radifox 1.0.9

RADIFOX is the RADiological Image File Ontology eXtension, a Python package for the organization and management of medical images.

RADIFOX is an organization and management system for medical images. There are multiple components under the RADIFOX umbrella:

• A detailed, type-based naming system for medical images (including a Python API)
• An organizational system flexible enough for a multitude of study designs
• A conversion system to convert from DICOM to NIfTI using DCM2NIIX
• A auto-provenance system to track the provenance of processing results
• A web-based quality assurance system

RADIFOX is designed to be flexible and extensible.

Table of Contents

• Overview
• Installation
• Basic Usage
  • CLI Scripts
    • radifox-convert
    • radifox-update
    • radifox-qa
  • Python API
    • ImageFile
    • ImageFilter
    • ProcessingModule
• RADIFOX Components
  • File Organization
  • Naming
  • Conversion
    • Look-up Tables
    • Manual Naming
    • JSON Sidecar Files
  • Provenance
  • Quality Assurance
• Additional Information
  • Advanced CLI Usage
    • radifox-convert
    • radifox-update
    • radifox-qa
  • JSON Sidecar Format
  • Container Creation

Overview

The core of the RADIFOX system is the naming and organization system. This system is designed to be flexible, but also can be opinionated. The directory organization can be simplified to:

<output-root>/<project-id>/<subject-id>/<session-id>/...

The naming system is a detailed, type-based naming system optimized for medical images. It can be simplified to:

<subject-id>_<session-id>_<image-id>_<image-type>.ext

The image type can be futher broken down into a number of components:

<bodypart>-<modality>-<technique>-<acqdim>-<orientation>-<excontrast>[-<extras>]

This organzation allows for the implementation of features such as auto-provenance.

Installation

RADIFOX is available on PyPI and can be installed with pip:

pip install radifox

This base install will cover the core functionality of RADIFOX. However, to run conversions, you will need the dcm2niix tool installed on your system (and included in your PATH).

To include the dependencies for the web-based quality assurance system, install with the qa extra:

pip install radifox[qa]

Basic Usage

CLI Scripts

The radifox package includes a number of CLI scripts to access various components of RADIFOX. These scripts are installed to your PATH when you install the radifox package. For a full listing of command line options, see Advanced CLI Usage.

radifox-convert

The radifox-convert script is used to convert DICOM files to NIfTI files using the dcm2niix tool. It is a wrapper around dcm2niix that uses the RADIFOX naming system to organize the output files.

Example Usage:

radifox-convert \
    --output-root /path/to/output \
    --project-id study \
    --subject-id 123456 \
    --session-id 1 \
    /path/to/dicom_files

This will copy the files in the direction /path/to/dicom_files to the output directory /path/to/output/study/123456/STUDY-1/dcm, organize them and convert them to NIfTI. The NIfTI files (and their JSON sidecar files) will be placed in /path/to/output/study/STUDY-123456/1/nii.

radifox-update

The radifox-update script is used to update naming for a directory of images. This is commonly done after an update to RADIFOX to ensure that all images are named according to the latest version of the naming system. It also could be done to incorporate a new look-up table or manual naming entries after QA.

Example Usage:

radifox-update --directory /path/to/output/study/STUDY-123456/1

This will update the naming for all images in the existing RADIFOX session directory /path/to/output/study/STUDY-123456/1. If the RADIFOX version, look-up table, or manual naming entries have changed, the images will be renamed to reflect the new information. If none of these have changed, the update will be skipped.

radifox-qa

The radifox-qa script is used to run the web-based quality assurance system.

Example Usage:

radifox-qa --port 8888 --root-directory /path/to/output

This will launch the QA webapp on port 8888, pointing to /path/to/output. The QA webapp will be accessible at http://localhost:8888 and will show projects in /path/to/output. Be sure to note the secret key printed to the terminal when the app starts. You will need this to log into the webapp. The secret key changes each time the app is launched.

You can specify your own secret key using the --secret-key option.

radifox-qa --port 8888 --root-directory /path/to/output --secret-key my-secret-key

'radifox-stage'

"Staging" is the process of filtering images for processing. radifox-stage is a processing module that is uses ImageFilters to accomplish this. radifox-stage looks over an entire subject and filters images based on provided --image-types. By default, all images matching the filter will be staged for processing. To keep only the best resolution images for each filter, use the --keep-best-res option. Additionally, it can generate registration targets based on provided --reg-filters. Plugins derived from the StagingPlugin abstract class can be used to add additional functionality to radifox-stage. Two default plugins MEMPRAGEPlugin and MP2RAGEPlugin are included with RADIFOX. These can be skipped by providing the --skip-default-plugins option. Staged results have the sform and qform matrices set to be equal by default. To skip this, use the --skip-set-sform option.

