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        1. Creation of computerized models of the brain from magnetic resonance imaging (MRI) data. [[FsTutorial/MorphAndRecon|link]]         1. Creation of computerized models of the brain from magnetic resonance imaging (MRI) data.
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        2. Processing of functional magnetic resonance imaging (fMRI) data. [[FsFastTutorial|link]]         2. Processing of functional magnetic resonance imaging (fMRI) data.
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        3. Measuring a number of morphometric properties of the brain including cortical thickness and regional volumes. [[https://surfer.nmr.mgh.harvard.edu/ftp/articles/fischl00-cortical-thickness.pdf|link]]         3. Measuring a number of morphometric properties of the brain including cortical thickness and regional volumes.
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        4. Intersubject averaging of structural and functional data using a procedure that aligns individuals based on their cortical folding patterns for optimal alignment of homologous neural regions. [[https://surfer.nmr.mgh.harvard.edu/ftp/articles/fischl99-morphing.pdf|link]]         4. Intersubject averaging of structural and functional data using a procedure that aligns individuals based on their cortical folding patterns for optimal alignment of homologous neural regions.
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        FreeSurfer consumes a lot of processor time, memory resources and disk space, so it is recommended to run FreeSurfer on as powerful a machine as you have available. For example, at MGH we typically run Linux CentOS 4 on 2.5GHz dual processor AMD Opterons with 4 to 8 GB of DDR SDRAM, and 250GB of disk space.         FreeSurfer consumes a lot of processor time, memory resources and disk space, so it is recommended to run FreeSurfer on as powerful a machine as you have available. For example, at MGH we typically run Linux CentOS 5 on 2.5GHz quad processor Intel Xeon with 4 to 8 GB of DDR SDRAM, and 500GB of disk space.
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        The processing procedures for the creation of cortical models require good quality T1 weighted MRI data, such as a Siemens MPRAGE ([[http://www.nmr.mgh.harvard.edu/~andre/|examples of appropriate Siemens scanner protocols]]) or GE SPGR sequence with approximately 1mm^3^ resolution (although a variety of quality data sets can be processed with additional manual intervention). Thickness should not exceed 1.5mm (~1mm^3 is ideal). The best FreeSurfer processing results come from scans having excellent gray/white matter contrast.         The processing procedures for the creation of cortical models require good quality T1 weighted MRI data, such as a Siemens MPRAGE ([[http://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferWiki?action=AttachFile&do=get&target=FreeSurfer_Suggested_Morphometry_Protocols.pdf|examples of appropriate Siemens scanner protocols]]) or GE SPGR sequence with approximately 1mm^3^ resolution (although a variety of quality data sets can be processed with additional manual intervention). Thickness should not exceed 1.5mm (~1mm^3 is ideal). [[http://adni.loni.ucla.edu/methods/documents/mri-protocols/|The protocols specified for the ADNI project are excellent examples to follow.]] The best FreeSurfer processing results come from scans having excellent gray/white matter contrast.
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        [[attachment:example_scans.tif|Examples of different quality scans (click to download TIFF image):]] {{attachment:example_scan_A.jpg}}
{{attachment:example_scan_B.jpg}}
{{attachment:example_scan_C.jpg}}
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        There is a variety of documentation about the use of FreeSurfer contained in the FreeSurfer wiki including [[DownloadAndInstall|installation of the software]], [[FsTutorial|tutorials]], [[FsTutorial/Data|sample data]], and [[WorkFlows|work flows]] providing step by step guides to performing specific tasks.         There is a variety of documentation about the use of FreeSurfer contained in the FreeSurfer wiki including [[TeachYourselfFreeSurfer|a collection of talks and tutorials to teach yourself how to use FreeSurfer]], [[DownloadAndInstall|installation of the software]], [[FsTutorial|tutorials]], [[FsTutorial/Data|sample data]], and [[WorkFlows|work flows]] providing step by step guides to performing specific tasks.
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            1. Read the introductory material on FreeSurfer from past lectures:
              * [[attachment:FSL_anatomical_stream.pdf|slides 1 (BF)]]
              * [[attachment:fsintro.pdf|slides 2 (DG)]]
              * [[attachment:fsrecon.pdf|slides 3 (DG)]]
            1. Read the links to slides marked as type 'talk' in the tutorials: [[FsTutorial|click here]]
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            2. Read the background material: [[ArticlesSlidesAndPosters|click here]]             2. Read the background material: [[https://www.zotero.org/freesurfer|click here]]
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            6. Peruse the wiki to get a fuller knowledge of all of the available processing procedures in the FreeSurfer software package             6. Follow the reconstruction workflow page: [[RecommendedReconstruction|click here]]
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            7. Peruse the wiki to get a fuller knowledge of all of the available processing procedures in the FreeSurfer software package
            8. Process your own data with a command such as this:
                    {{{
recon-all \
  -i <one slice in the anatomical dicom series> \
  -s <subject id that you make up> \
  -sd <directory to put the subject folder in> \
  -all
   }}}
where the input (-i) file is a single file representing a T1-weighted data set. If you have DICOM images, you must find a file in the T1 series to pass. You can do this with the dcmunack command.
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There is also a [[UserContributions_2fFAQ|FAQ]] There is also a [[UserContributions/FAQ|FAQ]]

top

FreeSurfer Beginners Guide

FreeSurfer is a freely available software package developed by investigators at the Athinoula A. Martinos Center for Biomedical Imaging used for a number of procedures including:

  1. Creation of computerized models of the brain from magnetic resonance imaging (MRI) data.
  2. Processing of functional magnetic resonance imaging (fMRI) data.
  3. Measuring a number of morphometric properties of the brain including cortical thickness and regional volumes.
  4. Intersubject averaging of structural and functional data using a procedure that aligns individuals based on their cortical folding patterns for optimal alignment of homologous neural regions.

Machine Requirements

  • To run FreeSurfer, you will need either a PC running Linux or a Macintosh running OS X.

    FreeSurfer consumes a lot of processor time, memory resources and disk space, so it is recommended to run FreeSurfer on as powerful a machine as you have available. For example, at MGH we typically run Linux CentOS 5 on 2.5GHz quad processor Intel Xeon with 4 to 8 GB of DDR SDRAM, and 500GB of disk space.

    See SystemRequirements for more info.

Data Requirements

example_scan_A.jpg example_scan_B.jpg example_scan_C.jpg

  • A - excellent GM/WM contrast, acquired with MP-RAGE pulse-sequence protocol

  • B - very good GM/WM contrast, acquired with MP-RAGE pulse-sequence protocol

  • C - good GM/WM contrast, acquired with SPGR pulse-sequence protocol; not as good GM/WM contrast as the MP-RAGE acquisitions; low bandwidth causes some temporal lobe artifacts (brightening of the gray matter)

Getting Started

where the input (-i) file is a single file representing a T1-weighted data set. If you have DICOM images, you must find a file in the T1 series to pass. You can do this with the dcmunack command.

An active e-mail list is available to answer specific questions about processing procedures.

There is also a FAQ

FreeSurferBeginnersGuide (last edited 2020-04-10 10:31:15 by LeahMorgan)