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BRIDGE Lab Documentation
  • BRIDGE Lab Documentation
  • 📘General
    • About Us
    • Onboarding
      • First Steps
      • Research Specialist Training
      • Project Coordinator Training
    • Misc
      • How to do misc things
      • Burning Scans to a Disc
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      • Printing
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  • 🖥️Tech
    • Setting Up Meetings in the Conference Room
    • Effective Troubleshooting
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    • Arthur
    • Servers
      • Connecting to an External Server
    • Bash 101
      • What is Bash?
      • Bash Examples
      • How to add elements to your bash profile
    • git and Github
  • 🩻Image Acquisition
    • ViSTa
  • 🗃️Data Organization
    • BIDs Data Formatting
    • MRI Data Organization
  • 🖼️Image Analysis
    • Image QC
      • Raw Data QC
        • Diffusion QC
        • T1/T2 QC
        • ViSTa QC
        • Spectroscopy QC
      • PyDesigner QC
    • Project Lifecycle
    • General Concepts
    • Raw Data
    • Preprocessing
      • Denoising UNI MP2RAGE Images
      • PyDesigner
      • ViSTa
    • Native Space Analysis
      • TractSeg
        • TractSeg + Within-Subject Registration
      • Segmentation
        • LST
        • Freesurfer
        • NOMIS
    • Registration
      • DTI-TK
      • TBSS
      • Coordinate Systems
    • Other Pipelines
    • Archiving
  • 📊Data Viz and Stats
    • Plotting in R
  • 📚Imaging Library
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On this page
  • 1. Downloading scan data from CBIHome
  • 2. Unzipping dicom folders
  • 3. Sorting dicoms by sequence type
  • 4. Converting dicoms to nifti format
  • 5. Sorting niftis
  1. Image Analysis

Raw Data

The first step in any image analysis pipeline is actually acquiring the data. After data is acquired at the scanner, it is pushed to a server called CBIHome.

Last updated 1 year ago

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1. Downloading scan data from CBIHome

  • Open Filezilla (or the FTP software of your choice)

  • Enter the address to CBIHome (sftp://cbihome.musc.edu), as well as your NetID and password

  • Navigate to the top level directory, then to MRData, then to your PI's folder, then to the study folder of your choice

  • Download scans of your choice

    • Note that you can set the output destination for your downloads - set this to the folder on your desktop in which you will organize your newly downloaded data

2. Unzipping dicom folders

  • You can unzip newly downloaded dicom folders in one of two ways:

    • Manually unzip the folders by double clicking (on Mac) or right clicking and extracting (on Windows)

    • Automate the unzipping process with a command line tool such as the bash tool 'unzip'.

      • To use bash unzip, open a Terminal and pass the following commands:

cd [filezilla download directory]
find . -type f -name '*.zip' -exec unzip {} \;

3. Sorting dicoms by sequence type

Our study dicoms are typically sorted by series/sequence name - the name of the type of scan to which the dicoms belong. Below is the simple-dicom-sort command to sort a set of dicoms. This can be placed into a for-loop in bash to sort many subjects' dicoms at once.

dicom_sort [input] [output] %SeriesName%

4. Converting dicoms to nifti format

dcm2niix -o [output folder] -f %p [input folder]

5. Sorting niftis

For ease of access and use, we also organize our nifti files. These are typically organized into three main folders - dwi, anat, other - as well as folders with sequences specific to each study (eg. fmri, vista, etc).

dwi - all diffusion data (DTI, DKI, FBI, etc)

anat - all structural data (T1, T2, etc)

other - all headcoil and extraneous data generated by the scanner as accessory to other scans

How you decide to sort niftis will vary by study. However, generally niftis can easily be sorted in bash with a command similar to:

mv [input path]/T* [output path]/anat
mv [input path]/D* [output path]/dwi

These examples use wildcards (*) to select all files that begin with T or D to move to the anat or dwi folders respectively.

See for more details

In order to access dicoms in a manageable way, they need to be sorted. This is done by looking at dicom meta data and sorting each dicom file based on a piece of meta data. There are many tools that allow a user to do this, such as simple-dicom-sort ( see for more details).

Similar to dicom sort, dicom conversion is done by matching dicoms based on meta data and then converting dicom sets into a single nifti file. The go-to tool for this task is dcm2niix, a tool that is packaged along with MRIcron ( see for more details).

🖼️
https://bridge-lab.gitbook.io/docs/general/onboarding/research-specialist-training#id-5.-workspace-setup
https://bridge-lab.gitbook.io/docs/general/onboarding/research-specialist-training#id-5.-workspace-setup
https://bridge-lab.gitbook.io/docs/general/onboarding/research-specialist-training#id-5.-workspace-setup
You can read more about this process via the linked PowerPoint made by the CBI