【MRtrix】 MRtrixを用いた拡散テンソルイメージング: DTI


1. 目的
2. コマンド
3. 使用例
3.1. 前準備
3.2. テンソルの推定(コマンド:dwi2tensor
3.3. 拡散定量値の算出(コマンド:tensor2metric


1. 目的

  • MRtrixを用いた拡散テンソルイメージング: DTI

2. コマンド

MRtrixを用いて、拡散テンソルイメージング(DTI)をするには、dwi2tensortensor2metricコマンドを用いる。

dwi2tensor拡散MRI画像からテンソルを推定するコマンドで、tensor2metric推定したテンソルから拡散定量値を算出するコマンドである。

dwi2tensorのヘルプは、次の通り。

クリックして展開
SYNOPSIS

     Diffusion (kurtosis) tensor estimation

USAGE

     dwi2tensor [ options ] dwi dt

        dwi          the input dwi image.

        dt           the output dt image.


DESCRIPTION

     By default, the diffusion tensor (and optionally its kurtosis) is fitted
     to the log-signal in two steps: firstly, using weighted least-squares
     (WLS) with weights based on the empirical signal intensities; secondly, by
     further iterated weighted least-squares (IWLS) with weights determined by
     the signal predictions from the previous iteration (by default, 2
     iterations will be performed). This behaviour can be altered in two ways:

     * The -ols option will cause the first fitting step to be performed using
     ordinary least-squares (OLS); that is, all measurements contribute equally
     to the fit, instead of the default behaviour of weighting based on the
     empirical signal intensities.

     * The -iter option controls the number of iterations of the IWLS
     prodedure. If this is set to zero, then the output model parameters will
     be those resulting from the first fitting step only: either WLS by
     default, or OLS if the -ols option is used in conjunction with -iter 0.

     The tensor coefficients are stored in the output image as follows:
     volumes 0-5: D11, D22, D33, D12, D13, D23

     If diffusion kurtosis is estimated using the -dkt option, these are stored
     as follows:
     volumes 0-2: W1111, W2222, W3333
     volumes 3-8: W1112, W1113, W1222, W1333, W2223, W2333
     volumes 9-11: W1122, W1133, W2233
     volumes 12-14: W1123, W1223, W1233

OPTIONS

  -ols
     perform initial fit using an ordinary least-squares (OLS) fit (see
     Description).

  -mask image
     only perform computation within the specified binary brain mask image.

  -b0 image
     the output b0 image.

  -dkt image
     the output dkt image.

  -iter integer
     number of iterative reweightings for IWLS algorithm (default: 2) (see
     Description).

  -predicted_signal image
     the predicted dwi image.

DW gradient table import options

  -grad file
     Provide the diffusion-weighted gradient scheme used in the acquisition in
     a text file. This should be supplied as a 4xN text file with each line is
     in the format [ X Y Z b ], where [ X Y Z ] describe the direction of the
     applied gradient, and b gives the b-value in units of s/mm^2. If a
     diffusion gradient scheme is present in the input image header, the data
     provided with this option will be instead used.

  -fslgrad bvecs bvals
     Provide the diffusion-weighted gradient scheme used in the acquisition in
     FSL bvecs/bvals format files. If a diffusion gradient scheme is present in
     the input image header, the data provided with this option will be instead
     used.

Standard options

  -info
     display information messages.

  -quiet
     do not display information messages or progress status; alternatively,
     this can be achieved by setting the MRTRIX_QUIET environment variable to a
     non-empty string.

  -debug
     display debugging messages.

  -force
     force overwrite of output files (caution: using the same file as input and
     output might cause unexpected behaviour).

  -nthreads number
     use this number of threads in multi-threaded applications (set to 0 to
     disable multi-threading).

  -config key value  (multiple uses permitted)
     temporarily set the value of an MRtrix config file entry.

  -help
     display this information page and exit.

  -version
     display version information and exit.

tensor2metricのヘルプは、次の通り。

クリックして展開
SYNOPSIS

     Generate maps of tensor-derived parameters

USAGE

     tensor2metric [ options ] tensor

        tensor       the input tensor image.


OPTIONS

  -adc image
     compute the mean apparent diffusion coefficient (ADC) of the diffusion
     tensor. (sometimes also referred to as the mean diffusivity (MD))

  -fa image
     compute the fractional anisotropy (FA) of the diffusion tensor.

  -ad image
     compute the axial diffusivity (AD) of the diffusion tensor. (equivalent to
     the principal eigenvalue)

  -rd image
     compute the radial diffusivity (RD) of the diffusion tensor. (equivalent
     to the mean of the two non-principal eigenvalues)

  -cl image
     compute the linearity metric of the diffusion tensor. (one of the three
     Westin shape metrics)

  -cp image
     compute the planarity metric of the diffusion tensor. (one of the three
     Westin shape metrics)

  -cs image
     compute the sphericity metric of the diffusion tensor. (one of the three
     Westin shape metrics)

  -value image
     compute the selected eigenvalue(s) of the diffusion tensor.

  -vector image
     compute the selected eigenvector(s) of the diffusion tensor.

  -num sequence
     specify the desired eigenvalue/eigenvector(s). Note that several
     eigenvalues can be specified as a number sequence. For example, '1,3'
     specifies the principal (1) and minor (3) eigenvalues/eigenvectors
     (default = 1).

  -modulate choice
     specify how to modulate the magnitude of the eigenvectors. Valid choices
     are: none, FA, eigval (default = FA).

  -mask image
     only perform computation within the specified binary brain mask image.

Standard options

  -info
     display information messages.

  -quiet
     do not display information messages or progress status; alternatively,
     this can be achieved by setting the MRTRIX_QUIET environment variable to a
     non-empty string.

  -debug
     display debugging messages.

  -force
     force overwrite of output files (caution: using the same file as input and
     output might cause unexpected behaviour).

  -nthreads number
     use this number of threads in multi-threaded applications (set to 0 to
     disable multi-threading).

  -config key value  (multiple uses permitted)
     temporarily set the value of an MRtrix config file entry.

  -help
     display this information page and exit.

  -version
     display version information and exit.

DTI拡散定量値(FA, MD, AD, RD, カラーFA)を計算するための基本的な使い方は、以下の通り。

dwi2tensor <入力画像> <出力画像>
tensor2metric -fa <出力画像> -adc <出力画像> -ad <出力画像> -rd <出力画像> -vec <出力画像> tensor.mif

3. 使用例

3.1. 前準備

まず、次のファイルを用意する。

.
├── DWI.nii.gz  # 拡散MRI
├── DWI_mask.nii.gz
├── bvals  # b-values
├── bvecs  # b-vectors
└── headers.json  # ヘッダー情報の入ったJSONファイル

こちらの記事を参考に、拡散MRI(DWI.nii.gz)とそのMPG軸情報(bvecs, bvals)とヘッダー情報(headers.json)をまとめて、MIF形式(DWI.mif)に変換する。

mrconvert -fslgrad bvecs bvals -json_import headers.json DWI.nii.gz DWI.mif

3.2. テンソルの推定(コマンド:dwi2tensor

ファイルの用意ができたら、dwi2tensorを次のように実行する

dwi2tensor DWI.mif tensor.mif

mrinfoを使って「tensor.mif」の情報を確認すると、6 volumesのデータであることが分かる。

mrinfo tensor.mif
************************************************
Image name:          "tensor.mif"
************************************************
  Dimensions:        130 x 130 x 82 x 6
  Voxel size:        1.76923 x 1.76923 x 1.8 x 1
  Data strides:      [ -1 2 3 4 ]
  Format:            MRtrix
  Data type:         32 bit float (little endian)
  Intensity scaling: offset = 0, multiplier = 1
  Transform:                    1           0           0        -109
                               -0           1           0      -103.7
                               -0           0           1      -58.57

それぞれのボリュームは、各方向の拡散係数に相当する。

The tensor coefficients are stored in the output image as follows:
volumes 0-5: D11, D22, D33, D12, D13, D23

3.3. 拡散定量値の算出(コマンド:tensor2metric

先程推定した、「tensor.mif」を使って拡散定量値を算出する。

tensor2metric -fa FA.mif -adc MD.mif -ad AD.mif -rd RD.mif -vec color_FA.mif tensor.mif

DTIの各拡散定量値画像は、以下。

【MRtrix】MRtrixを用いた拡散MRIの前処理 ~歪み・頭の動き・渦電流の補正~


1. 目的
2. コマンド
3. 使用例
3.1. 前準備
3.2. 歪み補正と頭の動き補正


1. 目的

  • MRtrixを用いた拡散MRIの前処理(歪み・頭の動き・渦電流の補正)

2. コマンド

MRtrixを用いて、拡散MRIの歪み・頭の動き・渦電流を補正するには、dwifslpreprocを用いる(古いMRtrixバージョンではdwipreproc)。

dwipreprocは、FSLtopupeddyを用いるので、前もってFSLをインストールしておく必要がある。

dwifslpreprocのヘルプは、次の通り。

クリックして展開
SYNOPSIS

     Perform diffusion image pre-processing using FSL's eddy tool; including
     inhomogeneity distortion correction using FSL's topup tool if possible

USAGE

     dwifslpreproc [ options ] input output

        input        The input DWI series to be corrected

        output       The output corrected image series

DESCRIPTION

     This script is intended to provide convenience of use of the FSL software
     tools topup and eddy for performing DWI pre-processing, by encapsulating
     some of the surrounding image data and metadata processing steps. It is
     intended to simply these processing steps for most commonly-used DWI
     acquisition strategies, whilst also providing support for some more exotic
     acquisitions. The "example usage" section demonstrates the ways in which
     the script can be used based on the (compulsory) -rpe_* command-line
     options.