A good default call of radifox-stage might be:

radifox-stage \
    --keep-best-res \
    --subject-dir /path/to/output/study/STUDY-123456 \
    --image-types \
        'bodypart=BRAIN;modality=T1;excontrast=PRE' \
        'bodypart=BRAIN;modality=T1;excontrast=POST' \
        'bodypart=BRAIN;modality=T2' \
        'bodypart=BRAIN;modality=PD' \
        'bodypart=BRAIN;modality=FLAIR' \
    --reg-filters \
        'bodypart=BRAIN;modality=T1;acqdim=3D;excontrast=PRE' \
        'bodypart=BRAIN;modality=T1;acqdim=3D;excontrast=POST' \
        'bodypart=BRAIN;acqdim=3D' \
        'bodypart=BRAIN;acqdim=2D'

Python API

The radifox package also includes a Python API for accessing additional components.

ImageFile

The ImageFile class is used to represent a single image file, including its name and metadata. It is a wrapper around a lot of pathlib.Path functions, so it can be used in place of a Path object in many cases. It additionally defines a number of properties to access naming breakdowns and metadata.

Example Usage:

from radifox.naming import ImageFile
img = ImageFile('/path/to/output/study/STUDY-123456/1/nii/STUDY-123456_01-03_BRAIN-T1-IRFSPGR-3D-SAGITTAL-PRE.nii.gz')
print(img.body_part) # prints 'BRAIN'
print(img.modality) # prints 'T1'
print(img.parent) # prints Path object for '/path/to/output/study/STUDY-123456/1/nii'
print(img.name) # prints 'STUDY-123456_01-03_BRAIN-T1-IRFSPGR-3D-SAGITTAL-PRE.nii.gz'
print(img.info.series_description) # prints 'IRFSPGR 3D SAGITTAL PRE'

Multiple pathlib.Path functions are available directly (like Path.name) and others are available through the path property (like Path.iterdir). These functions will return Path objects, not ImageFile objects.

print(img.path) # prints Path object for '/path/to/output/study/STUDY-123456/1/nii/STUDY-123456_01-03_BRAIN-T1-IRFSPGR-3D-SAGITTAL-PRE.nii.gz'

ImageFilter

The ImageFilter class is used to represent a filter for images based on naming. It is a wrapper around a dict that defines a set of key-value pairs that must be present in the image name. It can be defined as keyword arguments in the class constructer or by passing a formatted string to ImageFile.from_string.

Example Usage:

from radifox.naming import ImageFilter

imgs = [
    ImageFile('/path/to/output/study/STUDY-123456/1/nii/STUDY-123456_01-03_BRAIN-T1-IRFSPGR-3D-SAGITTAL-PRE.nii.gz'),
    ImageFile('/path/to/output/study/STUDY-123456/1/nii/STUDY-123456_01-04_BRAIN-T2-FSE-2D-AXIAL-POST.nii.gz'),
]

filt = ImageFilter(body_part='BRAIN', modality='T1')
print(filt) # prints "body_part=BRAIN,modality=T1"
print(filt.filter(imgs)) # prints ['/path/to/output/study/STUDY-123456/1/nii/STUDY-123456_01-03_BRAIN-T1-IRFSPGR-3D-SAGITTAL-PRE.nii.gz']

filt = ImageFilter.from_string('body_part=BRAIN,modality=T2')
print(filt) # prints "body_part=BRAIN,modality=T2"
print(filt.filter(imgs)) # prints ['/path/to/output/study/STUDY-123456/1/nii/STUDY-123456_01-04_BRAIN-T2-FSE-2D-AXIAL-POST.nii.gz']

ProcessingModule

The ProcessingModule class is used to represent a processing module for use in the auto-provenance system. Module code should inherit from this class and implement the cli and run methods, as well as define the name and version class attributes. The cli method should take either a list of options/arguments or None to pull from sys.argv. It should return a dict of keywards and arguments to pass directly to the run method. The run method should take a dict of keywords and arguments and return a dict of results.

