Gear Exchange – Flywheel.io

Introduction

Flywheel has curated this collection of Gears, or ready-to-use plug-in applications, that automate routine tasks, including metadata extraction, classification, quality assurance, BIDS conversion, and full analytic pipelines. This library is made available through the contributions of Flywheel employees, customers and members of the broader research community.

What is the Flywheel Gear Exchange?

The Flywheel Gear Exchange is a place where Gear authors can publish Gears they are willing to share with the broader Flywheel community.
It is a library of ready-to-use pre-processing and analysis pipeline applications
It is hosted within a GitHub Repository containing Manifests with CI enabled to automatically build and host Gear assets.

What are the Benefits of the Exchange?

Gears within the exchange can be installed to any Flywheel Instance
The Gear Exchange is Flywheel's principles in practice
- Transparency
- Reproducibility

How do I contribute?

If you would like to contribute a Gear you have developed to the Flywheel Gear Exchange, please visit the Gear Exchange GitHub repo and have a look at the README for instructions.

GEARS

VISTA Lab: ACPC-ANAT Normalize

Normalize anatomical NIfTI with the MNI template or with AC-PC coordinates provided by the user.

Author:

GLU <glerma@stanford.edu>

Maintainer:

GLU <glerma@stanford.edu>

License:

MIT

Version:

1.0.3 1.0.2 1.0.0

URL:

https://github.com/vistalab/acpc-anat

Source:

https://github.com/vistalab/acpc-anat

AFNI: Brain Warp

AFNI-based brain warping based on D99 Macaque Atlas warp scripts, which use AF NI functions (AFNI_2011_12_21_1014) to align a template and segmentation to the native space of an individual macaque in its native space. The output includes the native aligned to the template dataset and vice versa. It also creates surfaces for 1

structures in the individual native space and an approximate surface for the whole brain. All surfaces are saved in GIFTI format, and volumes are in AFNI format. This Gear will convert output volume files to NIfTI format.

Author:

Daniel Glen <glend@mail.nih.gov>

Maintainer:

Carlos Correa <cgc@stanford.edu>

License:

GPL-2.0

Version:

0.0.1

URL:

https://afni.nimh.nih.gov/pub/dist/atlases/macaque/macaqueatlas_1.2a/AFNI_scripts/

Source:

https://github.com/scitran-apps/afni-brain-warp

AFQ: Automated Fiber Quantification

AFQ was designed [by Jason D. Yeatman, et al.] to generate Tract Profiles of tissue properties for major fiber tracts in healthy and diseased brains. Online documenta tion can be found at: https://github.com/yeatmanlab/AFQ/wiki.

Author:

Jason D. Yeatman <jyeatman@uw.edu>

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

GPL-2.0

Version:

0.0.2

URL:

https://github.com/yeatmanlab/afq

Source:

https://github.com/scitran-apps/afq

AFQ Pipeline: Automated Fiber 6uantNܪcatNon Pipeline (DTIInit + MRtrix3 + ET + LiFE + AFQ)

This gear contains a multi-step pipeline designed to run DTIInit, MRtrix3, LiFE, ET, and AFQ. DTIInit runs preprocessing steps, MRTrix3 + Ensemble Tractography + LiFE gen erate a connectome which is then run through Automated Fiber Quantification (AFQ). AFQ generates tract profiles of tissue properties for major fiber tracts in the brain.

This gear also generates AFQ Browser outputs for visualization. Required inputs are (1) DWI NIfTI image, (2) BVEC file, (3) BVAL file, and (4) and Anatomical NIfTI file - which is optional and will be used to align the DWI data, if provided.

Author:

Yeatman et al., Stanford VISTA Lab, FMRIB Software Lab, MRTrix, Pestilli et al., Take mura et al.

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

Other

Version:

3.0.0 1.0.3 1.0.2 1.0.1 1.0.0

URL:

https://github.com/yeatmanlab/afq

Source:

https://github.com/scitran-apps/afq-pipline

AFQ Pipeline SDK: Automated Fiber Quantification Processing Pipeline

This SDK-enabled Gear is able to take a user-provided acquisition label and automati cally find appropriate inputs for the Gear. The Gear runs a 3-step pipeline culminating in a run of AFQ. The first step is optional, and will merge two diffusion datasets using FSLMERGE. The second step is diffusion data preprocessing using DTIINIT. The final step is Automated Fiber Quantification (AFQ), which generates tract profiles of tissue

properties for major white matter tracts in the brain.

Author:

Jason D. Yeatman, et. al, VISTA Lab, FMRIB Software Laboratory

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

Other

Version:

1.1.0 1.0.2 1.0.1 1.0.0

URL:

https://github.com/yeatmanlab/afq

Source:

https://github.com/scitran-apps/afq-pipeline-sdk

All-Subject BIDS fMRIPrep: Run BIDS fMRIPrep on all subjects in the project

See the description of BIDS fMRIPrep for version information.

Author:

Poldrack lab, Stanford University

Maintainer:

Flywheel <support@flywheel.io>

License:

BSD-3-Clause

Version:

1.0.2 1.0.1

URL:

https://github.com/flywheel-apps/all-subject-bids-fmriprep/blob/master/README.md

Source:

https://github.com/nipreps/fmriprep

Gradient Anisotropic Diffusion denoising

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

0.1.1_4.13.0

URL:

https://gitlab.com/flywheel-io/flywheel-apps/anisotropic-diffusion-denoising

Source:

https://gitlab.com/flywheel-io/flywheel-apps/anisotropic-diffusion-denoising 5

ANTs Build Template Parallel

ANTs based gear that run buildtemplateparallel.sh script and generate a template im age by co-registering a set of inputs images

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

0.1.1_2.3.5

URL:

https://gitlab.com/flywheel-io/flywheel-apps/ants-buildtemplateparallel

Source:

https://gitlab.com/flywheel-io/flywheel-apps/ants-buildtemplateparallel

Apply Canonical Transform

Reorient NIfTI data and metadata fields into RAS space by estimating and applying a canonical transform.

Author:

Bob Dougherty <bobd@stanford.edu>

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

MIT

Version:

0.1.0

URL:

https://github.com/vistalab/vistasoft/blob/master/fileFilters/nifti/niftiApplyCannonicalXform.m

Source:

https://github.com/scitran-apps/apply-canonical-xform

BIDS fMRIPrep: A Robust Preprocessing Pipeline for fMRI Data

fMRIPrep 20.2.4 (Long-Term Support version) is a functional magnetic resonance imaging (fMRI) data preprocessing pipeline that is designed to provide an easily ac cessible, state-of-the-art interface that is robust to variations in scan acquisition pro tocols and that requires minimal user input, while providing easily interpretable and comprehensive error and output reporting. It performs basic processing steps (core gistration, normalization, unwarping, noise component extraction, segmentation, skullstripping etc.) providing outputs that can be easily submitted to a variety of group level analyses, including task-based or resting-state fMRI, graph theory meas ures, surface or volume-based statistics, etc.

Author:

Poldrack lab, Stanford University

Maintainer:

Flywheel <support@flywheel.io>

License:

BSD-3-Clause

Version:

1.2.0_20.2.4 1.1.9_20.2.0 1.1.22_20.2.4 1.1.16_20.2.1 1.0.3_1.5.2 1.0.12_1.5.10 1.0.11_1.5.9

URL:

https://github.com/flywheel-apps/bids-fmriprep/blob/master/README.md

Source:

https://github.com/nipreps/fmriprep

BIDS Freesurfer: Freesurfer recon-all BIDS App

BIDS-Apps/Freesurfer (6.0.1-5) This app implements surface reconstruction using Freesurfer. It reconstructs the surface for each subject individually and then creates a study specific template. In case there are multiple sessions the Freesurfer longitu dinal pipeline is used (creating subject specific templates) unless instructed to com bine data across sessions. The current Freesurfer version is based on: freesurfer-Li nux-centos6_x86_64-stable-pub-v6.0.0.tar.gz.

Author:

http://surfer.nmr.mgh.harvard.edu/

Maintainer:

Flywheel <support@flywheel.io>

License:

Apache-2.0

Version:

1.0.5_6.0.1-5 1.0.4_6.0.1-5 1.0.1_6.0.1-5

URL:

https://github.com/BIDS-Apps/freesurfer

Source:

https://github.com/flywheel-apps/bids-freesurfer

BIDS MRIQC: Automatic prediction of quality and visual report ing of MRI scans in BIDS format

MRIQC (0.15.2 - April 6, 2020) extracts no-reference image quality metrics (IQMs) from T1w and T2w structural and functional magnetic resonance imaging data. Note: arguments --n_procs --mem_gb and --ants-nthreads are not availble to configure be caues they are set to use the maximum available as detected by MRIQC.