     The "-topup_options" and "-eddy_options" command-line options allow the
     user to pass desired command-line options directly to the FSL commands
     topup and eddy. The available options for those commands may vary between
     versions of FSL; users can interrogate such by querying the help pages of
     the installed software, and/or the FSL online documentation: (topup)
     https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup/TopupUsersGuide ; (eddy)
     https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/eddy/UsersGuide

     The script will attempt to run the CUDA version of eddy; if this does not
     succeed for any reason, or is not present on the system, the CPU version
     will be attempted instead. By default, the CUDA eddy binary found that
     indicates compilation against the most recent version of CUDA will be
     attempted; this can be over-ridden by providing a soft-link "eddy_cuda"
     within your path that links to the binary you wish to be executed.

     Note that this script does not perform any explicit registration between
     images provided to topup via the -se_epi option, and the DWI volumes
     provided to eddy. In some instances (motion between acquisitions) this can
     result in erroneous application of the inhomogeneity field during
     distortion correction. Use of the -align_seepi option is advocated in this
     scenario, which ensures that the first volume in the series provided to
     eddy is also the first volume in the series provided to eddy, guaranteeing
     alignment. But a prerequisite for this approach is that the image contrast
     within the images provided to the -se_epi option must match the b=0 volumes
     present within the input DWI series: this means equivalent TE, TR and flip
     angle (note that differences in multi-band factors between two acquisitions
     may lead to differences in TR).

EXAMPLE USAGES

     A basic DWI acquisition, where all image volumes are acquired in a single
     protocol with fixed phase encoding:
       $ dwifslpreproc DWI_in.mif DWI_out.mif -rpe_none -pe_dir ap -readout_time 0.55
     Due to use of a single fixed phase encoding, no EPI distortion correction
     can be applied in this case.

     DWIs all acquired with a single fixed phase encoding; but additionally a
     pair of b=0 images with reversed phase encoding to estimate the
     inhomogeneity field:
       $ mrcat b0_ap.mif b0_pa.mif b0_pair.mif -axis 3; dwifslpreproc DWI_in.mif DWI_out.mif -rpe_pair -se_epi b0_pair.mif -pe_dir ap -readout_time 0.72 -align_seepi
     Here the two individual b=0 volumes are concatenated into a single 4D image
     series, and this is provided to the script via the -se_epi option. Note
     that with the -rpe_pair option used here, which indicates that the SE-EPI
     image series contains one or more pairs of b=0 images with reversed phase
     encoding, the FIRST HALF of the volumes in the SE-EPI series must possess
     the same phase encoding as the input DWI series, while the second half are
     assumed to contain the opposite phase encoding direction but identical
     total readout time. Use of the -align_seepi option is advocated as long as
     its use is valid (more information in the Description section).

     All DWI directions & b-values are acquired twice, with the phase encoding
     direction of the second acquisition protocol being reversed with respect to
     the first:
       $ mrcat DWI_lr.mif DWI_rl.mif DWI_all.mif -axis 3; dwifslpreproc DWI_all.mif DWI_out.mif -rpe_all -pe_dir lr -readout_time 0.66
     Here the two acquisition protocols are concatenated into a single DWI
     series containing all acquired volumes. The direction indicated via the
     -pe_dir option should be the direction of phase encoding used in
     acquisition of the FIRST HALF of volumes in the input DWI series; ie. the
     first of the two files that was provided to the mrcat command. In this
     usage scenario, the output DWI series will contain the same number of image
     volumes as ONE of the acquired DWI series (ie. half of the number in the
     concatenated series); this is because the script will identify pairs of
     volumes that possess the same diffusion sensitisation but reversed phase
     encoding, and perform explicit recombination of those volume pairs in such
     a way that image contrast in regions of inhomogeneity is determined from
     the stretched rather than the compressed image.

     Any acquisition scheme that does not fall into one of the example usages
     above:
       $ mrcat DWI_*.mif DWI_all.mif -axis 3; mrcat b0_*.mif b0_all.mif -axis 3; dwifslpreproc DWI_all.mif DWI_out.mif -rpe_header -se_epi b0_all.mif -align_seepi
     With this usage, the relevant phase encoding information is determined
     entirely based on the contents of the relevant image headers, and
     dwifslpreproc prepares all metadata for the executed FSL commands
     accordingly. This can therefore be used if the particular DWI acquisition
     strategy used does not correspond to one of the simple examples as
     described in the prior examples. This usage is predicated on the headers of
     the input files containing appropriately-named key-value fields such that
     MRtrix3 tools identify them as such. In some cases, conversion from DICOM
     using MRtrix3 commands will automatically extract and embed this
     information; however this is not true for all scanner vendors and/or
     software versions. In the latter case it may be possible to manually
     provide these metadata; either using the -json_import command-line option
     of dwifslpreproc, or the -json_import or one of the -import_pe_* command-
     line options of MRtrix3's mrconvert command (and saving in .mif format)
     prior to running dwifslpreproc.

OPTIONS

  -pe_dir PE
     Manually specify the phase encoding direction of the input series; can be a
     signed axis number (e.g. -0, 1, +2), an axis designator (e.g. RL, PA, IS),
     or NIfTI axis codes (e.g. i-, j, k)

  -readout_time time
     Manually specify the total readout time of the input series (in seconds)

  -se_epi image
     Provide an additional image series consisting of spin-echo EPI images,
     which is to be used exclusively by topup for estimating the inhomogeneity
     field (i.e. it will not form part of the output image series)

  -align_seepi
     Achieve alignment between the SE-EPI images used for inhomogeneity field
     estimation, and the DWIs (more information in Description section)

  -json_import file
     Import image header information from an associated JSON file (may be
     necessary to determine phase encoding information)

  -topup_options " TopupOptions"
     Manually provide additional command-line options to the topup command
     (provide a string within quotation marks that contains at least one space,
     even if only passing a single command-line option to topup)

  -eddy_options " EddyOptions"
     Manually provide additional command-line options to the eddy command
     (provide a string within quotation marks that contains at least one space,
     even if only passing a single command-line option to eddy)

  -eddy_mask image
     Provide a processing mask to use for eddy, instead of having dwifslpreproc
     generate one internally using dwi2mask

  -eddy_slspec file
     Provide a file containing slice groupings for eddy's slice-to-volume
     registration

  -eddyqc_text directory
     Copy the various text-based statistical outputs generated by eddy, and the
     output of eddy_qc (if installed), into an output directory

  -eddyqc_all directory
     Copy ALL outputs generated by eddy (including images), and the output of
     eddy_qc (if installed), into an output directory

Options for specifying the acquisition phase-encoding design; note that one of the -rpe_* options MUST be provided

  -rpe_none
     Specify that no reversed phase-encoding image data is being provided; eddy
     will perform eddy current and motion correction only

  -rpe_pair
     Specify that a set of images (typically b=0 volumes) will be provided for
     use in inhomogeneity field estimation only (using the -se_epi option)

  -rpe_all
     Specify that ALL DWIs have been acquired with opposing phase-encoding

  -rpe_header
     Specify that the phase-encoding information can be found in the image
     header(s), and that this is the information that the script should use

Options for importing the diffusion gradient table

  -grad GRAD
     Provide the diffusion gradient table in MRtrix format

  -fslgrad bvecs bvals
     Provide the diffusion gradient table in FSL bvecs/bvals format

Options for exporting the diffusion gradient table

  -export_grad_mrtrix grad
     Export the final gradient table in MRtrix format

  -export_grad_fsl bvecs bvals
     Export the final gradient table in FSL bvecs/bvals format

Additional standard options for Python scripts

  -nocleanup
     do not delete intermediate files during script execution, and do not delete
     scratch directory at script completion.

  -scratch /path/to/scratch/
     manually specify the path in which to generate the scratch directory.

  -continue <ScratchDir> <LastFile>
     continue the script from a previous execution; must provide the scratch
     directory path, and the name of the last successfully-generated file.

Standard options

  -info
     display information messages.

  -quiet
     do not display information messages or progress status. Alternatively, this
     can be achieved by setting the MRTRIX_QUIET environment variable to a non-
     empty string.

  -debug
     display debugging messages.

  -force
     force overwrite of output files.

  -nthreads number
     use this number of threads in multi-threaded applications (set to 0 to
     disable multi-threading).

  -config key value  (multiple uses permitted)
     temporarily set the value of an MRtrix config file entry.

  -help
     display this information page and exit.

  -version
     display version information and exit.

基本的な使い方は、以下の通り。

dwifslpreproc <入力画像> <出力画像> [オプション]

3. 使用例

3.1. 前準備

位相エンコード方向を、APとPAそれぞれで撮像したデータがあったとする。DICOM形式からNIfTI形式に変換する方法は、以下の記事を参考にするとよい。

.
├── DWI_AP.nii.gz  # DW images (PE: AP)
├── DWI_PA.nii.gz  # DW images (PE: PA)
├── bvals_AP  # b-values (PE: AP)
├── bvals_PA  # b-values (PE: PA)
├── bvecs_AP  # b-vectors (PE: AP)
├── bvecs_PA  # b-vectors (PE: PA)
├── headers_AP.json  # DICOM headers (PE: AP)
└── headers_PA.json  # DICOM headers (PE: PA)

まず、こちらの記事を参考に、拡散MRI(DWI.nii.gz)とそのMPG軸情報(bvecs, bvals)とヘッダー情報(headers.json)をまとめて、MIF形式(DWI.mif)に変換する。

mrconvert -fslgrad bvecs_AP bvals_AP -json_import headers_AP.json DWI_AP.nii.gz DWI_AP.mif  # PE: AP
mrconvert -fslgrad bvecs_PA bvals_PA -json_import headers_PA.json DWI_PA.nii.gz DWI_PA.mif  # PE: PA