Example Usage:

import argparse
import logging
from pathlib import Path

import nibabel as nib
from radifox.records import ProcessingModule

class MyModule(ProcessingModule):
    name = "my-module"
    version = "1.0.0"

    @staticmethod
    def cli(args=None):
        parser = argparse.ArgumentParser()
        parser.add_argument("--input", type=Path, required=True)
        parser.add_argument("--mult-factor", type=float, required=True)
        parsed = parser.parse_args(args)
        
        return {
            "input": parsed.input,
            "mult_factor": parsed.mult_factor,
        }

    @staticmethod
    def run(in_file: Path, mult_factor: float):
        out_stem = in_file.name.split(".")[0]
        out_dir = in_file.parent.parent / "proc"
        out_dir.mkdir(exist_ok=True, parents=True)
        
        logging.info(f"Multiplying {in_file} by {mult_factor}.")
        obj = nib.Nifti1Image.load(in_file)
        data = obj.get_fdata()
        new_obj = nib.Nifti1Image(data * mult_factor, obj.affine, obj.header)
        new_obj.to_filename(out_dir/ f"{out_stem}_mult-{mult_factor}.nii.gz")
        return {
            'output': out_dir / f"{out_stem}_mult-{mult_factor}.nii.gz"
        }

A ProcessingModule subclass can then be run as MyModule() or MyModule(args) (where args is as list of strings for argparse to parse). This can be used to make a processing script by adding:

if __name__ == "__main__":
    MyModule()

to the end of the file.

StagingPlugin

The StagingPlugin class is used to represent a plugin for use in the radifox-stage module. Plugins should inherit from this class and implement the filter and run methods. The filter method should take a list of ImageFile objects and return a list of ImageFile objects. The most common way to achieve this would be to define an ImageFilter and use the filter method of that class. The run method should take a list of ImageFile objects and return a list of ImageFile objects. This method should perform the actual processing of the images.

Below is an example that calculates the sum of a list of multi-echo images of an MEMPRAGE acquisition.

import nibabel as nib
import numpy as np

from radifox.naming import ImageFile, ImageFilter
from radifox.modules import StagingPlugin

class MEMPRAGEPlugin(StagingPlugin):
    @staticmethod
    def filter(images: list[ImageFile]) -> list[ImageFile]:
        return ImageFilter(
            modality="T1",
            technique="IRFSPGR",
            extras=lambda x: any("ECHO" in s or s == "SUM" for s in x),
        ).filter(images)

    @staticmethod
    def run(images: list[ImageFile]) -> list[ImageFile]:
        out_imgs = []
        for img_set in MEMPRAGEPlugin.sort_by_series(images):
            # Choose a SUM image if both echoes and SUM are available
            sum_imgs = [img for img in img_set if "SUM" in img.extras]
            if sum_imgs:
                out_imgs.append(sum_imgs[0])
            else:
                out_imgs.append(MEMPRAGEPlugin.sum_memprage(img_set))
        return out_imgs

    @staticmethod
    def sum_memprage(imgs: list[ImageFile]) -> ImageFile:
        """Create a sum image from a list of MEMPRAGE echo images."""
        temp_img = sorted(imgs, key=lambda x: x.name)[0]
        out_fpath = temp_img.path.parent.parent / "stage" / f"{temp_img.stem}_sum.nii.gz"
        obj = nib.load(temp_img.path)
        sum_data = np.sum(
            [nib.Nifti1Image.load(img.path).get_fdata(dtype=np.float32) for img in imgs], axis=0
        )
        nib.Nifti1Image(sum_data, None, obj.header).to_filename(out_fpath)
        return ImageFile(out_fpath)

RADIFOX Components

RADIFOX is a collection of components that work together to provide a comprehensive system for managing medical images.

File Organization

The file organization structure is multi-level allowing for multiple projects to be stored together while being easily separated. The directory structure is as follows:

<root-directory>
└── <project-id>
    └── <subject-id>
        └── <session-id>

This is easily extensible to include multiple sessions per subject, multiple subjects per project, and multiple projects per root directory. The project-id, subject-id, and session-id are all user-defined and can be any string.

For example:

/path/to/output
└── study
    └── STUDY-123456
        └── 1
        └── 2
    └── STUDY-789012
        └── 1
        └── 2

Note: The subject-id is prefixed with the project-id to ensure that the subject-id is unique across projects.

Within each session directory, there are a number of subdirectories that are the same for every session:

...
└── <session-id>
    └── dcm
    └── nii
    └── logs
    └── qa

The dcm directory is where the original DICOM files are stored. The nii directory is where the converted NIfTI files (and JSON sidecars) are stored. The logs directory is where the logs from processing are stored. The qa directory is where the images for QA are stored.

