Creating Spherical ROIs in AFNI Using 3dUndump

Regions of interest; everybody wants them, but nobody knows how to get them. However, as Megatron once said, power flows to the one who knows how; desire alone is not enough.

Aware of this, I have created a script which will disenthrall you from the pit of ignorance and give you the power to create ROIs just about anywhere you please. The script uses AFNI's 3dUndump, which creates a spherical ROI of a given radius from which parameter values can be extracted using a tool like 3dmaskdump. The rationale is similar to creating ROIs using fslmaths or SPM's marsbar; and if you understand those, using 3dUndump is essentially the same thing.

The only caveat is that you must know the orientation of your dataset before using 3dUndump. AFNI defaults to RAI orientation, in which numbers increase from right to left, anterior to posterior, and inferior to superior; in other words, coordinates to the right of the origin will be negative (since numbers decrease going from left to right), and coordinates anterior to the origin will be negative (since numbers again decrease going from posterior to anterior). Always make sure to check the orientation using a command like 3dinfo -orient before creating your ROI, or open up your anatomical dataset in the AFNI viewer and navigate to the location that you want (e.g., right nucleus accumbens) and then write down the coordinates displayed in the upper left corner of the viewer. You can also use the option -orient LPI, if you're using coordinates from a paper.

This Python script that will let you input the coordinates, and then output a dataset ROI that can be overlaid on your anatomical image. The script can be found here.


Tutorial on 3dUndump:

Tutorial on MakeSpheres.py

Python SUITE: AFNI_Tools.gz

This is an update from my earlier post about the Python program convertDICOM2AFNI.py; now it is a folder containing a few programs that interact with each other. The rationale was to give the original dicom conversion script a wrapper that can change directory (since I am currently unable to do this from the python interpreter invoked by the Unix shell). All the user needs to do is download the file AFNI_Tools.gz into their home directory, unzip it using "tar -zxvf AFNI_Tools.gz" into your experimental directory, and run it using the command "tcsh convertRaw2AFNI.sh". The user will be prompted for a list of subject IDs, the name of the experimental directory, and will then run the script convertDICOM2AFNI.py inside the raw fMRI data folder for each subject. This in turn will prompt the user for study-specific info, such as number of slices, number of TRs, and so on. Currently, there is no way to account for runs with different numbers of TRs; I may add this function in the future.

A short screencast demo of the suite is available at www.screencast.com/users/Andrew.Jahn/folders/Jahn_Tools (you may need to go full-screen mode in order to see what I'm typing on the command line). What I did not mention in the demo is the subfolder "Paths" within the AFNI_Tools folder. It contains two text files, groupDir.txt and dataDir.txt. groupDir.txt contains the path to the experimental directory, while dataDir.txt contains the path to the output directory that you want for each subject; these should be modified when you download the suite. I've included some documentation that I hope is enough to get people started.

In addition, after converting the raw scanner files to both AFNI and NIFTI format, the program will drive AFNI directly from the command line and allow the user to interactively scroll through each functional dataset. For example, the first run of functional data will be displayed and a video of its timecourse will start playing; this is a good way to visually check for any scanner artifacts or excessive head motion, and deciding what to do with it. You can then hit enter in the terminal window that generated AFNI, and it will skip to the next functional run of data, and so on. The idea is to make it easy to do a first-pass analysis on your raw data and see whether anything is completely out of line.

As always, please send me feedback if you have actually used the program, and what you think about it.


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P.S. Photos from the AFNI bootcamp are now up. I now have photographic evidence that I was at the National Institutes of Health, and not somewhere else, like Aruba.

AFNI Bootcamp / Proof that I was not in Aruba

Python Script: convertDICOM2AFNI.py

I've written my first (useful) python script, a small program called convertDICOM2AFNI.py, that looks for DICOM files from our scanner and converts them to AFNI format. Converting raw scanner data to AFNI format using the to3d command is light years (i.e., minutes) faster than SPM's DICOM import, or MRIcron's dcm2nii program. dcm2nii, in particular, usually gave me a bunch of other files that I was never interested in and would convert everything in my raw DICOM folder, even though there were only a few runs that I was interested in looking at. It would also introduce some weird temporal interpolations into the data, modifying the header information to make it appear as though the volumes were slice-time corrected even though they actually were not.

In order to get around this problem, I may add an option to the script which converts to AFNI, and then converts to nifti format using 3dAFNI2NIFTI (although I have no idea what might get lost in the translation; will need to do some testing here).

To use the script, do the following:

1) Create folder containing all raw DICOM files generated from your experiment
2) Write down which session numbers correspond with which functional and anatomical runs (remember: pencil & paper are your friends)
3) Run the script from the raw DICOM directory using the command "python convertDICOM2AFNI.py"

It will ask you a series of questions about which session numbers are your functional runs, and then which session is your anatomical run. If you run the command without defaults (i.e., without using the "-d") flag, it will also ask you the number of slices in the z-direction, the number of TRs, and the length of each TR in milliseconds.

In the future, I plan to have the program automatically read in a defaults file that specifies all of these arguments automatically. However, that also assumes that the user is organized (which I am not). Also, as of this writing, it assumes that the z-slices alternate positively in the z-direction; if this is not true for you, you may need to change the script yourself.

The script is available for download over at my webpage (http://mypage.iu.edu/~ajahn) under "Python Scripts". The actual location of the scripts I write will probably change as I become more organized.


I plan to begin writing a suite of Python programs that I find useful for interacting with both SPM and AFNI data, eventually working up to a GUI sometime in the far future. For now, it will assume a degree of command line proficiency and that you can probably figure things out if the script does not perfectly fit your needs.


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Due to high demand from the Scottish population at IU, a screencast will soon be up detailing how to interpolate volumetric data onto a template surface.