次に、mrcatを使ってDWI_AP.mifとDWI_PA.mifをひとつの画像(DWI_all.mif)にまとめる。

オプションの-axis 3は、4次元目のt軸(Volume)方向にまとめるという意味である(MRtrixではAxisを0から数える [i.e., x: 0, y: 1, z: 2, t: 3])。mrcatの詳細は、こちら。

mrcat DWI_AP.mif DWI_PA.mif DWI_all.mif -axis 3

次に、dwiextractを用いて、b=0のみを抽出する。dwiextractの詳細は、こちら。

dwiextract -bzero DWI_all.mif DWI_b0.mif

3.2. 歪み補正と頭の動き補正

歪み補正と頭の動き補正をするために、次のコマンドを実行する。

ここで使用した、各オプションは以下。

  • -rpe_header:位相エンコード情報を読み込む
  • -se_epi:b=0(spin-echo EPI images)を指定
  • -align_seepi:磁場の不均一性場の推定で用いられる、SE-EPI画像とDWIの間の位置合わせを実行

dwifslpreproc DWI_all.mif DWI_preproc.mif -rpe_header -se_epi DWI_b0.mif -align_seepi

歪み補正後の画像は、以下。

頭の動き補正後の画像は、以下。


【MRtrix】MRtrixを用いた5TT(five-tissue-type)画像の生成


1. 目的
2. コマンド
3. 使用例
3.1. FSLアルゴリズムを用いる場合
3.2. FreeSurferアルゴリズムを用いる場合
4. 結果


1. 目的

  • MRtrixを用いた5TT(five-tissue-type)画像の生成

2. コマンド

MRtrixの5ttgenを用いて、次の5つの組織(five-tissue-type: 5TT)画像を生成する。

  1. Cortical grey matter
  2. Sub-cortical grey matter
  3. White matter
  4. CSF
  5. Pathological tissue

5ttgenのヘルプは、次の通り。

クリックして展開
SYNOPSIS

     Generate a 5TT image suitable for ACT

USAGE

     5ttgen [ options ] algorithm ...

        algorithm    Select the algorithm to be used to complete the script operation;
                     additional details and options become available once an
                     algorithm is nominated. Options are: freesurfer, fsl, gif,
                     hsvs

DESCRIPTION

     5ttgen acts as a 'master' script for generating a five-tissue-type (5TT)
     segmented tissue image suitable for use in Anatomically-Constrained
     Tractography (ACT). A range of different algorithms are available for
     completing this task. When using this script, the name of the algorithm to
     be used must appear as the first argument on the command-line after
     '5ttgen'. The subsequent compulsory arguments and options available depend
     on the particular algorithm being invoked.

     Each algorithm available also has its own help page, including necessary
     references; e.g. to see the help page of the 'fsl' algorithm, type '5ttgen
     fsl'.

Options common to all 5ttgen algorithms

  -nocrop
     Do NOT crop the resulting 5TT image to reduce its size (keep the same
     dimensions as the input image)

  -sgm_amyg_hipp
     Represent the amygdalae and hippocampi as sub-cortical grey matter in the
     5TT image

Additional standard options for Python scripts

  -nocleanup
     do not delete intermediate files during script execution, and do not delete
     scratch directory at script completion.

  -scratch /path/to/scratch/
     manually specify the path in which to generate the scratch directory.

  -continue <ScratchDir> <LastFile>
     continue the script from a previous execution; must provide the scratch
     directory path, and the name of the last successfully-generated file.

Standard options

  -info
     display information messages.

  -quiet
     do not display information messages or progress status. Alternatively, this
     can be achieved by setting the MRTRIX_QUIET environment variable to a non-
     empty string.

  -debug
     display debugging messages.

  -force
     force overwrite of output files.

  -nthreads number
     use this number of threads in multi-threaded applications (set to 0 to
     disable multi-threading).

  -config key value  (multiple uses permitted)
     temporarily set the value of an MRtrix config file entry.

  -help
     display this information page and exit.

  -version
     display version information and exit.

基本的な使い方は、以下の通り。5ttgenのアルゴリズムは、freesurfer, fsl, gif, hsvsがあるが、ここではfreesurferとfslのアルゴリズムについて使い方を解説する。

5ttgen [アルゴリズム] <入力画像> <出力画像>

3. 使用例

3.1. FSLアルゴリズムを用いる場合

FSLアルゴリズムを用いる場合、3D-T1WI(T1w.nii.gz)が必要となる。また、オプションとして3D-T2WIも入力することができる。

5ttgen fsl T1w.nii.gz 5tt.nii.gz

3.2. FreeSurferアルゴリズムを用いる場合

FreeSurferアルゴリズムを用いる場合、Freesurferの生成ファイルであるaparc+aseg.mgz(asegとついたファイル)が必要となる。

FreeSurferの使い方は、こちらの記事を参考にするとよい。

aparc+aseg.mgzが準備できたら、以下のコマンドを実行する。

5ttgen freesurfer aparc+aseg.mgz 5tt.nii.gz

4. 結果

5ttgenで生成された画像は、5ボリュームデータであり、各ボリュームと対応する組織は次の通り。

  1. Cortical grey matter
  2. Sub-cortical grey matter
  3. White matter
  4. CSF
  5. Pathological tissue

以下に、FSLとFreeSurferのアルゴリズムを用いて5ttgenした結果(緑)を示す。

【MRtrix】MRtrixを用いた拡散MRIのバイアス(信号ムラ)補正


1. 目的
2. コマンド
3. 使用例
3.1. 前準備
3.2. 拡散MRIのバイアス(信号ムラ)補正


1. 目的

  • MRtrixを用いた拡散MRIのバイアス(信号ムラ)補正

2. コマンド

MRtrixを用いて拡散MRIのバイアス(信号ムラ)補正をするには、dwibiascorrectを使用する。

ここでは、ANTsのN4アルゴリズムを用いたバイアス補正を紹介する。ANTsアルゴリズムを使用する場合は、ANTsを前もってインストールしておく必要がある。

dwibiascorrectのヘルプは、次の通り。

クリックして展開
SYNOPSIS

     Perform B1 field inhomogeneity correction for a DWI volume series

USAGE

     dwibiascorrect [ options ] algorithm ...

        algorithm    Select the algorithm to be used to complete the script operation;
                     additional details and options become available once an
                     algorithm is nominated. Options are: ants, fsl

Options for importing the diffusion gradient table

  -grad GRAD
     Provide the diffusion gradient table in MRtrix format

  -fslgrad bvecs bvals
     Provide the diffusion gradient table in FSL bvecs/bvals format

Options common to all dwibiascorrect algorithms

  -mask image
     Manually provide a mask image for bias field estimation

  -bias image
     Output the estimated bias field

Additional standard options for Python scripts

  -nocleanup
     do not delete intermediate files during script execution, and do not delete
     scratch directory at script completion.

  -scratch /path/to/scratch/
     manually specify the path in which to generate the scratch directory.

  -continue <ScratchDir> <LastFile>
     continue the script from a previous execution; must provide the scratch
     directory path, and the name of the last successfully-generated file.

Standard options

  -info
     display information messages.

  -quiet
     do not display information messages or progress status. Alternatively, this
     can be achieved by setting the MRTRIX_QUIET environment variable to a non-
     empty string.

  -debug
     display debugging messages.

  -force
     force overwrite of output files.

  -nthreads number
     use this number of threads in multi-threaded applications (set to 0 to
     disable multi-threading).

  -config key value  (multiple uses permitted)
     temporarily set the value of an MRtrix config file entry.

  -help
     display this information page and exit.

  -version
     display version information and exit.

基本的な使い方は、以下の通り。

dwibiascorrect ants <入力画像> <出力画像>

3.使用例

3.1.前準備

まず、こちらの記事を参考に、拡散MRI(DWI.nii.gz)とそのMPG軸情報(bvecs, bvals)とヘッダー情報(headers.json)をまとめて、MIF形式(DWI.mif)に変換する。

mrconvert -fslgrad bvecs bvals -json_import headers.json DWI.nii.gz DWI.mif

3.2.拡散MRIのバイアス(信号ムラ)補正

以下のコマンドを実行する。-biasオプションを指定することで、バイアスフィールドを出力することができる。

dwibiascorrect ants DWI.mif DWI_unbiased.mif -bias bias.mif

補正後の画像は、以下。

【MRtrix】MRtrixを用いた拡散MRIのノイズ除去 ~Denoise~


1. 目的
2. コマンド
2.1. 使用例


1. 目的

  • MRtrixを用いた拡散MRIのノイズ除去(Denoise)

2. コマンド

拡散MRIのノイズ除去には、MRtrixdwidenoiseを用いる。dwidenoiseは、Marchenko-Pastur PCAを用いたデノイズである。

拡散MRIのノイズ除去は、前処理の一番最初に実行する必要がある。

dwidenoiseのヘルプは、次の通り。

クリックして展開
SYNOPSIS

     dMRI noise level estimation and denoising using Marchenko-Pastur PCA

USAGE

     dwidenoise [ options ] dwi out

        dwi          the input diffusion-weighted image.

        out          the output denoised DWI image.


DESCRIPTION

     DWI data denoising and noise map estimation by exploiting data redundancy
     in the PCA domain using the prior knowledge that the eigenspectrum of
     random covariance matrices is described by the universal Marchenko-Pastur
     (MP) distribution. Fitting the MP distribution to the spectrum of
     patch-wise signal matrices hence provides an estimator of the noise level
     'sigma', as was first shown in Veraart et al. (2016) and later improved in
     Cordero-Grande et al. (2019). This noise level estimate then determines
     the optimal cut-off for PCA denoising.

     Important note: image denoising must be performed as the first step of the
     image processing pipeline. The routine will fail if interpolation or
     smoothing has been applied to the data prior to denoising.

     Note that this function does not correct for non-Gaussian noise biases
     present in magnitude-reconstructed MRI images. If available, including the
     MRI phase data can reduce such non-Gaussian biases, and the command now
     supports complex input data.

OPTIONS

  -mask image
     Only process voxels within the specified binary brain mask image.

  -extent window
     Set the patch size of the denoising filter. By default, the command will
     select the smallest isotropic patch size that exceeds the number of DW
     images in the input data, e.g., 5x5x5 for data with <= 125 DWI volumes,
     7x7x7 for data with <= 343 DWI volumes, etc.