In addition to these directories, there are a few files that stored in the session directory. The <subject-id>_<session-id>_UnconvertedInfo.json file is a JSON file that contains information from DICOM files that were skipped during conversion. The <subject-id>_<session-id>_ManualNaming.json file is a JSON file that contains manual naming entries for images in the session. The <subject-id>_<session-id>_Provenance.txt file is a text file that contains the provenance of the processing steps for the session.

After processing starts, a few other directories will be added to the session directory:

...
└── <session-id>
    └── proc
    └── stage
    └── tmp

The proc directory is where the processed images and fiels are stored. The stage directory is where the filtered images are placed prior to processing. The tmp directory is where intermediate files are stored during processing.

Naming

The RADIFOX naming system is a detailed, type-based naming system for medical images. It is currently focused on MRI images, but it is expected to extend to other modalities. There are six main components to the naming system:

• bodypart: The body part being imaged (e.g. BRAIN, CSPINE, etc.)
• modality: The imaging modality (e.g. T1, T2, etc.)
• technique: The imaging technique (e.g. IRFSPGR, FSE, etc.)
• acqdim: The acquisition dimension (2D or 3D)
• orientation: The imaging plane (AXIAL, SAGITTAL, CORONAL)
• excontrast: The exogenous contrast (PRE, POST, etc.)

An image filename is then constructed by combining these components with hyphens.

<subject-id>_<session-id>_<image-id>_<bodypart>-<modality>-<technique>-<acqdim>-<orientation>-<excontrast>.nii.gz

The image-id is a unique identifier for the image within the session, it is created from a study number (in case multiple imaging studies are in the same session) and an image number (in each study).

Additionally, image names can have extras appended to the end of the core name. These are additional descriptors that are not part of the core naming system, but are useful for identifying images. extras are connected to the main name with a hyphen (and multiple extras are separated by hyphens). Common uses for extras are echo numbers (e.g. ECHO1, ECHO2, etc.) in multi-echo sequences and complex image components (like MAG and PHA) in complex images. However, this can be used for any additional descriptor of the acquired image that may help route it through processing.

For example:

STUDY-123456_01-03_BRAIN-T2-FSE-2D-AXIAL-PRE-ECHO1.nii.gz
STUDY-123456_01-03_BRAIN-T2-FSE-2D-AXIAL-PRE-ECHO2.nii.gz

Processed images also have tags appended to the end of the name. This is to indicate the processing steps that were applied to the image. These tags are separated from the main name with an underscore (and multiple tags are separated by underscores). In general, new tags are appended to existing tags (so the order of tags is important). This is to ensure that the processing history of the image is preserved in the filename.

For example:

STUDY-123456_01-03_BRAIN-T2-FSE-2D-AXIAL-PRE-ECHO1_n4.nii.gz

Conversion

The conversion system is a wrapper around the dcm2niix tool. It uses the RADIFOX naming system to organize the output files. radifox-convert is the core command for this function.

The conversion process is as follows:

1. Copy the DICOM files to the dcm directory in the session directory.
2. Sort the DICOM files into series directories in the dcm directory and remove any duplicates.
3. Check for series that should be skipped (scouts, localizers, derived images, etc.).
4. Generate image names automatically from the DICOM metadata, look-up tables, and manual naming entries.
5. Convert the DICOM files to NIfTI using dcm2niix and rename to RADIFOX naming.
6. Create the JSON sidecar files for the NIfTI files (contains some DICOM metadata).
7. Create QA images for the converted NIfTI files.

Look-up Tables

The look-up tables are a set of rules for automatically naming images based on the DICOM SeriesDescription tag. They are stored in a comma-separated values (CSV) file in each project folder. They have a specific name format: <project-id>_lut.csv. If no look-up table is found for a project, a blank look-up table is written. Look-up table values take precidence over automatic naming, but are overwritten by manual names.

The look-up table file has five total columns: Project, Site, InstitutionName, SeriesDescription, and OutputFilename.

The first three columns (Project, Site, and InstitutionName) narrow down which images are affected. These columns match the project and site IDs and the DICOM InstitutionName tag. This means that if a particular site or even scanning center uses a specific SeriesDescription, it can be handled differently than others. The Site and InstitutionName columns are optional and can be None.

The SeriesDescription column is a string and must exactly match the DICOM SeriesDescription tag. This may mean that multiple rows are needed to cover all possible values of the SeriesDescription tag for a particular name.