Author:

Poldrack Lab, Stanford University

Maintainer:

Flywheel <support@flywheel.io>

License:

BSD-3-Clause

Version:

1.2.2_0.15.2 1.2.1_0.15.2 1.2.0_0.15.2 1.1.0_0.15.2 1.0.8_0.15.1 1.0.0_0.15.1

URL:

https://mriqc.readthedocs.io/en/stable/about.html

Source:

https://gitlab.com/flywheel-io/flywheel-apps/bids-mriqc

BIDS Pre-Curation

Prepare project for BIDS Curation. BIDS Pre-Curate offers a simple way to modify la bels and classifications of project data to be compatible with the BIDS-spec. Running pre-curate on a given project (as a project-level analysis) will generate CSV files that will be populated with a unique list of container labels, as well as slots for the infor mation needed for BIDS curation (classification, task, etc.). These CSV files can be downloaded and modified (outside of Flywheel) to provide missing or corrected infor mation. The completed CSV file is then uploaded to the project (as an attachment) and provided as input to a run of this same gear to do on-the-fly mappings and meta data updates. For more information, please see the readme in the source repository.

Author:

Flywheel Exchange, LLC

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

0.1.5 0.1.4

URL:

https://github.com/flywheel-apps/bids-pre-curate

Source:

https://github.com/flywheel-apps/bids-pre-curate

BIDS qsiprep

BIDS qsiprep 0.0.0_0.12.2 qsiprep configures pipelines for processing diffusion weighted MRI (dMRI) data. The main features of this software are A BIDS-app ap proach to preprocessing nearly all kinds of modern diffusion MRI data. Automatically generated preprocessing pipelines that correctly group, distortion correct, motion correct, denoise, coregister and resample your scans, producing visual reports and QC metrics. A system for running state-of-the-art reconstruction pipelines that in clude algorithms from Dipy, MRTrix, DSI Studio and others. A novel motion correction algorithm that works on DSI and random q-space sampling schemes

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

0.0.0_0.12.2

URL:

https://github.com/flywheel-apps/bids-qsiprep

Source:

https://qsiprep.readthedocs.io/en/latest/

Bruker to NIfTI Converter

Bruker2nifti is an open source medical image format converter from raw Bruker Para Vision to NifTi, without any intermediate step through the DICOM standard formats.

Author:

Flywheel

Maintainer:

support@flywheel.io

License:

Other

Version:

0.1.0

URL:

Source:

BXH-XCEDE-TOOLS: fMRI QA (v1.11.14)

Use BXH/XCEDE Tools to perform QA (quality assurance) calculations and produce images, graphs, and/or XML data as output. fmriqa_phantomqa.pl and fmriqa_gener ate.pl produce an HTML report with various QA measures. fmriqa_phantomqa.pl was designed for fMRI images of the BIRN stability phantom, and fmriqa_generate.pl has been used for human fMRI data.

Author:

Syam Gadde <gadde@biac.duke.edu>

Maintainer:

Michael Perry <support@flywheel.io>

License:

Other

Version:

1.0.2_1.11.14 1.0.1_1.11.14 0.1

URL:

https://www.nitrc.org/projects/bxh_xcede_tools/

Source:

https://github.com/flywheel-apps/bxh-xcede-tools-qa/

BIDS-APP: C-PAC Configurable Pipeline for the Analysis of Connectomes)

The Configurable Pipeline for the Analysis of Connectomes C-PAC is a software for performing high-throughput preprocessing and analysis of functional connectomes data using high-performance computers. C-PAC is implemented in Python using the Nipype pipelining library to efficiently combine tools from AFNI, ANTS, and FSL to achieve high quality and robust automated processing. This docker container, when built, is an application for performing participant level analyses. Future releases will include group-level analyses, when there is a BIDS standard for handling derivatives and group models.

Author:

Craddock C, Sikka S, Cheung B, et al.

Maintainer:

Flywheel <support@flywheel.io>

License:

Apache-2.0

Version:

0.3.0_1.8.0 0.1.2_v1.4.1 0.1.1_v1.4.1 0.0.1

URL:

https://gitlab.com/flywheel-io/flywheel-apps/cpac

Source:

https://gitlab.com/flywheel-io/flywheel-apps/cpac

CNI-DCM-CONVERT: DICOM Conversion Utility

CNI-DCM-CONVERT uses SciTran's data library (https://github.com/vistalab/scitran data) to convert raw DICOM data (within a zip archive) to NIfTI, Montage, and PNG (screenshot acquisitions) formats. DCM-CONVERT supports Siemens and GE DICOM data. This gear will also use dcm2niix to generate bids-sidecar metadata. Those met adata will be added to the output NIfTI file's info object in Flywheel.

Author:

Scientific Transparency (RF Dougherty, K Hahn, R Bowen, G Schaefer, LM Perry, H Wu)

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

Apache-2.0

Version:

2.6.0 2.5.0 2.4.0 2.3.2 2.3.1 2.3.0 2.2.0 2.1.2 2.1.1 2.1.0 2.0.1

URL:

https://github.com/vistalab/scitran-data

Source:

https://github.com/cni/cni-dcm-convert

CNI: DICOM MR Classifier

Extract metadata and determine classification from GE DICOM data.

Author:

Michael Perry <lmperry@stanford.edu>

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

Apache-2.0

Version:

3.3.0 3.2.1 3.2.0 3.1.2 3.1.0 3.0.0 2.1.0 2.0.0 1.0.2 1.0.1 1.0.0

URL:

https://cni.stanford.edu

Source:

https://github.com/cni/cni-dicom-mr-classifier

CNI: Quality Assurance Report (fMRI)

Run QA metrics (displacement, signal spikes) to create a quality assurance report (png) for an fMRI NIfTI using CNI/NIMS code.

Author:

Robert F. Dougherty, Hua Wu

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

Apache-2.0

Version:

1.0.4 1.0.3 1.0.2 1.0.1

URL:

https://cni.stanford.edu/wiki/QA

Source:

https://github.com/cni/cni-qa-report-fmri

BIDS Curation

Use this gear to initialize BIDS filenames and attributes on all files within a given project.

Author:

Flywheel <support@flywheel.io>

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

2.1.3_1.0.2 2.1.2_1.0.1 2.1.1_1.0.0 1.0.0_0.9.1 1.0.0_0.9.0 0.6.8 0.6.7 0.6.5 0.6.4 0.6.3 0.6.2 0.6.0 0.5.0 0.3.6 0.3.5 0.3.4 0.3.3 0.3.2 0.3.1 0.3.0 0.2.0 0.1.0 URL:

https://bids.neuroimaging.io/

Source:

https://gitlab.com/flywheel-io/flywheel-apps/curate-bids

SciTran: DCM-CONVERT - DICOM conversion tool

DCM-CONVERT uses SciTran's data library (https://github.com/scitran/data) to con vert raw DICOM data (zip archive) to NIfTI, Montage, and PNG (screenshot acquisi tions) formats. DCM-CONVERT supports Siemens and GE DICOM data.

Author:

Scientific Transparency (RF Dougherty, K Hahn, R Bowen, G Schaefer, LM Perry) Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

Apache-2.0

Version:

1.1.3 1.1.2 1.1.1 1.1.0 1.0.0

URL:

https://github.com/scitran/data

Source:

https://github.com/scitran-apps/dcm-convert

DCM to MIPS

Convert DICOM file into PNG images using Maximum Intensity Projection(MIP) tech nique.

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

0.1.0

URL:

https://gitlab.com/flywheel-io/flywheel-apps/dcm-to-mips

Source:

https://gitlab.com/flywheel-io/flywheel-apps/dcm-to-mips

DCM2NII: v.4AUGUST2014

Chris Rorden's dcm2nii (4AUGUST2014 64-bit) is a popular tool for converting im ages from the complicated formats used by scanner manufacturers (DICOM, PAR/ REC) to the simple NIfTI format used by many scientific tools. dcm2nii works for all modalities (CT, MRI, PET, SPECT) and sequence types.

Author:

Chris Rorden

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

BSD-2-Clause

Version:

0.1.0

URL:

https://www.nitrc.org/projects/dcm2nii/

Source:

https://github.com/scitran-apps/dcm2nii

dcm2niix: DICOM to NIfTI conversion (with PyDeface)

Implementation of Chris Rorden's dcm2niix tool for converting DICOM (or PAR/REC) to NIfTI (or NRRD), with an optional implementation of Poldrack Lab's PyDeface to remove facial structures from NIfTI.