  -noise level
     The output noise map, i.e., the estimated noise level 'sigma' in the data.
     Note that on complex input data, this will be the total noise level across
     real and imaginary channels, so a scale factor sqrt(2) applies.

  -datatype float32/float64
     Datatype for the eigenvalue decomposition (single or double precision).
     For complex input data, this will select complex float32 or complex
     float64 datatypes.

  -estimator Exp1/Exp2
     Select the noise level estimator (default = Exp2), either: 
     * Exp1: the original estimator used in Veraart et al. (2016), or 
     * Exp2: the improved estimator introduced in Cordero-Grande et al. (2019).

Standard options

  -info
     display information messages.

  -quiet
     do not display information messages or progress status; alternatively,
     this can be achieved by setting the MRTRIX_QUIET environment variable to a
     non-empty string.

  -debug
     display debugging messages.

  -force
     force overwrite of output files (caution: using the same file as input and
     output might cause unexpected behaviour).

  -nthreads number
     use this number of threads in multi-threaded applications (set to 0 to
     disable multi-threading).

  -config key value  (multiple uses permitted)
     temporarily set the value of an MRtrix config file entry.

  -help
     display this information page and exit.

  -version
     display version information and exit.

基本的な使い方は、次の通り。

dwidenoise <入力画像> <出力画像>

2.1. 使用例

前処理する前の拡散MRI(DWI.nii.gz)に、dwidenoiseを実行する。

dwidenoise DWI.nii.gz DWI_denoised.nii.gz

処理後の画像は、以下。

【MRtrix】拡散MRIからb値ごとに画像を抽出


1. 目的
2. コマンド
3.使用例
3.1.前準備
3.2.b=0のみを抽出
3.3.b≠0を抽出
3.4.b値ごとに抽出


1. 目的

  • 拡散MRIからb値ごとに画像を抽出

2. コマンド

拡散MRIからb値ごとに画像を抽出するには、MRtrixdwiextractを用いる。

dwiextractのヘルプは、次の通り。

クリックして展開
SYNOPSIS

     Extract diffusion-weighted volumes, b=0 volumes, or certain shells from a
     DWI dataset

USAGE

     dwiextract [ options ] input output

        input        the input DW image.

        output       the output image (diffusion-weighted volumes by default).


EXAMPLE USAGES

     Calculate the mean b=0 image from a 4D DWI series:
       $ dwiextract dwi.mif - -bzero | mrmath - mean mean_bzero.mif -axis 3
     The dwiextract command extracts all volumes for which the b-value is
     (approximately) zero; the resulting 4D image can then be provided to the
     mrmath command to calculate the mean intensity across volumes for each
     voxel.

OPTIONS

  -bzero
     Output b=0 volumes (instead of the diffusion weighted volumes, if
     -singleshell is not specified).

  -no_bzero
     Output only non b=0 volumes (default, if -singleshell is not specified).

  -singleshell
     Force a single-shell (single non b=0 shell) output. This will include b=0
     volumes, if present. Use with -bzero to enforce presence of b=0 volumes
     (error if not present) or with -no_bzero to exclude them.

DW gradient table import options

  -grad file
     Provide the diffusion-weighted gradient scheme used in the acquisition in
     a text file. This should be supplied as a 4xN text file with each line is
     in the format [ X Y Z b ], where [ X Y Z ] describe the direction of the
     applied gradient, and b gives the b-value in units of s/mm^2. If a
     diffusion gradient scheme is present in the input image header, the data
     provided with this option will be instead used.

  -fslgrad bvecs bvals
     Provide the diffusion-weighted gradient scheme used in the acquisition in
     FSL bvecs/bvals format files. If a diffusion gradient scheme is present in
     the input image header, the data provided with this option will be instead
     used.

DW shell selection options

  -shells b-values
     specify one or more b-values to use during processing, as a
     comma-separated list of the desired approximate b-values (b-values are
     clustered to allow for small deviations). Note that some commands are
     incompatible with multiple b-values, and will report an error if more than
     one b-value is provided. 
     WARNING: note that, even though the b=0 volumes are never referred to as
     shells in the literature, they still have to be explicitly included in the
     list of b-values as provided to the -shell option! Several algorithms
     which include the b=0 volumes in their computations may otherwise return
     an undesired result.

DW gradient table export options

  -export_grad_mrtrix path
     export the diffusion-weighted gradient table to file in MRtrix format

  -export_grad_fsl bvecs_path bvals_path
     export the diffusion-weighted gradient table to files in FSL (bvecs /
     bvals) format

Options for importing phase-encode tables

  -import_pe_table file
     import a phase-encoding table from file

  -import_pe_eddy config indices
     import phase-encoding information from an EDDY-style config / index file
     pair

Options for selecting volumes based on phase-encoding

  -pe desc
     select volumes with a particular phase encoding; this can be three
     comma-separated values (for i,j,k components of vector direction) or four
     (direction & total readout time)

Stride options

  -strides spec
     specify the strides of the output data in memory; either as a
     comma-separated list of (signed) integers, or as a template image from
     which the strides shall be extracted and used. The actual strides produced
     will depend on whether the output image format can support it.

Standard options

  -info
     display information messages.

  -quiet
     do not display information messages or progress status; alternatively,
     this can be achieved by setting the MRTRIX_QUIET environment variable to a
     non-empty string.

  -debug
     display debugging messages.

  -force
     force overwrite of output files (caution: using the same file as input and
     output might cause unexpected behaviour).

  -nthreads number
     use this number of threads in multi-threaded applications (set to 0 to
     disable multi-threading).

  -config key value  (multiple uses permitted)
     temporarily set the value of an MRtrix config file entry.

  -help
     display this information page and exit.

  -version
     display version information and exit.

基本的な使い方は、以下の通り。

dwiextract -bzero <入力画像> <出力画像>  # b=0のみを抽出
dwiextract -no_bzero <入力画像> <出力画像>  # b=0以外の拡散強調像を抽出
dwiextract -singleshell <入力画像> <出力画像>  # b=0以外の拡散強調像を抽出

3. 使用例

3.1. 前準備

まず、こちらの記事を参考に、拡散MRI(DWI.nii.gz)とそのMPG軸情報(bvecs, bvals)とヘッダー情報(headers.json)をまとめて、MIF形式(DWI.mif)に変換する。

mrconvert -fslgrad bvecs bvals -json_import headers.json DWI.nii.gz DWI.mif

ここで使用する拡散MRI(DWI.mif)は、b=0が1枚、b=1000が64枚、b=2000が64枚で構成されている(全部で129 volumes)。

mrinfo DWI.mif  |grep Dimensions
Dimensions:        130 x 130 x 82 x 129

3.2. b=0のみを抽出

オプション-bzeroを指定する。

dwiextract -bzero DWI.mif DWI_b0.mif

b=0の画像のみ抽出される。

mrinfo DWI_b0.mif  |grep Dimensions
Dimensions:        130 x 130 x 82 x 1

3.3. b≠0を抽出

オプション-no_bzeroを指定する。

dwiextract -no_bzero DWI.mif DWI_nonb0.mif

b≠0の画像のみ抽出される。

mrinfo DWI_nonb0.mif  |grep Dimensions
Dimensions:        130 x 130 x 82 x 128

3.4. b値ごとに抽出

オプション-singleshellを指定する。

例えば、b=1000のみを抽出する場合、以下のようになる。

dwiextract -shells 1000 DWI.mif DWI_b1000.mif

b=1000の画像のみ抽出される。

mrinfo DWI_b1000.mif  |grep Dimensions
Dimensions:        130 x 130 x 82 x 64

【MRtrix】MRtrixを用いた拡散MRIのマスク画像の作成


1. 目的
2. コマンド
3. 使用例
3.1. 前準備
3.2. 拡散MRIのマスク画像の作成


1. 目的

  • MRtrixを用いた拡散MRIのマスク画像の作成

2. コマンド

MRtrixを用いて拡散MRIのマスク画像の作成するには、dwi2maskを使用する。

dwi2maskのヘルプは、次の通り。

クリックして展開
SYNOPSIS

     Generates a whole brain mask from a DWI image

USAGE

     dwi2mask [ options ] input output

        input        the input DWI image containing volumes that are both
                     diffusion weighted and b=0

        output       the output whole-brain mask image


DESCRIPTION

     All diffusion weighted and b=0 volumes are used to obtain a mask that
     includes both brain tissue and CSF.

     In a second step peninsula-like extensions, where the peninsula itself is
     wider than the bridge connecting it to the mask, are removed. This may
     help removing artefacts and non-brain parts, e.g. eyes, from the mask.

OPTIONS

  -clean_scale value
     the maximum scale used to cut bridges. A certain maximum scale cuts
     bridges up to a width (in voxels) of 2x the provided scale. Setting this
     to 0 disables the mask cleaning step. (Default: 2)

DW gradient table import options

  -grad file
     Provide the diffusion-weighted gradient scheme used in the acquisition in
     a text file. This should be supplied as a 4xN text file with each line is
     in the format [ X Y Z b ], where [ X Y Z ] describe the direction of the
     applied gradient, and b gives the b-value in units of s/mm^2. If a
     diffusion gradient scheme is present in the input image header, the data
     provided with this option will be instead used.

  -fslgrad bvecs bvals
     Provide the diffusion-weighted gradient scheme used in the acquisition in
     FSL bvecs/bvals format files. If a diffusion gradient scheme is present in
     the input image header, the data provided with this option will be instead
     used.

