LORIS-MRI comprises the core libraries for loading and inserting imaging data in LORIS. It is maintained in a separate repository so that it can be installed on the file server and separated from the web server. These documents assume you have some knowledge regarding LORIS and a functioning installation of the core LORIS codebase. For information regarding LORIS itself, please consult the LORIS wiki.
LORIS-MRI is a set of libraries, scripts, and settings responsible for the insertion, organization, and archiving of uploaded imaging datasets. It expects an uploaded, compressed file containing a DICOM scan session composed of many DICOM files. These DICOM files will be archived on the server and converted to MINC and (optionally) NIfTI files. Knowledge of these file formats can be helpful, but are not necessary for using or installing LORIS-MRI.
LORIS-MRI allows you to easily organize and archive your imaging datasets and links them with corresponding behavioral data in LORIS. Scans can be viewed and quality controlled in the LORIS front end via web browser, facilitating collaboration between radiologists, clinicians and researchers.
LORIS-MRI allows multiple ways to upload scans, but typically, users
upload a compressed (.tgz, .tar.gz, or .zip) DICOM folder via the Imaging
Uploader module that should be composed of only DICOM files. LORIS requires
that the uploaded file name follow the naming convention
PSCID_CANDID_VISIT-LABEL.
In addition, all DICOM datasets uploaded via the Imaging Uploader or
transferred on the LORIS-MRI server must be free of any identifying
information (e.g. patient name). A tool can be provided to the sites to
facilitate de-identification. Please contact the LORIS team for details.
The LORIS-MRI pipeline starts once the scans are uploaded to the server. The pipeline can start automatically if the autolaunch configuration is set, otherwise a back-end administrator can manually run the pipeline. These options and scripts are detailed in the Pipeline Triggering Options documentation.
Insertion progress can be tracked by the user through the Log Viewer in the Imaging Uploader module, where descriptive messages can be consulted. The output of the main key steps in the insertion progress can also be consulted through:
- the LORIS DICOM Archive module for successfully archived DICOM datasets
* - the Imaging Browser module for MINC files (generated from DICOM) that pass the
study-defined MRI protocol
* - BrainBrowser using 3D or 4D navigation of these MINC files. More details on BrainBrowser's capabilities can be found here.
* Please note that all acquisitions are included in the DICOM archival
step. However, specific acquisitions (such as localizers or scouts) can be
excluded from the steps of the pipeline that start at, and follow the DICOM to
MINC conversion by specifying them in the excluded_series_description
field of the Config module (under the Imaging Pipeline section). Note that what
the series descriptions entered in that Config field need to be an exact match
of the series description DICOM field.
The following BIDS datasets can be imported into LORIS using insertion scripts that
are gathered under the Python directory of the LORIS-MRI repository. It contains one
main script called bids_import.py that allow import of datasets that have been
organized in a BIDS structure (see BIDS specifications).
Those import scripts were written in Python in order to take advantage of the
already existing PyBIDS library that reads BIDS structures.
Currently, we support the insertion of:
- MRI datasets organized in a BIDS structure
- Electrophysiology datasets organized in a BIDS structure
Note: electrophysiology datasets are imported in LORIS in a specific set of tables
illustrated in the image below.