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

Other

Version:

1.3.1_1.0.20201102 1.3.0_1.0.20201102 1.2.1_1.0.20201102 1.2.0_1.0.20201102 1.1.0_1.0.20201102 1.0.0_1.0.20200331 0.8.0_1.0.20200331 0.8.0_1.0.20190902 0.7.9_1.0.20190410 0.7.8_1.0.20190410 0.7.8_1.0.20181114 0.7.7_1.0.20181114 0.7.6_1.0.20180622_5af76a9 0.7.5_1.0.20180622_5af76a9

0.7.4_1.0.20180622_5af76a9 0.7.3_1.0.20180622 0.7.2_1.0.20180622 17

0.7.1_1.0.20180622 0.7.10_1.0.20190410 0.7.0_1.0.20180622 0.6.1_1.0.20180622 0.6.0_1.0.20180622 0.5.4_1.0.20180328 0.5.2_1.0.20180328 0.5.1_1.0.20180328 0.5.0_1.0.20171215 0.3.4_1.0.20171215 0.3.3_1.0.20171215 0.3.2 0.3.1 0.3 0.2.1 0.2 0.1.1 0.1.0 0.0.3

URL:

https://github.com/rordenlab/dcm2niix

Source:

https://github.com/flywheel-apps/dcm2niix

Debug File Generator: Creating a 1 GB ܪQe

This Gear produces a 1GB .txt file.

Author:

Jennifer Reiter <jenniferreiter@invenshure.com>

Maintainer:

Jennifer Reiter <jenniferreiter@invenshure.com>

License:

Other

Version:

0.0.1

URL:

https://github.com/flywheel-apps/debug-generatefile

Source:

https://github.com/flywheel-apps/debug-generatefile

De-identified Export

Profile-based anonymization and export of files within a project. Files within the source project will be anonymized (according to a required template) and exported to a specified project. Output is a csv file reporting the status of all exported items.

Author:

Flywheel, Inc.

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

1.2.3 1.2.2 1.2.1 1.2.0 1.0.0

URL:

https://gitlab.com/flywheel-io/flywheel-apps/deid-export/-/blob/main/README.md

Source:

https://gitlab.com/flywheel-io/flywheel-apps/deid-export

Dicom Fixer

Fixes various issues in dicoms.

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

0.4.0 0.3.6 0.2.1

URL:

https://gitlab.com/flywheel-io/flywheel-apps/dicom-fixer/-/blob/main/README.md

Source:

https://gitlab.com/flywheel-io/flywheel-apps/dicom-fixer

SciTran: DICOM MR Classifier

Extract metadata and determine classification from raw DICOM data. Compatible with Siemens, Philips, and GE DICOMs.

Author:

Michael Perry <lmperry@stanford.edu>

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

Apache-2.0

Version:

1.4.8 1.4.7 1.4.6 1.4.5 1.4.4 1.4.2 1.4.10 1.4.1 1.4.0 1.3.2 1.3.1 1.3.0 1.2.2 1.2.1 1.2.0 1.1.0 1.0.0 0.9.1 0.9.0 0.8.2 0.8.1 0.8.0 0.7.6 0.7.5 0.7.4 0.7.3 0.7.1 0.7.0 0.6.1 0.6.0 0.5.0 0.4.0 0.3.3 0.3.2 0.3.1 0.3.0 0.2.7 0.2.6 0.2.5 0.2.4 0.2.3 0.2.2 0.2.1 0.2 0.1.9 0.1.8 0.1.12 0.1.11 0.1.10

URL:

https://github.com/flywheel-apps/dicom-mr-classifier

Source:

https://github.com/flywheel-apps/dicom-mr-classifier/releases

Dicom QC

Validate dicom archive on a set of hardcoded and user-specified rules Author:

Flywheel <support@flywheel.io>

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

0.3.0

URL:

https://gitlab.com/flywheel-io/flywheel-apps/dicom-qc/-/blob/master/README.md Source:

https://gitlab.com/flywheel-io/flywheel-apps/dicom-qc

DCMTK: DICOM Send

DICOM Send utilizes DCMTK's storescu to send DICOM data from a Flywheel in stance to a destination DICOM server, hosted externally. This Gear supports the transmission of individual DICOM files and archives, as well as the transmission of an entire session when a specific input is not provided. Note that a private tag is add ed to each DICOM file to be transmitted (Flywheel:DICOM Send, at group 0x0021). Im portantly, the external DICOM server must be reachable from the engine host of the Flywheel instance.

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

Other

Version:

2.1.1 2.1.0 2.0.0 1.1.2 1.1.1 1.1.0 1.0.0 0.9 0.6.2 0.14.1 0.14.0 0.13.0 0.12.0 0.11.0 0.10.0

URL:

http://support.dcmtk.org/docs/storescu.html

Source:

https://github.com/flywheel-apps/dicom-send

VISTALAB: DTI Error

Find RMSE between the measured and ADC (or dSIG) based on tensor model. Calcu late the histogram of differences between dti based predictions (ADC or dSig) with the actual ADC or dSig data. Larger deviations suggest noisier data.

Author:

Brian Wandell <wandell@stanford.edu>, Michael Perry <lmperry@stanford.edu>

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

GPL-2.0

Version:

0.1.0

URL:

https://github.com/scitran-apps/dtiError

Source:

https://github.com/scitran-apps/dtiError/src

VISTALAB: dtiInit - Diffusion Data Initialization Pipeline

VISTALAB's dtiInit (DTI Initialization) runs the VISTASOFT/mrDiffusion pre-process ing pipeline on raw DWI data. This Gear allows all dtiInit parameters to be set from within the configuration UI. All outputs are archived in a zip file for easy download. dtiInit.json is saved for easy reference to configuration parameters used at runtime.

Author:

VISTA Lab, Stanford University

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

GPL-2.0

Version:

0.2.2 0.2.1 0.1.2

URL:

https://github.com/vistalab/vistasoft/wiki/dwi-Initialization

Source:

https://github.com/scitran-apps/dtiinit

dtiInit: Diffusion Map Generation

Generate diffusion maps, including Fractional Anisotropy (FA), Axial Diffusivity (AD), Mean Diffusivity (MD), and Radial Diffusivity (RD). The input to this Gear is a dtiInit archive, containing a 'dt6.mat' file. This archive is generated from either the dtiInit Flywheel Gear, or from the Flywheel Gear which executes the AFQ processing pipe line. Outputs are fa, md, rd, and ad files (in gzipped NIfTI format).

Author:

Stanford VISTA Lab (vistalab.stanford.edu)

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

GPL-2.0

Version:

1.0.0

URL:

https://github.com/vistalab/vistasoft/wiki

Source:

https://github.com/vistalab/fw-gear-dtiinit-diffusion-maps

VISTA Lab: DWI Flip BVEC

Flips the sign of the the specified B-vector(s).

Author:

GLU <glerma@stanford.edu>

Maintainer:

GLU <glerma@stanford.edu>

License:

MIT

Version:

1.0.0

URL:

https://github.com/vistalab/dwi-flip-bvec

Source:

https://github.com/vistalab/dwi-flip-bvec

SCITRAN: DWI Split Shells

Extract individual diffusion shells from multi-shell DWI data. Output includes a NIfTI, BVEC, and BVAL file for each diffusion shell found in the data. By default this gear will normalize the bvalues (e.g., b=998 will become b=1000).

Author:

GLU <glerma@stanford.edu>

Maintainer:

GLU <glerma@stanford.edu>

License:

MIT

Version:

2.0.0 1.1.0 1.0.0

URL:

https://github.com/scitran-apps/dwi-split-shells

Source:

https://github.com/scitran-apps/dwi-split-shells

Brain Vision EEG Classifier

Classifies Brain Vision EEG data and appends metadata attributes to the file's cus tom info structure within Flywheel. Input to this gear is a Flywheel packaged EEG ar chive (.eeg.zip) containing Brain Vision EEG data (in .vhdr format). Output is a JSON file (.metadata.json) containing metadata that will be used by the Flywheel platform to populate the input file's custom info fields.

Author:

Travis Richardson

Maintainer:

Travis Richardson

License:

MIT

Version:

1.0.0

URL:

https://github.com/flywheel-apps/eeg-classifier

Source:

https://github.com/flywheel-apps/eeg-classifier

Export ROIs

A gear for exporting ROI's saved in the OHIF viewer to CSV's

Author:

Flywheel SSE

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

1.1.1 1.0.3

URL:

https://github.com/flywheel-apps/ROI_export

Source:

https://github.com/flywheel-apps/ROI_export

CMRR: Extract CMRR Physio

Extract physiological log files from encoded '_PHYSIO' DICOM file generated by CMRR MB sequences (>=R015, >=VD13A), Generate BIDs compliant files if desired

Author:

E. Auerbach, CMRR, 2016

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

1.2.7 1.2.4 1.2.3 1.2.2 1.2.1 1.0.0 0.1.1 0.1

URL:

https://github.com/CMRR-C2P/MB

Source:

https://github.com/flywheel-apps/extract-cmrr-physio/releases

File Classifier

Generic file classifier

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

0.2.0

URL:

https://gitlab.com/flywheel-io/flywheel-apps/file-classifier.git

Source:

https://gitlab.com/flywheel-io/flywheel-apps/file-classifier.git

File Curator

Curates a given file, to be used as a gear rule

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

0.2.0 0.1.3 0.1.0

URL:

https://gitlab.com/flywheel-io/flywheel-apps/file-curator

Source:

https://gitlab.com/flywheel-io/flywheel-apps/file-curator

File metadata importer

Import file metadata into Flywheel.