基本的な使い方は、以下の通り。

dwi2mask <入力画像> <出力画像>

3. 使用例

3.1. 前準備

まず、こちらの記事を参考に、拡散MRI(DWI.nii.gz)とそのMPG軸情報(bvecs, bvals)とヘッダー情報(headers.json)をまとめて、MIF形式(DWI.mif)に変換する。

mrconvert -fslgrad bvecs bvals -json_import headers.json DWI.nii.gz DWI.mif

3.2. 拡散MRIのマスク画像の作成

以下のコマンドを実行する。

dwi2mask DWI.mif DWI_mask.mif

拡散MRIとマスク画像(緑)を重ね合わせてみる。

【MRtrix】MRtrixを用いた解像度の変更 ~Upsampling~


1. 目的
2. コマンド
3. 使用例
3.1. ボクセルサイズを指定(オプション:-voxel)
3.2. スケールを指定(オプション:-scale))
3.3. ボクセルサイズを指定(オプション:-voxel))
3.4. 目的の解像度を持つ画像を指定(オプション:-template))


1. 目的

  • MRtrixを用いたアップサンプリング(Upsampling)

2. コマンド

MRtrixのmrgridを用いる。

mrgridのヘルプは、次の通り。

クリックして展開
SYNOPSIS

     Modify the grid of an image without interpolation (cropping or padding) or
     by regridding to an image grid with modified orientation, location and or
     resolution. The image content remains in place in real world coordinates.

USAGE

     mrgrid [ options ] input operation output

        input        input image to be regridded.

        operation    the operation to be performed, one of: regrid, crop, pad.

        output       the output image.


DESCRIPTION

     - regrid: This operation performs changes of the voxel grid that require
     interpolation of the image such as changing the resolution or location and
     orientation of the voxel grid. If the image is down-sampled, the
     appropriate smoothing is automatically applied using Gaussian smoothing
     unless nearest neighbour interpolation is selected or oversample is
     changed explicitly. The resolution can only be changed for spatial
     dimensions. 

     - crop: The image extent after cropping, can be specified either manually
     for each axis dimensions, or via a mask or reference image. The image can
     be cropped to the extent of a mask. This is useful for axially-acquired
     brain images, where the image size can be reduced by a factor of 2 by
     removing the empty space on either side of the brain. Note that cropping
     does not extend the image beyond the original FOV unless explicitly
     specified (via -crop_unbound or negative -axis extent).

     - pad: Analogously to cropping, padding increases the FOV of an image
     without image interpolation. Pad and crop can be performed simultaneously
     by specifying signed specifier argument values to the -axis option.

     This command encapsulates and extends the functionality of the superseded
     commands 'mrpad', 'mrcrop' and 'mrresize'. Note the difference in -axis
     convention used for 'mrcrop' and 'mrpad' (see -axis option description).

EXAMPLE USAGES

     Crop and pad the first axis:
       $ mrgrid in.mif crop -axis 0 10,-5 out.mif
     This removes 10 voxels on the lower and pads with 5 on the upper bound,
     which is equivalent to padding with the negated specifier (mrgrid in.mif
     pad -axis 0 -10,5 out.mif).

     Right-pad the image to the number of voxels of a reference image:
       $ mrgrid in.mif pad -as ref.mif -all_axes -axis 3 0,0 out.mif -fill nan
     This pads the image on the upper bound of all axes except for the volume
     dimension. The headers of in.mif and ref.mif are ignored and the output
     image uses NAN values to fill in voxels outside the original range of
     in.mif.

     Regrid and interpolate to match the voxel grid of a reference image:
       $ mrgrid in.mif regrid -template ref.mif -scale 1,1,0.5 out.mif -fill nan
     The -template instructs to regrid in.mif to match the voxel grid of
     ref.mif (voxel size, grid orientation and voxel centres). The -scale
     option overwrites the voxel scaling factor yielding voxel sizes in the
     third dimension that are twice as coarse as those of the template image.

Regridding options (involves image interpolation, applied to spatial axes only)

  -template image
     match the input image grid (voxel spacing, image size, header
     transformation) to that of a reference image. The image resolution
     relative to the template image can be changed with one of -size, -voxel,
     -scale.

  -size dims
     define the size (number of voxels) in each spatial dimension for the
     output image. This should be specified as a comma-separated list.

  -voxel size
     define the new voxel size for the output image. This can be specified
     either as a single value to be used for all spatial dimensions, or as a
     comma-separated list of the size for each voxel dimension.

  -scale factor
     scale the image resolution by the supplied factor. This can be specified
     either as a single value to be used for all dimensions, or as a
     comma-separated list of scale factors for each dimension.

  -interp method
     set the interpolation method to use when reslicing (choices: nearest,
     linear, cubic, sinc. Default: cubic).

  -oversample factor
     set the amount of over-sampling (in the target space) to perform when
     regridding. This is particularly relevant when downsamping a
     high-resolution image to a low-resolution image, to avoid aliasing
     artefacts. This can consist of a single integer, or a comma-separated list
     of 3 integers if different oversampling factors are desired along the
     different axes. Default is determined from ratio of voxel dimensions
     (disabled for nearest-neighbour interpolation).

Pad and crop options (no image interpolation is performed, header transformation is adjusted)

  -as reference image
     pad or crop the input image on the upper bound to match the specified
     reference image grid. This operation ignores differences in image
     transformation between input and reference image.

  -uniform number
     pad or crop the input image by a uniform number of voxels on all sides

  -mask image
     crop the input image according to the spatial extent of a mask image. The
     mask must share a common voxel grid with the input image but differences
     in image transformations are ignored. Note that even though only 3
     dimensions are cropped when using a mask, the bounds are computed by
     checking the extent for all dimensions. Note that by default a gap of 1
     voxel is left at all edges of the image to allow valid trilinear
     interpolation. This gap can be modified with the -uniform option but by
     default it does not extend beyond the FOV unless -crop_unbound is used.

  -crop_unbound
     Allow padding beyond the original FOV when cropping.

  -axis index spec  (multiple uses permitted)
     pad or crop the input image along the provided axis (defined by index).
     The specifier argument defines the number of voxels added or removed on
     the lower or upper end of the axis (-axis index delta_lower,delta_upper)
     or acts as a voxel selection range (-axis index start:stop). In both
     modes, values are relative to the input image (overriding all other
     extent-specifying options). Negative delta specifier values trigger the
     inverse operation (pad instead of crop and vice versa) and negative range
     specifier trigger padding. Note that the deprecated commands 'mrcrop' and
     'mrpad' used range-based and delta-based -axis indices, respectively.

  -all_axes
     Crop or pad all, not just spatial axes.

General options

  -fill number
     Use number as the out of bounds value. nan, inf and -inf are valid
     arguments. (Default: 0.0)

Stride options

  -strides spec
     specify the strides of the output data in memory; either as a
     comma-separated list of (signed) integers, or as a template image from
     which the strides shall be extracted and used. The actual strides produced
     will depend on whether the output image format can support it.

Data type options

  -datatype spec
     specify output image data type. Valid choices are: float32, float32le,
     float32be, float64, float64le, float64be, int64, uint64, int64le,
     uint64le, int64be, uint64be, int32, uint32, int32le, uint32le, int32be,
     uint32be, int16, uint16, int16le, uint16le, int16be, uint16be, cfloat32,
     cfloat32le, cfloat32be, cfloat64, cfloat64le, cfloat64be, int8, uint8,
     bit.

Standard options

  -info
     display information messages.

  -quiet
     do not display information messages or progress status; alternatively,
     this can be achieved by setting the MRTRIX_QUIET environment variable to a
     non-empty string.

  -debug
     display debugging messages.

  -force
     force overwrite of output files (caution: using the same file as input and
     output might cause unexpected behaviour).

  -nthreads number
     use this number of threads in multi-threaded applications (set to 0 to
     disable multi-threading).

  -config key value  (multiple uses permitted)
     temporarily set the value of an MRtrix config file entry.

  -help
     display this information page and exit.

  -version
     display version information and exit.

解像度の変更する場合の基本的な使い方は、以下の通り。

mrgrid <入力画像> regrid -voxel <値> <出力画像>  # ボクセルサイズを指定
mrgrid <入力画像> regrid -scale <値> <出力画像>  # スケールを指定
mrgrid <入力画像> regrid -template <目的の解像度を持つ画像> <出力画像>  # 目的の解像度を持つ画像を指定

3. 使用例

3D-T1WI(T1w.nii.gz)の解像度を変更する。

3D-T1WI(T1w.nii.gz)の解像度を確認してみる。

mrinfo T1w.nii.gz
************************************************
Image name:          "T1w.nii.gz"
************************************************
  Dimensions:        192 x 256 x 256
  Voxel size:        0.9 x 0.9375 x 0.9375
  Data strides:      [ -1 2 3 ]
  Format:            NIfTI-1.1 (GZip compressed)
  Data type:         signed 16 bit integer (little endian)
  Intensity scaling: offset = 0, multiplier = 1
  Transform:               0.9998     0.01794   0.0003439      -82.89
                         -0.01788      0.9946      0.1023      -113.6
                         0.001492     -0.1023      0.9948      -114.6
  comments:          6.0.3:b862cdd5

3.1. ボクセルサイズを指定(オプション:-voxel

-voxelオプションを用いて、以下のコマンドを実行。

ボクセルサイズを1mm isotropicにする。

mrgrid T1w.nii.gz regrid -voxel 1 T1w_1mm_iso.nii.gz

解像度を確認してみる。ボクセルサイズが1 x 1 x 1(1mm iso)になっている

mrinfo T1w_1mm_iso.nii.gz
************************************************
Image name:          "T1w_1mm_iso.nii.gz"
************************************************
  Dimensions:        173 x 240 x 240
  Voxel size:        1 x 1 x 1
  Data strides:      [ -1 2 3 ]
  Format:            NIfTI-1.1 (GZip compressed)
  Data type:         32 bit float (little endian)
  Intensity scaling: offset = 0, multiplier = 1
  Transform:               0.9998     0.01794   0.0003439      -82.94
                         -0.01788      0.9946      0.1023      -113.6
                         0.001492     -0.1023      0.9948      -114.5
  comments:          6.0.3:b862cdd5
  mrtrix_version:    3.0.0-40-g3e1ed225

