names simplify, omitting the session subfolders and labels.
Edit the “anat” sample and change the datatype to extra_data. Hoover with your mouse over the orange filename to see what it means. No change the datatype to exclude the data to see what happens. Go back to the original settings by clicking the reset button. Now make the name of the T1 scan more user friendly, e.g. by naming the acquisition label simply acq-mprage. Click OK to approve your edits and to go back to the main window.
Next, edit the task and acquisition labels of the functional scans into something more readable, e.g. task-reward for the mb8 scans and task-stop for the mb3me3 scans. For the “reward” runs, add a tag of choice (e.g. “fmap1” or “fmap_reward”) to the B0FieldSource field in the meta table. Likewise, add another tag to the “stop” runs (e.g. “fmap2” or “fmap_stop”). You also don’t need the dir entity in the filenames, so remove these label values (and note how they disappear from the filename).
Make the field map scans more user friendly, e.g. by simplifying the acquisition labels to acq-2p4iso and acq-2p5iso. In both “2p4iso” fieldmap scans (magnitude and phasediff), add the same tag you used for the “reward” runs” to the B0FieldIdentifier field. If you like, you can also add a search pattern to the IntendedFor field such that it will select your reward runs (see the field map notes for more details). Do the same for the “2p5iso” fieldmap scans, using the tag for the “stop” runs.
Go back to the main window and check your edits by selecting all four “reward” func- and fmap-scans (use control-or shift-click). Click with the right mouse button on a selected scan and choose Compare from the context menu that popped up. Are all your tags consistent?
When all done, go to the Options tab and change the dcm2niix settings to get non-zipped NIfTI output data (i.e. *.nii instead of *.nii.gz, see “dcm2niix -h” for help). Test the tool to see if it can run and, as a final step, save your bidsmap and close the editor. You can always go back later to change any of your edits by running the bidseditor command-line tool directly. Try that.

Mapping Presentation log data

In the second tab of the bidseditor window, you see a similar participant table (top) and samples table with Presentation run-items. If you are not going to work with Presentation data, then you may skip the next paragraph.

4. BIDS coining

The next step—converting the source data into a BIDS collection—is straightforward and can be repeated whenever new data arrives. To do this, simply run the bidscoiner command-line tool:

$ bidscoiner raw bids

Check your bids/code/bidscoin/bidscoiner.log (the complete terminal output) and bids/code/bidscoin/bidscoiner.errors (the summary that is also printed at the end) files for any errors or warnings. You should not have any :-)
Compare the results in your bids/sub-* subject folders with the in bids_ref reference result. Are the file and folder names the same (don’t worry about missing individual echo images, they are combined/generated as described below)? Also check the json sidecar files of the field maps. Do they have the right EchoTime and B0FieldIdentifier/IntendedFor fields?
What happens if you re-run the bidscoiner command? Are the same subjects processed again? Delete the bids/sub-001 folder and re-run the bidscoiner command to recreate bids/sub-001.

5. Finishing up

Now that you have converted the data to BIDS, you still need to do work to make it fully ready for data analysis and sharing. For instance:

Combine the echos using the echocombine tool (see echocombine --help examples), such that the echo-combined image is saved in the same func folder. Open the .bidsignore file in the bids directory and add a func/*_echo-* line. The individual echos will now be ignored by BIDS-apps that use func data.
Deface the anatomical scans of sub-001 using the deface tool (see deface --help)). This will take a while, but will obviously not work well for our phantom dataset. Therefore store the ‘defaced’ output in the derivatives folder (instead of e.g. overwriting the existing images).
Generate a QC report of the anatomical scans using the slicereport tool (see slicereport -h) and open the bids/derivatives/slicereport/index.html file in your browser.
Inspect the bids/participants.tsv file and decide if it is OK.
Update the dataset_description.json and README files in your bids folder
As a final step, run the bids-validator on your bidscointutorial/bids folder. Are you completely ready now to share this dataset?