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

1.1.0 1.0.1 0.2.1 0.2.0

URL:

https://gitlab.com/flywheel-io/flywheel-apps/file-metadata-importer

Source:

https://gitlab.com/flywheel-io/flywheel-apps/file-metadata-importer

Flaudit: Flywheel Audit

Audit your Flywheel project for sequence completeness, BIDS curation summaries, gear and job runs, workflow completeness, and more.

Author:

Tinashe Michael Tapera

Maintainer:

Tinashe Michael Tapera

License:

BSD-3-Clause

Version:

0.0.5_0.1.6

URL:

https://fw-heudiconv.readthedocs.io/en/latest/

Source:

VPNL: fLoc - Face Localizer Analysis Pipeline

Automated analysis of fMRI data from fLoc funcional localizer experiment used to define category-selective cortical regions. By default the Gear generates the follow ing voxel-wise parameters maps: Beta values, model residual error, proportion of var iance explained, and GLM contrasts (t-values). All parameter maps are saved as .mat and nifti files in session/Inplane/GLMs/ and can be viewed in Vistasoft. The Gear al so writes a file named 'fLocAnalysis_log.txt' that logs progress and saves input and glm parameters as fLocAnalysisParams.mat. If there are 10 conditions specified, 15 contrast maps will be generated. 10 maps will contrast each individual condition ver sus all others. The other 5 maps will contrast conditions 1 and 2 vs all others, 3 and 4 versus all others, and so on. If there are not 10 conditions specified in the parfiles, then the maps generated will contrast each individual condition versus all others.

Author:

Anthony Stigliani, VPNL, Stanford

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

Other

Version:

0.3.0 0.2.1 0.2.0 0.1.0

URL:

https://github.com/VPNL/fLoc

Source:

https://github.com/VPNL/fLoc

Flywheel Example Gear

Sample gear to demonstrate a simple use case of outputting the name of each input file.

Author:

Flywheel <support@flywheel.io>

Maintainer:

Ryan Sanford <ryansanford@flywheel.io>

License:

MIT

Version:

0.0.4 0.0.3

URL:

https://flywheel.io/

Source:

https://github.com/flywheel-apps/example-gear

fMRIPREP: A Robust Preprocessing Pipeline for fMRI Data

fmriprep is a functional magnetic resonance imaging (fMRI) data preprocessing pipe line that is designed to provide an easily accessible, state-of-the-art interface that is robust to variations in scan acquisition protocols and that requires minimal user in put, while providing easily interpretable and comprehensive error and output report ing. It performs basic processing steps (coregistration, normalization, unwarping, 30

noise component extraction, segmentation, skullstripping etc.) providing outputs that can be easily submitted to a variety of group level analyses, including task based or resting-state fMRI, graph theory measures, surface or volume-based statis tics, etc.

Author:

Poldrack Lab, Stanford University

Maintainer:

Flywheel <support@flywheel.io>

License:

Other

Version:

6.1.3_1.5.5 6.1.2_1.5.5 6.1.1_1.5.5 6.0.0_1.5.5 5.7.1_1.2.6-1 5.7.0_1.2.6-1 5.6.3_1.2.6-1 5.6.2_1.2.6-1 5.6.1_1.1.4 5.6.0_1.1.4 5.5.0_1.1.4 5.4.2_1.1.2 5.4.1_1.1.2 5.4.0_1.1.2 5.3.0_1.1.2 5.2.2_1.1.1 5.2.0_1.0.15 5.1.0_1.0.15 5.0.0_1.0.6 0.4.7_1.0.15 0.4.6_1.0.15 0.4.5_1.0.6 0.4.4_1.0.6 0.4.3_1.0.6 0.4.2 0.3.3 0.3.2 0.3 0.2 0.1 URL:

https://fmriprep.readthedocs.io/en/1.5.5/

Source:

https://github.com/flywheel-apps/fmriprep

FreeSurfer 7.2.0: run recon-all

FreeSurfer version 7.1.1 Release (July 27, 2020). This gear takes an anatomical NIfTI file and performs all of the FreeSurfer cortical reconstruction process. Outputs are provided in a zip file and include the entire output directory tree from Recon-All. Con figuration options exist for setting the subject ID and for converting outputs to NIfTI, OBJ, and CSV. FreeSurfer is a software package for the analysis and visualization of structural and functional neuroimaging data from cross-sectional or longitudinal studies. It is developed by the Laboratory for Computational Neuroimaging at the Athinoula A. Martinos Center for Biomedical Imaging. Please see https://surf er.nmr.mgh.harvard.edu/fswiki/FreeSurferSoftwareLicense for license information.

Author:

Laboratory for Computational Neuroimaging <freesurfer@nmr.mgh.harvard.edu>

Maintainer:

Flywheel <support@flywheel.io>

License:

Other

Version:

1.1.1_7.2.0 1.1.0_7.1.1 1.0.0_7.1.1_rc0 1.0.0_7.1.1 0.4.2_6.0.1 0.4.1_6.0.1 0.4.0_6.0.1 0.3.1_6.0.1 0.3.0 0.2.0 0.1.4 0.1.3 0.1.2 0.1.0_7.1.1_rc0 0.1.0

URL:

https://surfer.nmr.mgh.harvard.edu

Source:

https://github.com/flywheel-apps/freesurfer-recon-all

FSL-ANAT - Anatomical Processing Pipeline

This tool provides a general pipeline for processing anatomical images (e.g. T1- weighted scans).

Author:

Analysis Group, FMRIB, Oxford, UK.

Maintainer:

Flywheel <support@flywheel.io>

License:

Other

Version:

1.1.2_6.0.1 1.1.1_6.0.1 1.1.1_5.0.9 1.1.0_6.0.1 1.1.0_5.0.9 1.0.0_6.0.1 1.0.0_5.0.9

URL:

https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/fsl_anat

Source:

https://github.com/flywheel-apps/fsl-anat

FSL: Brain Extraction Tool (BET2)

Brain Extraction Tool (BET2) from FMRIB Software Library (FSL) v5.0. BET (Brain Ex traction Tool) deletes non-brain tissue from an image of the whole head. It can also estimate the inner and outer skull surfaces, and outer scalp surface, if you have good quality T1 and T2 input images.

Author:

Analysis Group, FMRIB, Oxford, UK.

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

Apache-2.0

Version:

0.2.3 0.2.2 0.2.1 0.2.0 0.1.0

URL:

http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET

Source:

https://github.com/scitran-apps/fsl-bet

FSL: FMRIB Automated Segmentation Tool (FAST4, v5.0.9)

FAST (FMRIB's Automated Segmentation Tool) segments a 3D image of the brain in to different tissue types (Grey Matter, White Matter, CSF, etc.), whilst also correcting for spatial intensity variations (also known as bias field or RF inhomogeneities). The underlying method is based on a hidden Markov random field model and an associ ated Expectation-Maximization algorithm. The whole process is fully automated and can also produce a bias field-corrected input image and a probabilistic and/or partial 33

volume tissue segmentation. It is robust and reliable, compared to most finite mix ture model-based methods, which are sensitive to noise.

Author:

Analysis Group, FMRIB, Oxford, UK.

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

Apache-2.0

Version:

0.1.1 0.1

URL:

http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FAST

Source:

https://github.com/scitran-apps/fsl-fast

FSL: FEAT - fMRI preprocessing (v6.0)

FSL's FEAT (FMRI Expert Analysis Tool). As implemented in this Gear FEAT allows for basic preprocessing of an fMRI dataset including motion correction using MCFLIRT [Jenkinson 2002]; slice-timing correction using Fourier-space time-series phase-shift ing; non-brain removal using BET [Smith 2002]; spatial smoothing using a Gaussian kernel; multiplicative mean intensity normalization of the volume at each timepoint; and highpass temporal filtering (Gaussian-weighted least-squares straight line fit ting), brain extraction, and registration to a standard image (MNI152).