3.2. スケールを指定(オプション:-scale

-scaleオプションを用いて、以下のコマンドを実行。

スケールを2にして、解像度を2倍にする。

mrgrid T1w.nii.gz regrid -scale 2 T1w_scale2.nii.gz

解像度を確認してみる。解像度が173 x 240 x 240からになっている。

mrinfo T1w_scale2.nii.gz
************************************************
Image name:          "T1w_scale2.nii.gz"
************************************************
  Dimensions:        384 x 512 x 512
  Voxel size:        0.45 x 0.46875 x 0.46875
  Data strides:      [ -1 2 3 ]
  Format:            NIfTI-1.1 (GZip compressed)
  Data type:         32 bit float (little endian)
  Intensity scaling: offset = 0, multiplier = 1
  Transform:               0.9998     0.01794   0.0003439      -83.12
                         -0.01788      0.9946      0.1023      -113.9
                         0.001492     -0.1023      0.9948      -114.8
  comments:          6.0.3:b862cdd5
  mrtrix_version:    3.0.0-40-g3e1ed225

3.3. ボクセルサイズを指定(オプション:-voxel

-voxelオプションを用いて、以下のコマンドを実行。

ボクセルサイズを1mm isotropicにする。

mrgrid T1w.nii.gz regrid -voxel 1 T1w_1mm_iso.nii.gz

解像度を確認してみる。ボクセルサイズが1 x 1 x 1(1mm iso)になっている。

mrinfo T1w_1mm_iso.nii.gz
************************************************
Image name:          "T1w_1mm_iso.nii.gz"
************************************************
  Dimensions:        173 x 240 x 240
  Voxel size:        1 x 1 x 1
  Data strides:      [ -1 2 3 ]
  Format:            NIfTI-1.1 (GZip compressed)
  Data type:         32 bit float (little endian)
  Intensity scaling: offset = 0, multiplier = 1
  Transform:               0.9998     0.01794   0.0003439      -82.94
                         -0.01788      0.9946      0.1023      -113.6
                         0.001492     -0.1023      0.9948      -114.5
  comments:          6.0.3:b862cdd5
  mrtrix_version:    3.0.0-40-g3e1ed225

3.4. 目的の解像度を持つ画像を指定(オプション:-template

標準脳(MNI152)の3D-T1WI(MNI152_T1_2mm.nii.gz)と同じ解像度にする。標準脳(MNI152_T1_2mm.nii.gz)の解像度は以下。

mrinfo MNI152_T1_2mm.nii.gz
************************************************
Image name:          "MNI152_T1_2mm.nii.gz"
************************************************
  Dimensions:        91 x 109 x 91
  Voxel size:        2 x 2 x 2
  Data strides:      [ -1 2 3 ]
  Format:            NIfTI-1.1 (GZip compressed)
  Data type:         signed 16 bit integer (little endian)
  Intensity scaling: offset = 0, multiplier = 1
  Transform:                    1           0           0         -90
                               -0           1           0        -126
                               -0           0           1         -72
  comments:          FSL5.0

個人脳(T1w.nii.gz)を標準脳(MNI152_T1_2mm.nii.gz)の解像度に合わせるには、-templateオプションを用いて、以下のコマンドを実行。

mrgrid T1w.nii.gz regrid -template MNI152_T1_2mm.nii.gz T1w_MNIreso.nii.gz

解像度を確認してみる。解像度が標準脳(MNI152_T1_2mm.nii.gz)と同じになっている。

mrinfo T1w_MNIreso.nii.gz
************************************************
Image name:          "T1w_MNIreso.nii.gz"
************************************************
  Dimensions:        91 x 109 x 91
  Voxel size:        2 x 2 x 2
  Data strides:      [ -1 2 3 ]
  Format:            NIfTI-1.1 (GZip compressed)
  Data type:         32 bit float (little endian)
  Intensity scaling: offset = 0, multiplier = 1
  Transform:                    1           0           0         -90
                               -0           1           0        -126
                               -0           0           1         -72
  comments:          6.0.3:b862cdd5
  mrtrix_version:    3.0.0-40-g3e1ed225

【FSL/MRtrix】4D画像から3D画像を抽出


1. 目的
2. FSLを用いる場合
2.1. コマンド
2.2. 使用例
3. MRtrixを用いる場合
3.1. コマンド
3.2. 使用例


1. 目的

  • 4D画像から3D画像を抽出

2. FSLを用いる場合

2.1. コマンド

FSLfslroiコマンドを用いる。

fslroiのヘルプは、次の通り。

クリックして展開
Usage: fslroi <input> <output> <xmin> <xsize> <ymin> <ysize> <zmin> <zsize>
       fslroi <input> <output> <tmin> <tsize>

       fslroi <input> <output> <xmin> <xsize> <ymin> <ysize> <zmin> <zsize> <tmin> <tsize>
Note: indexing (in both time and space) starts with 0 not 1! Inputting -1 for a size will set it to the full image extent for that dimension.

4D画像から3D画像を抽出する際の、基本的な使い方は以下の通り。

fslroi <入力画像> <出力画像> <Volume Index> <Volume Indexから残したいVolume数>

2.2. 使用例

例えば、5ttgen等で作成した以下のような5つの組織画像(5tt.nii.gz)が4D画像となっている場合。

Pathological tissue(Volume 4th)を取り除くには、次のようにコマンドを実行する。FSLではVolumeのIndexを0から数える。つまり、1番目のVolumeのIndexは0となる。以下のコードを翻訳すると、「Volume Index0番から数えて4 Volumesまでを残す」ということになる。

fslroi 5tt.nii.gz 4tt.nii.gz 0 4

fslhdコマンドを用いて、ボリューム数を確認すると、処理前で5 Volumesだったのが処理後に4 Volumesになっていることが分かる。使い方の詳細は、こちらの記事を参考に。

fslhd 5tt.nii.gz |grep ^dim4
fslhd 4tt.nii.gz |grep ^dim4
dim4        5  # 5tt.nii.gz
dim4        4  # 4tt.nii.gz

3. MRtrixを用いる場合

3.1. コマンド

MRtrixmrconvertコマンドを用いる。

mrconvertのヘルプは、次の通り。

クリックして展開
SYNOPSIS

     Perform conversion between different file types and optionally extract a
     subset of the input image

USAGE

     mrconvert [ options ] input output

        input        the input image.

        output       the output image.


DESCRIPTION

     If used correctly, this program can be a very useful workhorse. In
     addition to converting images between different formats, it can be used to
     extract specific studies from a data set, extract a specific region of
     interest, or flip the images. Some of the possible operations are
     described in more detail below.

     Note that for both the -coord and -axes options, indexing starts from 0
     rather than 1. E.g. -coord 3 <#> selects volumes (the fourth dimension)
     from the series; -axes 0,1,2 includes only the three spatial axes in the
     output image.

     Additionally, for the second input to the -coord option and the -axes
     option, you can use any valid number sequence in the selection, as well as
     the 'end' keyword (see the main documentation for details); this can be
     particularly useful to select multiple coordinates.

     The -vox option is used to change the size of the voxels in the output
     image as reported in the image header; note however that this does not
     re-sample the image based on a new voxel size (that is done using the
     mrresize command).

     By default, the intensity scaling parameters in the input image header are
     passed through to the output image header when writing to an integer
     image, and reset to 0,1 (i.e. no scaling) for floating-point and binary
     images. Note that the -scaling option will therefore have no effect for
     floating-point or binary output images.

     The -axes option specifies which axes from the input image will be used to
     form the output image. This allows the permutation, omission, or addition
     of axes into the output image. The axes should be supplied as a
     comma-separated list of axis indices. If an axis from the input image is
     to be omitted from the output image, it must either already have a size of
     1, or a single coordinate along that axis must be selected by the user by
     using the -coord option. Examples are provided further below.

     The -bvalue_scaling option controls an aspect of the import of diffusion
     gradient tables. When the input diffusion-weighting direction vectors have
     norms that differ substantially from unity, the b-values will be scaled by
     the square of their corresponding vector norm (this is how multi-shell
     acquisitions are frequently achieved on scanner platforms). However in
     some rare instances, the b-values may be correct, despite the vectors not
     being of unit norm (or conversely, the b-values may need to be rescaled
     even though the vectors are close to unit norm). This option allows the
     user to control this operation and override MRrtix3's automatic detection.

EXAMPLE USAGES

     Extract the first volume from a 4D image, and make the output a 3D image:
       $ mrconvert in.mif -coord 3 0 -axes 0,1,2 out.mif
     The -coord 3 0 option extracts, from axis number 3 (which is the fourth
     axis since counting begins from 0; this is the axis that steps across
     image volumes), only coordinate number 0 (i.e. the first volume). The
     -axes 0,1,2 ensures that only the first three axes (i.e. the spatial axes)
     are retained; if this option were not used in this example, then image
     out.mif would be a 4D image, but it would only consist of a single volume,
     and mrinfo would report its size along the fourth axis as 1.

     Extract slice number 24 along the AP direction:
       $ mrconvert volume.mif slice.mif -coord 1 24
     MRtrix3 uses a RAS (Right-Anterior-Superior) axis convention, and
     internally reorients images upon loading in order to conform to this as
     far as possible. So for non-exotic data, axis 1 should correspond
     (approximately) to the anterior-posterior direction.

     Extract only every other volume from a 4D image:
       $ mrconvert all.mif every_other.mif -coord 3 1:2:end
     This example demonstrates two features: Use of the colon syntax to
     conveniently specify a number sequence (in the format 'start:step:stop');
     and use of the 'end' keyword to generate this sequence up to the size of
     the input image along that axis (i.e. the number of volumes).

     Alter the image header to report a new isotropic voxel size:
       $ mrconvert in.mif isotropic.mif -vox 1.25
     By providing a single value to the -vox option only, the specified value
     is used to set the voxel size in mm for all three spatial axes in the
     output image.

     Alter the image header to report a new anisotropic voxel size:
       $ mrconvert in.mif anisotropic.mif -vox 1,,3.5
     This example will change the reported voxel size along the first and third
     axes (ideally left-right and inferior-superior) to 1.0mm and 3.5mm
     respectively, and leave the voxel size along the second axis (ideally
     anterior-posterior) unchanged.

