imagespace-transformations

studyforrest.org Dataset

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Template images and image space transformations

This repository contains data derived from the raw data releases of the studyforrest.org project. In particular these are:

• participant/scan-specific template images
• transformation between these respective image spaces

For more information about the project visit: http://studyforrest.org

File name conventions

Each directory in the subject directories and the "templates" directory corresponds to one image template. Templates in sub* directories are participant-specific (not aligned across participants). However, templates with the same name have corresponding input data. Templates in the templates directory have been derived from all participants, and there are typically transformation from participant specific templates into the group template space provided. Group template images carry a grp prefix in their label.

All transformations are the output if FSL tools: either MAT files with 4x4 affine transformation matrices from FLIRT, or FNIRT warp files.

Here is an example of how transformations can be located. The transformation of the template image created from all 3T BOLD images of participant 01 acquired in phase 2 of the project into the group template space for 3T BOLD scans can be found in:

sub-01/bold3Tp2/in_grpbold3Tp2/subj2tmpl_warp.nii.gz

Each template directory contains one or more image files with more-or-less self-explanatory names, such as "head", "brain", or "brain_mask". File with such a name in the one of the in_* folders represent the image in the parent folder, aligned and resliced to the target space for this transformation. These images can be used to inspect the quality of the transformation.

Lastly, the code/ directory contains the source code for computing template images and transformation between them.

How to obtain the dataset

This repository is a DataLad dataset. It provides fine-grained data access down to the level of individual files, and allows for tracking future updates. In order to use this repository for data retrieval, DataLad is required. It is a free and open source command line tool, available for all major operating systems, and builds up on Git and git-annex to allow sharing, synchronizing, and version controlling collections of large files. You can find information on how to install DataLad at handbook.datalad.org/en/latest/intro/installation.html.

Get the dataset

A DataLad dataset can be cloned by running

datalad clone <url>

Once a dataset is cloned, it is a light-weight directory on your local machine. At this point, it contains only small metadata and information on the identity of the files in the dataset, but not actual content of the (sometimes large) data files.

Retrieve dataset content

After cloning a dataset, you can retrieve file contents by running

datalad get <path/to/directory/or/file>

This command will trigger a download of the files, directories, or subdatasets you have specified.

DataLad datasets can contain other datasets, so called subdatasets. If you clone the top-level dataset, subdatasets do not yet contain metadata and information on the identity of files, but appear to be empty directories. In order to retrieve file availability metadata in subdatasets, run

datalad get -n <path/to/subdataset>

Afterwards, you can browse the retrieved metadata to find out about subdataset contents, and retrieve individual files with datalad get. If you use datalad get <path/to/subdataset>, all contents of the subdataset will be downloaded at once.

Stay up-to-date

DataLad datasets can be updated. The command datalad update will fetch updates and store them on a different branch (by default remotes/origin/master). Running

datalad update --merge

will pull available updates and integrate them in one go.

More information

More information on DataLad and how to use it can be found in the DataLad Handbook at handbook.datalad.org. The chapter "DataLad datasets" can help you to familiarize yourself with the concept of a dataset.