Author:

Analysis Group, FMRIB, Oxford, UK.

Maintainer:

Flywheel <support@flywheel.io>

License:

Apache-2.0

Version:

1.0.4_6.0 1.0.3_6.0 0.1.4 0.1.3 0.1.1 0.1

URL:

http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT

Source:

https://github.com/flywheel-apps/fsl-feat

FSL fslhd

FSL's fslhd reads fields from a NIfTI file header. This Gear takes that header and gen erates metadata that is placed upon the input file's info field in the Flywheel data base. FSLHD reports every field of an Analyze or Nifti header (note that the fields are different although some are common, e.g. pixdims). The reported values are those used internally in FSL programs and are sometimes different from the raw values stored in the file to avoid incorrect settings (e.g. dimN has a minimum value of 1, not 0).

Author:

Analysis Group, FMRIB, Oxford, UK.

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

1.1.3_5.0 1.1.2_5.0 1.1.1 1.1.0 1.0.1 1.0.0

URL:

https://gitlab.com/flywheel-io/flywheel-apps/fsl-fslhd

Source:

https://gitlab.com/flywheel-io/flywheel-apps/fsl-fslhd

FSL: fslreorient2std - Reorient Image to Standard Template

fslreorient2std is a tool for reorienting the image to match the approximate orienta tion of the standard template images (MNI152). It only applies 0, 90, 180 or 270 de gree rotations. It is not a registration tool. It requires NIfTI images with valid orienta tion information in them (seen by valid labels in FSLView). This tool assumes the la bels are correct - if not, fix that before using this Gear.

Author:

Analysis Group, FMRIB, Oxford, UK.

Maintainer:

Flywheel <support@flywheel.io>

License:

Other

Version:

1.0.0

URL:

https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils

Source:

https://github.com/flywheel-apps/fsl-fslreorient2std

FSL: SIENA - Longitudinal analysis of brain change

Siena estimates percentage brain volume change (PBVC) betweem two input images, taken of the same subject, at different points in time. It calls a series of FSL pro grams to strip the non-brain tissue from the two images, register the two brains (un der the constraint that the skulls are used to hold the scaling constant during the reg istration) and analyse the brain change between the two time points. As implemen ted in this Gear Siena allows for analysis of 14 subcortical regions as well as the Brain-Stem/4th Ventricle (with VENT option). Inputs should be structural images (T1- weighted, T2-weighted, PD, etc) where the in-plane resolution is better than 2mm (ide ally 1mm). Outputs consist of an archive containing the results of the analysis, as well as an HTML report summarizing the analysis findings.

Author:

Analysis Group, FMRIB, Oxford, UK.

Maintainer:

Flywheel <support@flywheel.io>

License:

Apache-2.0

Version:

1.0.2_5.0 1.0.1_5.0 1.0.0_5.0

URL:

https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/SIENA

Source:

https://github.com/flywheel-apps/fsl-siena-sienax

FSL: SIENAX - Brain tissue volume, normalised for subject head size

FSL's SIENAX. Sienax estimates total brain tissue volume, from a single image, nor malised for skull size. It calls a series of FSL programs: It first strips non-brain tissue, and then uses the brain and skull images to estimate the scaling between the sub ject's image and standard space. It then runs tissue segmentation to estimate the volume of brain tissue, and multiplies this by the estimated scaling factor, to reduce head-size-related variability between subjects. Inputs should be structural image (T1- weighted, T2-weighted, PD, etc) where the in-plane resolution is better than 2mm (ide ally 1mm). Outputs consist of an archive containing the results of the analysis, as well as an HTML report summarizing the analysis findings.

Author:

Analysis Group, FMRIB, Oxford, UK.

Maintainer:

Flywheel <support@flywheel.io>

License:

Apache-2.0

Version:

1.0.2_5.0 1.0.1_5.0 1.0.0_5.0

URL:

https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/SIENA

Source:

https://github.com/flywheel-apps/fsl-siena-sienax

FSL fslstats

This gear returns statistical output for a given NIFTI image.

Author:

Analysis Group, FMRIB, Oxford, UK

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

0.1.5_5.0.9

URL:

https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils

Source:

https://gitlab.com/flywheel-io/flywheel-apps/fsl-stats

FSL: SUPER Brain Extraction Tool (BET2)

Modified Brain Extraction Tool 2 (BET2) from FMRIB Software Library (FSL) v5.0 called SuperBET2 deletes non-brain tissue from an image of the whole head. It can 38

also estimate the inner and outer skull surfaces, and outer scalp surface, if you have good quality T1.

Author:

Sina Aslan, Ph.D.

Maintainer:

Flywheel Support <support@flywheel.io>

License:

Apache-2.0

Version:

1.0.0_5.0.7

URL:

https://github.com/saslan-7/super-bet2

Source:

https://github.com/flywheel-apps/fsl-superbet2

FSL: TOPUP correction for susceptibility induced distortions

Estimates a distortion correction field given one or more pairs of images with oppo site PE directions

Author:

FSL

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

0.0.3 0.0.2

URL:

https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup

Source:

https://github.com/flywheel-apps/fsl-topup

FSL: FSLMERGE - FMRIB Merge Tool (FSL v5.0)

FSLMERGE (FMRIB) concatenates image files into a single output. This concatena tion can be in time, or in X, Y or Z. All image dimensions (except for the one being concatenated over) must be the same in all input images. For example, this can be used to take multiple 3D files (eg as output by SPM) and create a single 4D image file. This Gear also supports the merger of diffusion data with bvec/bval files.

Author:

Analysis Group, FMRIB, Oxford, UK.

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

Apache-2.0

Version:

0.1.2 0.1.1 0.1

URL:

https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils

Source:

https://github.com/scitran-apps/fslmerge

Flywheel HeuDiConv

HeuDiConv-style BIDS curation on Flywheel. Flywheel HeuDiConv (or fw-heudiconv, pronounced /fwuː di kɑː n(v)/) is a Python-based toolkit that leverages the flexibility and comprehensiveness of HeuDiConv to curate neuroimaging data on Flywheel into a BIDS-valid format.

Author:

Tinashe Michael Tapera

Maintainer:

Tinashe Michael Tapera

License:

Other

Version:

0.1.15_0.1.0

URL:

https://github.com/PennBBL/fw-heudiconv/wiki

Source:

Gannet 3.0: Analysis of edited MRS data

Gannet is a software package designed for the analysis of edited magnetic reso nance spectroscopy (MRS) data. Gannet runs in Matlab and is available as code rath er than executables, empowering users to make local changes. Gannet is designed to run without user intervention, to remove operator variance from the quantification of edited MRS data. This Gear uses a compiled version from huawu02/gannet, which is modified to support latest generation GE P-Files, and is executed using the Matlab Compiler Runtime.

Author:

Richard Edden, et. al

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

Other

Version:

0.1.6_3.0 0.1.5_3.0 0.1.4_3.0 0.1.3_3.0 0.1.2_2.1 0.1.0_2.1

URL:

http://www.gabamrs.com/

Source:

https://github.com/scitran-apps/gannet

PHI Screen (Google DLP)

This is a gear for inspecting and redacting sensitive information from DICOM files via the Google DLP API.

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

Other

Version:

1.0.0

URL:

https://cloud.google.com/dlp

Source:

https://gitlab.com/flywheel-io/flywheel-apps/google-dlp-phi-screen

HCP: Diffusion Preprocessing Pipeline

Runs the diffusion preprocessing steps of the Human Connectome Project Minimal Preprocessing Pipeline described in Glasser et al. 2013. This includes correction for EPI distortion (using FSL topup), correction for motion and eddy-current distortion (using FSL eddy), and registration to subject anatomy. In addition, this Gear gener ates a QC mosaic. NOTE: This Gear requires that the HCP-Structural Gear has been 42

run - the output of which is used here. This Gear allows input of up to 4 diffusion ac quisitions.

Author:

Human Connectome Project

Maintainer:

Flywheel <support@flywheel.io>

License:

Other

Version:

1.0.2_4.3.0 1.0.1_4.0.1 0.2.0_4.0.1 0.1.5 0.1.4 0.1.3 0.1.2 0.1.0

URL:

https://github.com/Washington-University/Pipelines

Source:

https://github.com/flywheel-apps/hcp-diff

HCP: Functional Preprocessing Pipeline

Runs the functional preprocessing steps of the Human Connectome Project Minimal Preprocessing Pipeline described in Glasser et al. 2013. Currently, this Gear includes v4.0-alpha release of fMRIVolume and fMRISurface, as well as generating some help

ful QC images. NOTE: this Gear requires that the HCP structural preprocessing pipe line has been run, as the output of that pipeline must be provided as input to this Gear.