     Turn a single-volume 4D image into a 3D image:
       $ mrconvert 4D.mif 3D.mif -axes 0,1,2
     Sometimes in the process of extracting or calculating a single 3D volume
     from a 4D image series, the size of the image reported by mrinfo will be
     "X x Y x Z x 1", indicating that the resulting image is in fact also 4D,
     it just happens to contain only one volume. This example demonstrates how
     to convert this into a genuine 3D image (i.e. mrinfo will report the size
     as "X x Y x Z".

     Insert an axis of size 1 into the image:
       $ mrconvert XYZD.mif XYZ1D.mif -axes 0,1,2,-1,3
     This example uses the value -1 as a flag to indicate to mrconvert where a
     new axis of unity size is to be inserted. In this particular example, the
     input image has four axes: the spatial axes X, Y and Z, and some form of
     data D is stored across the fourth axis (i.e. volumes). Due to insertion
     of a new axis, the output image is 5D: the three spatial axes (XYZ), a
     single volume (the size of the output image along the fourth axis will be
     1), and data D will be stored as volume groups along the fifth axis of the
     image.

     Manually reset the data scaling parameters stored within the image header
     to defaults:
       $ mrconvert with_scaling.mif without_scaling.mif -scaling 0.0,1.0
     This command-line option alters the parameters stored within the image
     header that provide a linear mapping from raw intensity values stored in
     the image data to some other scale. Where the raw data stored in a
     particular voxel is I, the value within that voxel is interpreted as:
     value = offset + (scale x I).  To adjust this scaling, the relevant
     parameters must be provided as a comma-separated 2-vector of
     floating-point values, in the format "offset,scale" (no quotation marks).
     This particular example sets the offset to zero and the scale to one,
     which equates to no rescaling of the raw intensity data.

Options for manipulating fundamental image properties

  -coord axis selection  (multiple uses permitted)
     retain data from the input image only at the coordinates specified in the
     selection along the specified axis. The selection argument expects a
     number sequence, which can also include the 'end' keyword.

  -vox sizes
     change the voxel dimensions reported in the output image header

  -axes axes
     specify the axes from the input image that will be used to form the output
     image

  -scaling values
     specify the data scaling parameters used to rescale the intensity values

Options for handling JSON (JavaScript Object Notation) files

  -json_import file
     import data from a JSON file into header key-value pairs

  -json_export file
     export data from an image header key-value pairs into a JSON file

Options to modify generic header entries

  -clear_property key  (multiple uses permitted)
     remove the specified key from the image header altogether.

  -set_property key value  (multiple uses permitted)
     set the value of the specified key in the image header.

  -append_property key value  (multiple uses permitted)
     append the given value to the specified key in the image header (this adds
     the value specified as a new line in the header value).

  -copy_properties source
     clear all generic properties and replace with the properties from the
     image / file specified.

Stride options

  -strides spec
     specify the strides of the output data in memory; either as a
     comma-separated list of (signed) integers, or as a template image from
     which the strides shall be extracted and used. The actual strides produced
     will depend on whether the output image format can support it.

Data type options

  -datatype spec
     specify output image data type. Valid choices are: float32, float32le,
     float32be, float64, float64le, float64be, int64, uint64, int64le,
     uint64le, int64be, uint64be, int32, uint32, int32le, uint32le, int32be,
     uint32be, int16, uint16, int16le, uint16le, int16be, uint16be, cfloat32,
     cfloat32le, cfloat32be, cfloat64, cfloat64le, cfloat64be, int8, uint8,
     bit.

DW gradient table import options

  -grad file
     Provide the diffusion-weighted gradient scheme used in the acquisition in
     a text file. This should be supplied as a 4xN text file with each line is
     in the format [ X Y Z b ], where [ X Y Z ] describe the direction of the
     applied gradient, and b gives the b-value in units of s/mm^2. If a
     diffusion gradient scheme is present in the input image header, the data
     provided with this option will be instead used.

  -fslgrad bvecs bvals
     Provide the diffusion-weighted gradient scheme used in the acquisition in
     FSL bvecs/bvals format files. If a diffusion gradient scheme is present in
     the input image header, the data provided with this option will be instead
     used.

  -bvalue_scaling mode
     enable or disable scaling of diffusion b-values by the square of the
     corresponding DW gradient norm (see Desciption). Valid choices are yes/no,
     true/false, 0/1 (default: automatic).

DW gradient table export options

  -export_grad_mrtrix path
     export the diffusion-weighted gradient table to file in MRtrix format

  -export_grad_fsl bvecs_path bvals_path
     export the diffusion-weighted gradient table to files in FSL (bvecs /
     bvals) format

Options for importing phase-encode tables

  -import_pe_table file
     import a phase-encoding table from file

  -import_pe_eddy config indices
     import phase-encoding information from an EDDY-style config / index file
     pair

Options for exporting phase-encode tables

  -export_pe_table file
     export phase-encoding table to file

  -export_pe_eddy config indices
     export phase-encoding information to an EDDY-style config / index file
     pair

Standard options

  -info
     display information messages.

  -quiet
     do not display information messages or progress status; alternatively,
     this can be achieved by setting the MRTRIX_QUIET environment variable to a
     non-empty string.

  -debug
     display debugging messages.

  -force
     force overwrite of output files (caution: using the same file as input and
     output might cause unexpected behaviour).

  -nthreads number
     use this number of threads in multi-threaded applications (set to 0 to
     disable multi-threading).

  -config key value  (multiple uses permitted)
     temporarily set the value of an MRtrix config file entry.

  -help
     display this information page and exit.

  -version
     display version information and exit.

4D画像から3D画像を抽出する際の、基本的な使い方は以下の通り。

mrconvert <入力画像> <出力画像> -coord <軸番号> <残したいボリューム数>

3.2. 使用例

例えば、5ttgen等で作成した以下のような5つの組織画像(5tt.nii.gz)が4D画像となっている場合。

Pathological tissue(Volume 4th)を取り除くには、次のようにコマンドを実行する。MRtrixでもFSLと同様に、VolumeのIndexを0から数える。つまり、1番目のVolumeのIndexは0となる。また軸番号は、x, y, z, tの順番に0, 1, 2, 3であり、Volume数を操作するには、t軸(-coord 3)を操作することになる。以下のコードを翻訳すると、「Volume Index0番からVolume Index3番までを残す」ということになる。

mrconvert 5tt.nii.gz 4tt.nii.gz -coord 3 0:3

mrinfoコマンドを用いて、ボリューム数を確認すると、処理前で5 Volumesだったのが処理後に4 Volumesになっていることが分かる。使い方の詳細は、こちらの記事を参考に。

mrinfo 5tt.nii.gz 4tt.nii.gz
************************************************
Image name:          "5tt.nii.gz"
************************************************
  Dimensions:        168 x 185 x 169 x 5
  Voxel size:        0.9 x 0.9375 x 0.9375 x ?
  Data strides:      [ 1 2 3 4 ]
  Format:            NIfTI-1.1 (GZip compressed)
  Data type:         32 bit float (little endian)
  Intensity scaling: offset = 0, multiplier = 1
  Transform:               0.9998     0.01794   0.0003439      -70.81
                         -0.01788      0.9946      0.1023       -88.1
                         0.001492     -0.1023      0.9948      -56.89
  comments:          6.0.3:b862cdd5
  mrtrix_version:    3.0.0-40-g3e1ed225
************************************************
Image name:          "4tt.nii.gz"
************************************************
  Dimensions:        168 x 185 x 169 x 4
  Voxel size:        0.9 x 0.9375 x 0.9375 x ?
  Data strides:      [ 1 2 3 4 ]
  Format:            NIfTI-1.1 (GZip compressed)
  Data type:         32 bit float (little endian)
  Intensity scaling: offset = 0, multiplier = 1
  Transform:               0.9998     0.01794   0.0003439      -70.81
                         -0.01788      0.9946      0.1023       -88.1
                         0.001492     -0.1023      0.9948      -56.89
  comments:          6.0.3:b862cdd5
  mrtrix_version:    3.0.0-40-g3e1ed225

【FSL/MRtrix】画像の切り取り・マスキング ~Masking~

目的

  • 画像の切り取り・マスキング ~Masking~

FSLを用いる場合

コマンド

FSLで画像の切り取り・マスキングをするには、fslmaths-masオプションを使用する。

fslmathsのヘルプは、次の通り。

クリックして展開
Usage: fslmaths [-dt <datatype>] <first_input> [operations and inputs] <output> [-odt <datatype>]

Datatype information:
 -dt sets the datatype used internally for calculations (default float for all except double images)
 -odt sets the output datatype ( default is float )
 Possible datatypes are: char short int float double input
 "input" will set the datatype to that of the original image

Binary operations:
  (some inputs can be either an image or a number)
 -add   : add following input to current image
 -sub   : subtract following input from current image
 -mul   : multiply current image by following input
 -div   : divide current image by following input
 -rem   : modulus remainder - divide current image by following input and take remainder
 -mas   : use (following image>0) to mask current image
 -thr   : use following number to threshold current image (zero anything below the number)
 -thrp  : use following percentage (0-100) of ROBUST RANGE to threshold current image (zero anything below the number)
 -thrP  : use following percentage (0-100) of ROBUST RANGE of non-zero voxels and threshold below
 -uthr  : use following number to upper-threshold current image (zero anything above the number)
 -uthrp : use following percentage (0-100) of ROBUST RANGE to upper-threshold current image (zero anything above the number)
 -uthrP : use following percentage (0-100) of ROBUST RANGE of non-zero voxels and threshold above
 -max   : take maximum of following input and current image
 -min   : take minimum of following input and current image
 -seed  : seed random number generator with following number
 -restart : replace the current image with input for future processing operations
 -save : save the current working image to the input filename