The OutputFilename column is where the RADIFOX naming is specified. You do not have to specify all components of the name, only the ones that need to be changed. For example, if you only want to change the bodypart to BRAIN for a specific SeriesDescription, you can specify BRAIN in the OutputFilename column. However, you must specify all components that come prior to the one you want to change as None. For example, to change the modality to T1 for a specific SeriesDescription, you must specify None-T1 in the OutputFilename column. This can also be used to change the extras, by specifying them at the end of the OutputFilename column. For example, to add ECHO1 to the end of the name for a specific SeriesDescription, but change nothing else, you must specify None-None-None-None-None-None-ECHO1 in the OutputFilename column.

Manual Naming

Manual naming entries are the most specific way to name images. They are stored as a JSON file in each session directory (<subject-id>_<session-id>_ManualNaming.json). This JSON file is a dictionary with the DICOM series directory path (dcm/...) as the key and the new name as the value. This series path can be found as the SourcePath in the sidecar JSON file for the image. Manual naming entries take precidence over look-up tables and automatic naming. The naming convention for manual naming entries is the same as for look-up tables.

The simplest way to create manual naming entries is to use the radifox-qa webapp.

JSON Sidecar Files

JSON sidecar files are created for each NIfTI file during conversion. They contain information about the conversion process (versions, look-up table values, manual naming, etc.) as well as critical DICOM metadata. The JSON sidecar files are stored in the nii directory in eact session directory next to their corresponding NIfTI file.

Sidecar files are human-readable, but can also be accessed in Python using the json standard package. Most of the crutial information will be in the SeriesInfo key of the sidecar file.

import json

obj = json.load(open('/path/to/output/study/STUDY-123456/1/nii/STUDY-123456_01-03_BRAIN-T1-IRFSPGR-3D-SAGITTAL-PRE.json'))
print(obj['SeriesInfo']['SeriesDescription']) # prints 'IRFSPGR 3D SAGITTAL PRE'
print(obj('SeriesInfo')['SliceThickness']) # prints 1.0

A complete record of the sidecar JSON format is below JSON Sidecar Format.

Provenance

The auto-provenance system is a system for tracking the provenance of processing results. It allows developers to easily include RADIFOX management features into their processing scripts in a consistent way. This includes automatic generation of provenance records, automatic logging during execution and automatic generation of QA images from outputs.

The auto-provenance system is based on the ProcessingModule class. This is an abstract class that defines the basic structure of a processing module. Developers should inherit from this class and implement the cli and run methods, as well as define the name and version class attributes. See ProcessingModule for more details.

Provenance Records

Provenance from this system is stored in two different ways. The first is at the session level in the <subject-id>_<session-id>_Provenance.yml file. This is an append-only text file that contains the provenance records of all processing steps for the session. The second is a provenance text file (.prov) that is stored with each processed file. This contains the provenance record for the process that created the processed file only.

Provenance records are stored in the YAML format that is human-readable, but also easily parsed by Python. The format is as follows:

---
Id: <record-id>
Module: <module-name>:<module-version>
Container: 
  url: <container-url>:<container-tag>@<container-commit>
  hash: <container-hash>
  builder: <container-builder>
  timestamp: <container-timestamp>
User: <user-name>@<hostname>
StartTime: <start-timestamp>
Duration: <duration-days-hours-minutes-seconds>
Inputs:
  <input-key-1>: <input-filename-1>:<input-hash-1>
  <input-key-2>: 
    - <input-filename-2>:<input-hash-2>
    - <input-filename-3>:<input-hash-3>
Outputs:
  <output-key-1>: <output-filename-1>:<output-hash-1>
  <output-key-2>: 
    - <output-filename-2>:<output-hash-2>
    - <output-filename-3>:<output-hash-3>
Parameters:
  <parameter-key-1>: <parameter-value-1>
  <parameter-key-2>: <parameter-value-2>
Command: <command-string>
...

The <record-id> is a unique identifier for the record created from a hash of the rest of record. The <module-name> and <module-version> are the name and version of the processing module that created the record (defined in ProcessingModule subclass). The <container-url>, <container-tag>, <container-commit> and <container-hash> values are the URL, tag, commit, and hash of the container used to run the processing module. The <container-timestamp>, <container-builder> values are the timestamp and builder identity of the container used to run the processing module. These are derived from specific labels set during container creation. For more information on how compatible containers are created, see Container Creation. The <user-name> and <timestamp> are the user name of the user that ran the processing module and the timestamp of the processing module run completion. The <input-key>s, <input-filename>s, and <input-hash>s are the input names, filenames, and hashes of the input files to the processing module. Outputs are structured the same way. The <parameter-key>s and <parameter-value>s are the key-value pairs of the parameters passed to the processing module (that are not files). The <command-string> is the exact command string that was used to run the processing module.