Author:

Human Connectome Project

Maintainer:

Flywheel <support@flywheel.io>

License:

Other

Version:

1.0.3_4.3.0_rc0 1.0.1_4.0.1 0.2.0_4.0.1 0.1.7 0.1.6 0.1.4 0.1.3 0.1.2 0.1.0 URL:

https://github.com/Washington-University/Pipelines

Source:

https://github.com/flywheel-apps/hcp-func

HCP: ICAFIX Functional Pipeline

Runs ICA-FIX denoising on functional data preprocessed according to the HCP Mini mal Preprocessing Pipeline. This Gear is based on scripts from the v4.0-alpha release of the ICAFIX, PostFix, and RestingStateStats pipelines. NOTE: This Gear requires that HCP-STRUCT and HCP-FUNC Gears have been run, as the outputs of those gears are required inputs here. Also note that more than 1 HCP-FUNC output can be provi ded as input.

Author:

Human Connectome Project

Maintainer:

Flywheel <support@flywheel.io>

License:

Other

Version:

0.2.0 0.1.7 0.1.6 0.1.5 0.1.4 0.1.3 0.1.2 0.1.0

URL:

https://github.com/Washington-University/Pipelines

Source:

https://github.com/flywheel-apps/hcp-icafix

HCP: Structural Preprocessing Pipeline

Runs the structural preprocessing steps of the Human Connectome Project Minimal Preprocessing Pipeline, described in Glasser et al. 2013. Currently this includes v4.0- alpha release of PreFreeSurfer, FreeSurfer, and PostFreeSurfer pipelines. This Gear al

so generates some helpful QC images. NOTE: This Gear is a prerequisite for other Gears in the HCP suite.

Author:

Human Connectome Project

Maintainer:

Flywheel <support@flywheel.io>

License:

Other

Version:

1.0.2_4.3.0 1.0.1_4.0.1 1.0.0_4.0.0 0.1.8 0.1.7 0.1.6 0.1.5 0.1.3 0.1.2 0.1.1 URL:

https://github.com/Washington-University/Pipelines

Source:

https://github.com/flywheel-apps/hcp-struct

HDFT Subsampled Diffusion Reconstruction

Computes a transformation of multi-shell diffusion weighted data to a set of Spheri cal Harmonic coefficients and outputs 4D Spherical Harmonic coefficient data. This is a first step in the Schneider Lab HDFT diffusion reconstruction process. See: Pa

thak, S. K., Fissell, C., Krishnaswamy, D., Aggarwal, S., Hachey, R., Schneider, W. (2015). Diffusion reconstruction by combining spherical harmonics and generalized q-sampling imaging. ISMRM, Toronto, Canada.

Author:

Schneider Lab, University of Pittsburgh

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

GPL-2.0

Version:

0.0.1

URL:

http://www.lrdc.pitt.edu/schneiderlab/

Source:

https://github.com/schlabhdft/ALDIT

Hierarchy Curator

Curates a container in the flywheel hierarchy given a python HierarchyCurator class. Using an implementation of the HierarchyCurator Class (provided as an input file (e.g., curator.py)) this gear is able to curate an entire project, walking down the hierar chy through project, subject, session, acquisition, analysis, and file containers.

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

2.1.0 1.1.0 1.0.0

URL:

https://gitlab.com/flywheel-io/flywheel-apps/hierarchy-curator

Source:

https://gitlab.com/flywheel-io/flywheel-apps/hierarchy-curator

Histogram Matching Intensity Standardization

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

0.1.0_4.13.0

URL:

https://gitlab.com/flywheel-io/flywheel-apps/intensity-standardization Source:

https://gitlab.com/flywheel-io/flywheel-apps/intensity-standardization

ME-ICA: Multi-Echo Independent Components Analysis

Multi-Echo Independent Components Analysis (ME-ICA) is a method for fMRI analy sis and denoising based on the T2* decay of BOLD signals, as measured using multi echo fMRI. ME-ICA decomposes multi-echo fMRI datasets into independent compo nents (ICs) using FastICA, then categorizes ICs as BOLD or noise using their BOLD and non-BOLD weightings (measured as Kappa and Rho values, respectively). Re moving non-BOLD weighted components robustly denoises data for motion, physiolo gy and scanner artifacts, in a simple and physically principled way. Pipeline includes: 1. Preprocess multi-echo datasets and apply multi-echo ICA based on spatial concat enation. 2. Calculation of motion parameters based on images with highest contrast. 3. Application of motion correction and coregistration parameters. 4. EPI preprocessing (temporal alignment, smoothing, etc) in appropriate order. 5. Compute PCA and ICA in conjunction with TE-dependence analysis.

Author:

Prantik Kundu

Maintainer:

Flywheel <support@flywheel.io>

License:

Other

Version:

0.3.8 0.3.4_3.2beta1 0.2.4 0.2.0 0.1.1 0.1.0

URL:

https://github.com/ME-ICA/me-ica/blob/master/README.meica

Source:

https://github.com/flywheel-apps/me-ica

FreeSurfer: MBIRN Defacer for structural MRI (mri-deface v1.22)

MBIRN Defacer for structural MRI (mri-deface v1.22). MRI_DEFACE (v1.22) from Free Surfer is a tool for removing identifiable facial features (eyes, nose, and mouth). This algorithm locates the subject's facial features and removes them without disturbing brain tissue. The algorithm was devised to work on T1-weighted anatomical MR data; it consumes NIfTI, DICOM, or MGH formats and produces a defaced anatomical im age in either NIfTI or MGH format. Please cite http://www.ncbi.nlm.nih.gov/pmc/arti cles/PMC2408762/ if using this tool in your work.

Author:

Amanda Bischoff-Grethe, et al.

Maintainer:

Flywheel <support@flywheel.io>

License:

GPL-2.0

Version:

0.3.0_1.22 0.2 0.1.2 0.1.1

URL:

https://surfer.nmr.mgh.harvard.edu/fswiki/mri_deface

Source:

https://github.com/flywheel-apps/mri-deface

MRIQC: No-reference image quality metrics for quality assess ment of MRI

MRIQC (v0.10.1) extracts no-reference IQMs (image quality metrics) from structural (T1w and T2w) and functional MRI (magnetic resonance imaging) data. Note, this gear only supports the generation of individual scan reports; group reports are not generated. Also note, for the auto-detection config option to work for this gear, the follow gears must be run beforehand: (1) dicom-mr-classifier then (2) dcm2niix (ver sion 0.3.1 or higher).

Author:

Oscar Esteban, Krzysztof F. Gorgolewski. Poldrack Lab, Psychology, CRN, Stanford University

Maintainer:

Flywheel <support@flywheel.io>

License:

BSD-3-Clause

Version:

0.7.0_0.15.1 0.6.4_0.15.1 0.6.3_0.11.0 0.6.2_0.11.0 0.6.1_0.11.0 0.6.0 0.5.1 0.5.0 0.4.1 0.4.0 0.3.3 0.3.2 0.3.1 0.3 0.2 0.1

URL:

https://github.com/poldracklab/mriqc

Source:

https://github.com/flywheel-apps/mriqc

MRtrix3: Preprocessing Pipeline

mrtrix3preproc runs the MRtrix3 preprocessing pipeline. It uses FSL's topup when the optional inverse phase encoded data are provided, otherwise the pipeline uses FSL's eddy tool. The pipeline can also perform de-noising, reslicing, and alignment to an anatomical image. Required inputs are diffusion NIfTI, BVEC, BVAL, and Anatomical (ACPC aligned) NIfTI.

Author:

MRtrix, FSL, and Brain-Life teams.

Maintainer:

Garikoitz Lerma-Usabiaga <glerma@stanford.edu>

License:

Other

Version:

1.0.2 1.0.0

URL:

https://mrtrix.readthedocs.io/en/latest/reference/scripts/dwipreproc.html#dwipreproc

Source:

https://github.com/scitran-apps/mrtrix3preproc

N4 Bias Correction

N4 Bias Field Correction.

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

0.1.0_2.3.5

URL:

https://gitlab.com/flywheel-io/flywheel-apps/n4-bias-correction

Source:

https://gitlab.com/flywheel-io/flywheel-apps/n4-bias-correction

NDMG (NeuroData's MR Graphs Package)

NeuroData's MR Graphs package, ndmg (pronounced "nutmeg"), is a turn-key pipeline which uses structural and diffusion MRI data to estimate multi-resolution connec tomes reliably and scalably.

Author:

Gregory Kiar, Eric W. Bridgeford, Joshua T. Vogelstein, et al.