Automatic Logging

The auto-provenance system also includes automatic logging during execution. This is done by setting up a logging handler that writes to the logs directory in the session directory. This handler is set up by default to log all messages to the logs/<module-name>/<first-input-filename>-<timestamp>-info.log file. This can be adjusted to logs/<module-name>-<timestamp>-info.log by setting log_uses_filename to False in the ProcessingModule subclass. Currently, there is support for INFO, WARNING and ERROR level messages. They can be accessed at any point in the run method by calling logging.info(message) (or warning or error). You must import logging at the top of the file to use this feature. If there are warnings or errors produced during execution, they will be written to additional log files (-warning.log and -error.log) for easy viewing. There is currently no support for DEBUG level messages, but that is planned for the future.

Automatic QA Images

The auto-provenance system also includes automatic generation of QA images from outputs. Any output that is returned from the run method will have a QA image generated automatically, if it is a NIfTI file (ends in .nii.gz).

Quality Assurance

The web-based quality assurance system is a system for viewing images and recording QA results. It is a Flask-based webapp that can be run locally. There are two modes: conversion and processing that can be switched between using the links in the top navigation bar.

The conversion mode is used to view and make corrections to the naming of images after conversion. There are three types of actions that can be taken in conversion mode.

• Ignore Button: This will mark the image to be skipped by the conversion process on update.
• Body Type Buttons: This will change the bodypart of the image to the selected value. It is currently available for BRAIN, CSPINE, TSPINE, LSPINE, and ORBITS.
• Correct Name Button: This will open a form to correct any of the core aspects of the RADIFOX naming convention. extras are not yet supported.

The processing mode is used to view outputs of various processing steps. For each processing step, images of the outputs are shown with the provenance record for that step. No actions are currently availabe in processing mode, but we hope to record QA results directly from the app.

The QA webapp is launched with the radifox-qa command. It is a webapp that runs locally on port 5000 by default. Be sure to copy down the Secret Key that is printed to the console when the webapp is launched. This will be required to log into the webapp and changes each time the app is launched. It can also be specified using the --secret-key option. For convenience, you can log into the app using http://{HOST}:{PORT}/login?key={SECRET_KEY}, which is printed when the app is launched. It can also be accessed at http://{HOST}:{PORT} (http://localhost:5000 by default) and the key can be entered there. See radifox-qa above for more details.

Additional Information

Advanced CLI Usage

radifox-convert

Option	Description	Default
source	The source directory (or zip file) containing the DICOM files.	required
-o, --output-root	The root directory for the output files (contains project directories).	required
-p, --project-id	The project ID for the converted session.	required
-s, --subject-id	The subject ID for the converted session.	required
-e, --session-id	The session ID for the converted session.	required
-l, --lut-file	The look-up table file to use for naming.	<output-root>/<project-id>/<project-id>_lut.csv
--site-id	The site ID for the converted session.	None
--force	Force conversion even if session directory already exists.	False
--reckless	Skip consistency checks when forcing run (will overwrite files!)	False
--safe	If the session directory already exists, use a new directory with -# appended (does not change session ID or filenames)	False
--no-project-subdir	Do not create a project subdirectory in the output root directory. Subjects will be placed directly into the --output-root directory	False
--symlink	Create symlinks to the original DICOM files instead of copying them.	False
--hardlink	Create hardlinks to the original DICOM files instead of copying them.	False
--verbose	Log debug output.	False
--version	Output RADIFOX version and exit.	False
--help	Show help message and exit.	False
--parrec	Convert PAR/REC files instead of DICOM files.	False
--institution-name	The institution name to use for the session (required for PAR/REC conversion).	None
--magnetic-field-strength	The magnetic field strength to use for the session (required for PAR/REC conversion).	None
--anonymize	Experimental anonymization support (will remove copied DICOM files).	False
--date-shift-days	The number of days to shift the date by during anonymization.	None
--tms-metafile	The TMS metafile to use for subject, site and session ID.	None

radifox-update

Option	Description	Default
directory	The converted RADIFOX directory to update.	required
-l, --lut-file	The look-up table file to use for naming.	<directory>/../<project-id>_lut.csv
--force	Force conversion even if session directory already exists.	False
--verbose	Log debug output.	False
--version	Output RADIFOX version and exit.	False
--help	Show help message and exit.	False

radifox-qa

Option	Description	Default
--port	The port to run the QA webapp on.	5000
--host	The host bind address for the QA webapp.	localhost
--root-directory	The output root to read projects from (contains project directories)	/data
--secret-key	The secret key to use for the QA webapp.	None
--workers	Number of workers to use for web server.	1

radifox-stage

Option	Description	Default
--subject-dir	The path to the subject directory to stage.	required
--image-types	A set of ImageFilter strings used to filter the images for staging	required
--reg-filters	A set of ImageFilter strings used for determining registration targets.	None
--keep-best-res	Only keep the highest resolution image for each filter.	False
--plugin-paths	A list of additional plugin paths to add.	None
--skip-default-plugins	Skip the default plugins included with staging.	False
--skip-set-sform	Skip setting the sform matrix for staged images.	False

JSON Sidecar Format

The JSON sidecar format is a dictionary with 8 top-level keys:

• __version__: A dictionary of software versions used in conversion (radifox and dcm2niix)
• InputHash: A hash of the input directory or archive file used in conversion
• LookupTable: A dictionary of look-up table values used in conversion (limited by project/site ID/institution, if applicable)
• ManualNames: A dictionary of manual name entries used in conversion
• Metadata: A dictionary of session level metadata items (Project ID, Subject ID, Session ID, etc.)
• RemoveIdentifiers: A boolean indicating if identifiers were removed from the converted files
• SeriesInfo: A dictionary of DICOM metadata and conversion information for each converted image

The SeriesInfo value has most of the information about the converted image, including converted DICOM tags.

• AcqDateTime: The acquisition date and time of the image
• AcquiredResolution: The acquired, in-plane resolution of the image (list of 2 floats)
• AcquisitionDimension: The number of acquisition dimensions (2D or 3D)
• AcquisitionMatrix: The acquired in-plane matrix size of the image (list of 2 ints)
• BodyPartExamined: The body part examined in the image
• ComplexImageComponent: The complex number component represented in the image (MAGNITUDE, PHASE, REAL, IMAGINARY)
• ConvertImage: Boolean indicating if the image was supposed to be converted
• DeviceIdentifier: An identifier for the device used to acquire the image
• EPIFactor: The echo planar imaging (EPI) factor of the image
• EchoTime: The echo time (in ms) of the image
• EchoTrainLength: The echo train length of the image
• ExContrastAgent: Any information about the exogenous contrast agent used in the acquisition
• FieldOfView: The field of view (in mm) of the image (list of 2 floats)
• FlipAngle: The flip angle (in degrees) of the image
• ImageOrientationPatient: The DICOM image orientation patient tag of the image (list of 6 floats)
• ImagePositionPatient: The DICOM image position patient tag of the image (list of 3 floats)
• ImageType: The DICOM image type tag of the image (list of strings)
• InstitutionName: The institution name of the device used to acquire the image
• InversionTime: The inversion time (in ms) of the image
• LookupName: Any naming components for this image pulled from the lookup-table (list of strings)
• MagneticFieldStrength: The magnetic field strength (in T) of the image
• ManualName: Any naming components for this image pulled from the manual naming entries (list of strings)
• Manufacturer: The manufacturer of the device used to acquire the image
• MultiFrame: Boolean indicating if the image is a multi-frame DICOM image
• NiftiCreated: Boolean indicating if the image was successfully converted to NIfTI
• NiftiHash: The hash of the converted NIfTI file
• NiftiName: The final filename for the converted NIfTI file.
• NumFiles: Number of files (or frames) incorporated into the image (number of slices).
• NumberOfAverages: The number of averages used in the acquisition
• PercentSampling: The percent of k-space sampling used in the acquisition
• PixelBandwidth: The pixel bandwidth (in Hz) of the image
• PredictedName: Automatically generated name prediction from the DICOM metadata (list of strings)
• ReceiveCoilName: The name of the receive coil used in the acquisition
• ReconMatrix: The reconstructed in-plane matrix size of the image (list of 2 ints)
• ReconResolution: The reconstructed, in-plane resolution of the image (list of 2 floats)
• RepetitionTime: The repetition time (in ms) of the image
• ScanOptions: Any scan options used in the acquisition
• ScannerModelName: The model name of the scanner used to acquire the image
• SequenceName: The name of the sequence used to acquire the image
• SequenceType: The type of sequence used to acquire the image
• SequenceVariant: The variant of the sequence used to acquire the image
• SeriesDescription: The DICOM series description tag of the image
• SeriesNumber: The DICOM series number tag of the image
• SeriesUID: The DICOM series UID tag for the image
• SliceOrientation: The slice orientation of the image (axial, sagittal, or coronal)
• SliceSpacing: The slice spacing (in mm) between slices of the image
• SliceThickness: The slice thickness (in mm) of the image
• SoftwareVersions: The software versions of the device that acquired the image
• SourceHash: The hash of the source DICOM files
• SourcePath: The path to the source DICOM files (relative to session directory, e.g dcm/...)
• StudyDescription: The study description DICOM tag for the image
• StudyUID: The study UID DICOM tag for the image
• TriggerTime: The trigger time (in ms) of the image (can be used to store inversion time)
• VariableFlipAngle: Boolean indicating if the image used variable flip angles