Maintainer:

Derek Pisner <dpisner@utexas.edu>

License:

Apache-2.0

Version:

0.1.0_staging

URL:

https://github.com/neurodata/ndmg

Source:

https://github.com/flywheel-apps/ndmg

NIFTI to MIPS

Convert NIfTI file into PNG images using Maximum Intensity Projection(MIP) techni que

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

0.1.0

URL:

https://gitlab.com/flywheel-io/flywheel-apps/nifti-to-mips/

Source:

https://gitlab.com/flywheel-io/flywheel-apps/nifti-to-mips/

Atropos

A finite mixture modeling (FMM) segmentation approach with possibilities for

Author:

Yaroslav O. Halchenko

Maintainer:

Yaroslav O. Halchenko <debian@onerussian.com>

License:

BSD-3-Clause

Version:

0.1.dev.nipype.1.0.3.3 0.0.2.nipype.1.0.3.1

URL:

http://nipype.readthedocs.io/en/1.0.3/interfaces/generated/interfaces.ants/registration.html

Source:

https://github.com/yarikoptic/gearificator

BrainExtraction

Author:

Yaroslav O. Halchenko

Maintainer:

Yaroslav O. Halchenko <debian@onerussian.com>

License:

BSD-3-Clause

Version:

0.1.dev.nipype.1.0.3.3 0.1.dev.nipype.1.0.3.2

URL:

http://nipype.readthedocs.io/en/1.0.3/interfaces/generated/interfaces.ants/registration.html

Source:

https://github.com/yarikoptic/gearificator

CorticalThickness

Author:

Yaroslav O. Halchenko

Maintainer:

Yaroslav O. Halchenko <debian@onerussian.com>

License:

BSD-3-Clause

Version:

0.1.dev.nipype.1.0.3.2

URL:

http://nipype.readthedocs.io/en/1.0.3/interfaces/generated/interfaces.ants/registration.html

Source:

https://github.com/yarikoptic/gearificator

DenoiseImage

Author:

Yaroslav O. Halchenko

Maintainer:

Yaroslav O. Halchenko <debian@onerussian.com>

License:

BSD-3-Clause

Version:

0.1.dev.nipype.1.0.3.2

URL:

http://nipype.readthedocs.io/en/1.0.3/interfaces/generated/interfaces.ants/registration.html

Source:

https://github.com/yarikoptic/gearificator

KellyKapowski

Nipype Interface to ANTs' KellyKapowski, also known as DiReCT.

Author:

Yaroslav O. Halchenko

Maintainer:

Yaroslav O. Halchenko <debian@onerussian.com>

License:

BSD-3-Clause

Version:

0.1.dev.nipype.1.0.3.3 0.1.dev.nipype.1.0.3.2

URL:

http://nipype.readthedocs.io/en/1.0.3/interfaces/generated/interfaces.ants/registration.html

Source:

https://github.com/yarikoptic/gearificator

LaplacianThickness

Calculates the cortical thickness from an anatomical image

Author:

Yaroslav O. Halchenko

Maintainer:

Yaroslav O. Halchenko <debian@onerussian.com>

License:

BSD-3-Clause

Version:

0.2.dev1.nipype.1.1.7 0.1.dev.nipype.1.0.3.2

URL:

http://nipype.readthedocs.io/en/1.1.7/interfaces/generated/interfaces.ants/registration.html

Source:

https://github.com/yarikoptic/gearificator

N4BiasFieldCorrection

N4 is a variant of the popular N3 (nonparameteric nonuniform normalization)

Author:

Yaroslav O. Halchenko

Maintainer:

Yaroslav O. Halchenko <debian@onerussian.com>

License:

BSD-3-Clause

Version:

0.0.2.nipype.1.0.3.1

URL:

http://nipype.readthedocs.io/en/1.0.3/interfaces/generated/interfaces.ants/registration.html

Source:

https://github.com/yarikoptic/gearificator

PrepareFieldmap

Author:

Yaroslav O. Halchenko

Maintainer:

Yaroslav O. Halchenko <debian@onerussian.com>

License:

Other

Version:

0.1.dev.nipype.1.0.3.3

URL:

http://nipype.readthedocs.io/en/1.0.3/interfaces/generated/interfaces.ants/registration.html

Source:

https://github.com/yarikoptic/gearificator

FSL BET (Brain Extraction Tool)

FSL BET command for skull stripping

Author:

Yaroslav O. Halchenko

Maintainer:

Yaroslav O. Halchenko <debian@onerussian.com>

License:

Other

Version:

0.0.2.nipype.1.0.3.1

URL:

http://nipype.readthedocs.io/en/1.0.3/interfaces/generated/interfaces.ants/registration.html

Source:

https://github.com/yarikoptic/gearificator

FAST

FSL FAST command for segmentation and bias correction

Author:

Yaroslav O. Halchenko

Maintainer:

Yaroslav O. Halchenko <debian@onerussian.com>

License:

Other

Version:

0.1.dev.nipype.1.0.3.2 0.0.2.nipype.1.0.3.1

URL:

http://nipype.readthedocs.io/en/1.0.3/interfaces/generated/interfaces.ants/registration.html

Source:

https://github.com/yarikoptic/gearificator

Nobrainer

A framework for developing neural network models for 3D image processing.

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

Other

Version:

0.1.0

URL:

https://github.com/flywheel-apps/nobrainer-gear

Source:

https://github.com/neuronets/nobrainer

OpenSlide to PNG converter

OpenSlide: Uses the OpenSlide library to convert whole-slide image files to .png for viewing in Flywheel. Supported file types include Aperio (.svs, .tif), Hamamatsu (.ndpi, .vms, .vmu), Leica (.scn), MIRAX (.mrxs), Philips (.tiff), Sakura (.svslide), Trestle (.tif), Ventana (.bif, .tif), Generic tiled TIFF (.tif)

Author:

Adam Goode, M. Satyanarayanan, Carnegie Mellon University <https:// openslide.org/>

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

0.0.1_1.1.1

URL:

https://github.com/openslide/openslide-python

Source:

https://github.com/flywheel-apps/openslide-to-png

SciTran PAR/REC MR Classifier

Extract metadata from PAR/REC MR data generated by Philips MR scanners.

Author:

Michael Perry <lmperry@stanford.edu>

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

Apache-2.0

Version:

2.0.0 1.1.1 1.1.0 1.0.0 0.0.5 0.0.3

URL:

https://scitran.github.io

Source:

https://github.com/scitran-apps/parrec-mr-classifier

GE P-File Metadata Import and Classification

Extracts GE P-File header and generates JSON metadata (.metadata.json) which is saved in Flywheel on the P-File's info object. This gear also attempts to determine the P-File's classification (measurement, intent, etc.) using information about the se quence, as well as heuristics based upon the series description.

Author:

Michael Perry <lmperry@stanford.edu>

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

BSD-2-Clause

Version:

2.4.0_23ec2b6 2.3.2 2.3.1 2.3.0 2.2.0 2.1.0 2.0.0 1.8.0 1.7.1 1.7.0 1.6.1 1.6.0 1.5.2 1.5.1 1.5.0 1.4.0 1.3.2 1.3.1 1.3.0 1.2.1 1.2.0 1.1.1 1.1.0 1.0.0

URL:

https://cni.stanford.edu

Source:

https://github.com/cni/pfile-mr-classifier

Philips to ISMRM-RD Converter (philips_to_ismrmrd v0.1.0, ismrmrd v1.3.2)

The Philips to ISMRM-RD Convertor (philips_to_ismrmrd v0.1.0, ismrmrd v1.3.2) is used to convert data from Philips Raw file (.raw) to ISMRM-RD raw data format (.h5).

Author:

Souheil Inati, Michael Hansen, et al.

Maintainer:

Jennifer Reiter <jenniferreiter@invenshure.com>

License:

Other

Version:

0.1

URL:

https://github.com/ismrmrd/philips_to_ismrmrd

Source:

https://github.com/flywheel-apps/philips_to_ismrmrd

Pydeface Gear

A gear to remove facial structure from MRI images.

Author:

poldracklab

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

1.0.0

URL:

https://github.com/flywheel-apps/pydeface-gear

Source:

https://github.com/poldracklab/pydeface 61

pyradiomics

Uses pyRadiomics module to generate a .csv file of image features. Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

1.0.0

URL:

https://gitlab.com/flywheel-io/flywheel-apps/pyradiomics

Source:

https://pyradiomics.readthedocs.io/en/latest/index.html

DTIPREP: DWI Quality Assurance Report

DTIPrep performs a Study-specific Protocol based automatic pipeline for DWI/DTI quality control and preparation. This is both a GUI and command line tool. The con figurable pipeline includes image/diffusion information check, padding/Cropping of data, slice-wise, interlace-wise and gradient-wise intensity and motion check, head motion and Eddy current artifact correction, and DTI computing. Version 1.2.4

Author:

DTIPrep (Francois Budin <fbudin@unc.edu>)

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

Apache-2.0

Version:

0.0.2

URL:

https://www.nitrc.org/projects/dtiprep

Source:

https://github.com/scitran-apps/qa-dtiprep

Quality Assurance Report (fMRI)

Run QA metrics (displacement, signal spikes) to create a quality assurance report (png) for an fMRI NIfTI using modified CNI/NIMS code from @rfdougherty.