Container Creation

For reproducibility, processing must be done in a container. This can be Docker or Apptainer/Singularity, but requires a few specific labels to be set to maintain strict accounting of the container used.

The labels are:

• ci.timestamp: Timestamp of the container image creation (%Y-%m-%dT%H:%M:%SZ)
• ci.builder: The username of the builder of the container image (who initiated the build)
• ci.image: URL of the container image in a repository (e.g. Docker Hub)
• ci.tag: Version tag of the container image
• ci.commit: Commit hash of the Dockerfile/repo used to build the container image
• ci.digest: Digest hash of the container image

These labels are most easily set by using Continuous Integration (CI) to create your images. This is an example .gitlab-ci.yml to achieve this on GitLab:

variables:
  GIT_STRATEGY: clone
  GIT_DEPTH: 0

build:
  image: docker:20.10.16
  stage: build
  services:
    - docker:20.10.16-dind
  variables:
    TAG: $CI_REGISTRY_IMAGE:$CI_COMMIT_REF_NAME
  script:
    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
    - docker build 
      --label ci.timestamp=$(date -u +'%Y-%m-%dT%H:%M:%SZ')
      --label ci.builder=$GITLAB_USER_LOGIN
      --label ci.image=$CI_REGISTRY_IMAGE
      --label ci.tag=$CI_COMMIT_REF_NAME
      --label ci.commit=$CI_COMMIT_SHA 
      -t $TAG .
    - DIGEST=$(docker inspect --format='{{index .Id}}' $TAG)
    - echo "FROM $TAG" | docker buildx build --label ci.digest=$DIGEST -t $TAG --push -
  only:
    - tags

Using a GitHub action is similar and can be done with GitHub Actions:

name: Publish Docker Image to GHCR

on:
  push:
    branches:
      - 'main'
    tags:
      - '*'

jobs:
  docker:
    name: Build and Push Docker Image
    runs-on: ubuntu-latest
    permissions:
      packages: write
    steps:
      -
        name: Get build time
        id: build_time
        run: echo "time=$(date -u +'%Y-%m-%dT%H:%M:%SZ')" >> "$GITHUB_OUTPUT"
      -
        name: Checkout
        uses: actions/checkout@v4
        with:
          fetch-depth: 0
          ref: ${{ github.ref_name }}
      -
        name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3
        with:
          driver: docker
      -
        name: Login to Registry
        uses: docker/login-action@v3
        with:
          registry: ghcr.io
          username: ${{ github.actor }}
          password: ${{ github.token }}
      -
        name: Build image
        id: docker_build
        uses: docker/build-push-action@v5
        with:
          context: .
          load: true
          labels: |
            ci.timestamp=${{ steps.build_time.outputs.time }}
            ci.image=${{ github.repository }}
            ci.tag=${{ github.ref_name }}
            ci.commit=${{ github.sha }}
            ci.builder=${{ github.triggering_actor }}
          tags: ghcr.io/${{ github.repository }}:${{ github.ref_name }}
          build-args: |
            BUILDKIT_CONTEXT_KEEP_GIT_DIR=true
      -
        name: Write new Dockerfile
        run: echo "FROM ghcr.io/${{ github.repository }}:${{ github.ref_name }}" > Dockerfile.new

      - name: Build labeled image
        uses: docker/build-push-action@v5
        with:
          context: .
          file: Dockerfile.new
          push: true
          labels: ci.digest=${{ steps.docker_build.outputs.digest }}
          tags: ghcr.io/${{ github.repository }}:${{ github.ref_name }}