Author:

Robert F. Dougherty

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

Apache-2.0

Version:

0.1.7

URL:

https://github.com/cni/nims/blob/master/nimsproc/qa_report.py

Source:

https://github.com/scitran-apps/qa-report-fmri

QuickNAT Pytorch

Flywheel gear wrapper for QuickNAT_pytorch

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

Apache-2.0

Version:

0.1.0

URL:

https://github.com/flywheel-apps/quicknat-gear

Source:

https://github.com/ai-med/quickNAT_pytorch

NEUROPYTHY: Retinotopy Template Generation (Benson, et. al.)

Runs FreeSurfer's RECON-ALL and applies the V1, V2, and V3 anatomical template of retinotopy from Benson et al. (2014) as well as the ROI template of Wang et al. (2015) to the output images using the Neuropythy neuroscience library for Python by Noah C. Benson. * Note that this Gear does not use the original version of the Benson et al. template, but rather an updated version that has also been published on the website indicated in the original paper. If using this Gear in your work, please cite: Benson NC, Butt OH, Datta R, Radoeva PD, Brainard DH, Aguirre GK (2012) The retinotopic organi zation of striate cortex is well predicted by surface topology. Curr Biol22(21):2081-5.

Author:

Noah C. Benson <nben@nyu.edu>

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

GPL-2.0

Version:

0.1.0

URL:

https://github.com/noahbenson/neuropythy

Source:

https://github.com/scitran-apps/retinotopy-templates

ROI to NIfTI

This gear converts ROIs created in Flywheel's OHIF viewer to NIfTI files.

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

0.3.2 0.2.4 0.2.3 0.2.0 0.1.1 0.1.0

URL:

Source:

https://github.com/flywheel-apps/ROI2nix

SciTran: NIfTI Montage Creation Tool

Creates a montage (zip, or png) from a NIfTI file.

Author:

SciTran Team

Maintainer:

Michael Perry <lmperry@stanford.edu>

License:

Apache-2.0

Version:

1.4.0 1.3 1.2

URL:

https://github.com/scitran-apps/nifti-montage

Source:

https://github.com/scitran-apps/nifti-montage

Siemens to ISMRM-RD Converter (siemens_to_ismrmrd v1.0.1, ismrmrd v1.3.2)

The Siemens to ISMRM-RD Converter (siemens_to_ismrmrd v1.0.1, ismrmrd v1.3.2) is used to convert data from Siemens raw data format (.dat) to ISMRM-RD raw data for mat (.h5).

Author:

Souheil Inati, Michael Hansen, et al.

Maintainer:

Jennifer Reiter <jenniferreiter@invenshure.com>

License:

Other

Version:

0.1

URL:

https://github.com/ismrmrd/siemens_to_ismrmrd

Source:

https://github.com/flywheel-apps/siemens_to_ismrmrd

DICOM splitter

The DICOM splitter extracts embedded localizer DICOM frames and/or re-group DI COM frames in archive by specific DICOM tags provided by user.

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

MIT

Version:

1.1.2 1.1.1 1.1.0

URL:

https://gitlab.com/flywheel-io/flywheel-apps/splitter

Source:

https://gitlab.com/flywheel-io/flywheel-apps/splitter

Task tsv Converter

Converts log files to tsv task files as per bids specs

Author:

Harsha Kethineni

Maintainer:

Harsha Kethineni

License:

Other

Version:

0.1.9 0.1.5 0.1.4 0.1.3 0.1.2 0.1.10 0.1.1 0.1.0-1 0.1.0

URL:

Source:

WSI to dicom

This gear contains a tool that converts whole slide images (WSIs) to DICOM. To read the underlying whole slide images (WSIs), this tool relies on OpenSlide, which sup ports a variety of file formats.

Author:

Flywheel

Maintainer:

Flywheel <support@flywheel.io>

License:

Other

Version:

0.1.2_1.0.3 0.1.1_1.0.3

URL:

https://github.com/GoogleCloudPlatform/wsi-to-dicom-converter

Source:

https://github.com/flywheel-apps/fwgear-wsi-to-dicom-converter

XCPENGINE: pipeline for processing of structural and function al data.

The XCP imaging pipeline (XCP system) for preprocessing of structural and function al data.

Author:

Ted Satterthwaite

Maintainer:

Ted Satterthwaite

License:

Other

Version:

1.0631

URL:

https://xcpengine.readthedocs.io/

Source:

https://github.com/PennBBL/xcpEngine

{[{category.name}]}

Introduction

What is the Flywheel Gear Exchange?

What are the Benefits of the Exchange?

How do I contribute?

GEARS

AFNI: Brain Warp

AFQ: Automated Fiber Quantification

AFQ Pipeline: Automated Fiber 6uantNܪcatNon Pipeline (DTIInit + MRtrix3 + ET + LiFE + AFQ)

AFQ Pipeline SDK: Automated Fiber Quantification Processing Pipeline

All-Subject BIDS fMRIPrep: Run BIDS fMRIPrep on all subjects in the project

Gradient Anisotropic Diffusion denoising

ANTs Build Template Parallel

Apply Canonical Transform

BIDS fMRIPrep: A Robust Preprocessing Pipeline for fMRI Data

BIDS Freesurfer: Freesurfer recon-all BIDS App

BIDS MRIQC: Automatic prediction of quality and visual report ing of MRI scans in BIDS format

BIDS Pre-Curation

BIDS qsiprep

Bruker to NIfTI Converter

BXH-XCEDE-TOOLS: fMRI QA (v1.11.14)

BIDS-APP: C-PAC Configurable Pipeline for the Analysis of Connectomes)

CNI-DCM-CONVERT: DICOM Conversion Utility

CNI: DICOM MR Classifier

Apache-2.0

CNI: Quality Assurance Report (fMRI)

BIDS Curation

SciTran: DCM-CONVERT - DICOM conversion tool

DCM to MIPS

DCM2NII: v.4AUGUST2014

dcm2niix: DICOM to NIfTI conversion (with PyDeface)

Debug File Generator: Creating a 1 GB ܪQe

De-identified Export

Dicom Fixer

SciTran: DICOM MR Classifier

Dicom QC

DCMTK: DICOM Send

VISTALAB: DTI Error

VISTALAB: dtiInit - Diffusion Data Initialization Pipeline

dtiInit: Diffusion Map Generation

VISTA Lab: DWI Flip BVEC

SCITRAN: DWI Split Shells

Brain Vision EEG Classifier

Export ROIs

CMRR: Extract CMRR Physio

File Classifier

File metadata importer

Flaudit: Flywheel Audit

VPNL: fLoc - Face Localizer Analysis Pipeline

Flywheel Example Gear

fMRIPREP: A Robust Preprocessing Pipeline for fMRI Data

FreeSurfer 7.2.0: run recon-all

FSL-ANAT - Anatomical Processing Pipeline

FSL: Brain Extraction Tool (BET2)

FSL: FMRIB Automated Segmentation Tool (FAST4, v5.0.9)

FSL: FEAT - fMRI preprocessing (v6.0)

FSL fslhd

FSL: fslreorient2std - Reorient Image to Standard Template

FSL: SIENA - Longitudinal analysis of brain change

FSL: SIENAX - Brain tissue volume, normalised for subject head size

FSL fslstats

FSL: SUPER Brain Extraction Tool (BET2)

FSL: TOPUP correction for susceptibility induced distortions

FSL: FSLMERGE - FMRIB Merge Tool (FSL v5.0)

Flywheel HeuDiConv

Gannet 3.0: Analysis of edited MRS data

PHI Screen (Google DLP)

HCP: Diffusion Preprocessing Pipeline

HCP: Functional Preprocessing Pipeline

HCP: ICAFIX Functional Pipeline

HCP: Structural Preprocessing Pipeline

HDFT Subsampled Diffusion Reconstruction

Hierarchy Curator

Histogram Matching Intensity Standardization

ME-ICA: Multi-Echo Independent Components Analysis

FreeSurfer: MBIRN Defacer for structural MRI (mri-deface v1.22)

MRIQC: No-reference image quality metrics for quality assess ment of MRI

MRtrix3: Preprocessing Pipeline

N4 Bias Correction

NDMG (NeuroData's MR Graphs Package)