Basic unary operations:
 -exp   : exponential
 -log   : natural logarithm
 -sin   : sine function
 -cos   : cosine function
 -tan   : tangent function
 -asin  : arc sine function
 -acos  : arc cosine function
 -atan  : arc tangent function
 -sqr   : square
 -sqrt  : square root
 -recip : reciprocal (1/current image)
 -abs   : absolute value
 -bin   : use (current image>0) to binarise
 -binv  : binarise and invert (binarisation and logical inversion)
 -fillh : fill holes in a binary mask (holes are internal - i.e. do not touch the edge of the FOV)
 -fillh26 : fill holes using 26 connectivity
 -index : replace each nonzero voxel with a unique (subject to wrapping) index number
 -grid <value> <spacing> : add a 3D grid of intensity <value> with grid spacing <spacing>
 -edge  : edge strength
 -tfce <H> <E> <connectivity>: enhance with TFCE, e.g. -tfce 2 0.5 6 (maybe change 6 to 26 for skeletons)
 -tfceS <H> <E> <connectivity> <X> <Y> <Z> <tfce_thresh>: show support area for voxel (X,Y,Z)
 -nan   : replace NaNs (improper numbers) with 0
 -nanm  : make NaN (improper number) mask with 1 for NaN voxels, 0 otherwise
 -rand  : add uniform noise (range 0:1)
 -randn : add Gaussian noise (mean=0 sigma=1)
 -inm <mean> :  (-i i ip.c) intensity normalisation (per 3D volume mean)
 -ing <mean> :  (-I i ip.c) intensity normalisation, global 4D mean)
 -range : set the output calmin/max to full data range

Matrix operations:
 -tensor_decomp : convert a 4D (6-timepoint )tensor image into L1,2,3,FA,MD,MO,V1,2,3 (remaining image in pipeline is FA)

Kernel operations (set BEFORE filtering operation if desired):
 -kernel 3D : 3x3x3 box centered on target voxel (set as default kernel)
 -kernel 2D : 3x3x1 box centered on target voxel
 -kernel box    <size>     : all voxels in a cube of width <size> mm centered on target voxel
 -kernel boxv   <size>     : all voxels in a cube of width <size> voxels centered on target voxel, CAUTION: size should be an odd number
 -kernel boxv3  <X> <Y> <Z>: all voxels in a cuboid of dimensions X x Y x Z centered on target voxel, CAUTION: size should be an odd number
 -kernel gauss  <sigma>    : gaussian kernel (sigma in mm, not voxels)
 -kernel sphere <size>     : all voxels in a sphere of radius <size> mm centered on target voxel
 -kernel file   <filename> : use external file as kernel

Spatial Filtering operations: N.B. all options apart from -s use the default kernel or that _previously_ specified by -kernel
 -dilM    : Mean Dilation of non-zero voxels
 -dilD    : Modal Dilation of non-zero voxels
 -dilF    : Maximum filtering of all voxels
 -dilall  : Apply -dilM repeatedly until the entire FOV is covered
 -ero     : Erode by zeroing non-zero voxels when zero voxels found in kernel
 -eroF    : Minimum filtering of all voxels
 -fmedian : Median Filtering 
 -fmean   : Mean filtering, kernel weighted (conventionally used with gauss kernel)
 -fmeanu  : Mean filtering, kernel weighted, un-normalised (gives edge effects)
 -s <sigma> : create a gauss kernel of sigma mm and perform mean filtering
 -subsamp2  : downsamples image by a factor of 2 (keeping new voxels centred on old)
 -subsamp2offc  : downsamples image by a factor of 2 (non-centred)

Dimensionality reduction operations:
  (the "T" can be replaced by X, Y or Z to collapse across a different dimension)
 -Tmean   : mean across time
 -Tstd    : standard deviation across time
 -Tmax    : max across time
 -Tmaxn   : time index of max across time
 -Tmin    : min across time
 -Tmedian : median across time
 -Tperc <percentage> : nth percentile (0-100) of FULL RANGE across time
 -Tar1    : temporal AR(1) coefficient (use -odt float and probably demean first)

Basic statistical operations:
 -pval    : Nonparametric uncorrected P-value, assuming timepoints are the permutations; first timepoint is actual (unpermuted) stats image
 -pval0   : Same as -pval, but treat zeros as missing data
 -cpval   : Same as -pval, but gives FWE corrected P-values
 -ztop    : Convert Z-stat to (uncorrected) P
 -ptoz    : Convert (uncorrected) P to Z
 -rank    : Convert data to ranks (over T dim)
 -ranknorm: Transform to Normal dist via ranks

Multi-argument operations:
 -roi <xmin> <xsize> <ymin> <ysize> <zmin> <zsize> <tmin> <tsize> : zero outside roi (using voxel coordinates). Inputting -1 for a size will set it to the full image extent for that dimension.
 -bptf  <hp_sigma> <lp_sigma> : (-t in ip.c) Bandpass temporal filtering; nonlinear highpass and Gaussian linear lowpass (with sigmas in volumes, not seconds); set either sigma<0 to skip that filter
 -roc <AROC-thresh> <outfile> [4Dnoiseonly] <truth> : take (normally binary) truth and test current image in ROC analysis against truth. <AROC-thresh> is usually 0.05 and is limit of Area-under-ROC measure FP axis. <outfile> is a text file of the ROC curve (triplets of values: FP TP threshold). If the truth image contains negative voxels these get excluded from all calculations. If <AROC-thresh> is positive then the [4Dnoiseonly] option needs to be set, and the FP rate is determined from this noise-only data, and is set to be the fraction of timepoints where any FP (anywhere) is seen, as found in the noise-only 4d-dataset. This is then controlling the FWE rate. If <AROC-thresh> is negative the FP rate is calculated from the zero-value parts of the <truth> image, this time averaging voxelwise FP rate over all timepoints. In both cases the TP rate is the average fraction of truth=positive voxels correctly found.

Combining 4D and 3D images:
 If you apply a Binary operation (one that takes the current image and a new image together), when one is 3D and the other is 4D,
 the 3D image is cloned temporally to match the temporal dimensions of the 4D image.

e.g. fslmaths inputVolume -add inputVolume2 output_volume
     fslmaths inputVolume -add 2.5 output_volume
     fslmaths inputVolume -add 2.5 -mul inputVolume2 output_volume

     fslmaths 4D_inputVolume -Tmean -mul -1 -add 4D_inputVolume demeaned_4D_inputVolume

基本的な使い方は、以下の通り。

fslmaths <入力画像> -mas <マスク画像> <出力画像>

使用例

頭蓋除去されていないFA画像とマスク画像(緑)を、重ね合わせて表示した画像を以下に示す。

頭蓋除去されていないFA画像(FA.nii.gz)をマスク画像(mask.nii.gz)でマスキングするには、以下のコマンドを実行する。

fslmaths FA.nii.gz -mas mask.nii.gz FA_masked.nii.gz

マスキングして、頭蓋除去したFA画像は以下。

MRtrixを用いる場合

コマンド

MRtrixで画像の切り取り・マスキングをするには、mrcalc-multオプションを使用する。

mrcalcのヘルプは、次の通り。

クリックして展開
SYNOPSIS

     Apply generic voxel-wise mathematical operations to images

USAGE

     mrcalc [ options ] operand [ operand ... ]

        operand      an input image, intensity value, or the special keywords
                     'rand' (random number between 0 and 1) or 'randn' (random
                     number from unit std.dev. normal distribution) or the
                     mathematical constants 'e' and 'pi'.


DESCRIPTION

     This command will only compute per-voxel operations. Use 'mrmath' to
     compute summary statistics across images or along image axes.

     This command uses a stack-based syntax, with operators (specified using
     options) operating on the top-most entries (i.e. images or values) in the
     stack. Operands (values or images) are pushed onto the stack in the order
     they appear (as arguments) on the command-line, and operators (specified
     as options) operate on and consume the top-most entries in the stack, and
     push their output as a new entry on the stack.

     As an additional feature, this command will allow images with different
     dimensions to be processed, provided they satisfy the following
     conditions: for each axis, the dimensions match if they are the same size,
     or one of them has size one. In the latter case, the entire image will be
     replicated along that axis. This allows for example a 4D image of size [ X
     Y Z N ] to be added to a 3D image of size [ X Y Z ], as if it consisted of
     N copies of the 3D image along the 4th axis (the missing dimension is
     assumed to have size 1). Another example would a single-voxel 4D image of
     size [ 1 1 1 N ], multiplied by a 3D image of size [ X Y Z ], which would
     allow the creation of a 4D image where each volume consists of the 3D
     image scaled by the corresponding value for that volume in the
     single-voxel image.

EXAMPLE USAGES

     Double the value stored in every voxel:
       $ mrcalc a.mif 2 -mult r.mif
     This performs the operation: r = 2*a  for every voxel a,r in images a.mif
     and r.mif respectively.

     A more complex example:
       $ mrcalc a.mif -neg b.mif -div -exp 9.3 -mult r.mif
     This performs the operation: r = 9.3*exp(-a/b)

     Another complex example:
       $ mrcalc a.mif b.mif -add c.mif d.mif -mult 4.2 -add -div r.mif
     This performs: r = (a+b)/(c*d+4.2).

     Rescale the densities in a SH l=0 image:
       $ mrcalc ODF_CSF.mif 4 pi -mult -sqrt -div ODF_CSF_scaled.mif
     This applies the spherical harmonic basis scaling factor: 1.0/sqrt(4*pi),
     such that a single-tissue voxel containing the same intensities as the
     response function of that tissue should contain the value 1.0.

basic operations

  -abs  (multiple uses permitted)
     |%1| : return absolute value (magnitude) of real or complex number

  -neg  (multiple uses permitted)
     -%1 : negative value

  -add  (multiple uses permitted)
     (%1 + %2) : add values

  -subtract  (multiple uses permitted)
     (%1 - %2) : subtract nth operand from (n-1)th

  -multiply  (multiple uses permitted)
     (%1 * %2) : multiply values

  -divide  (multiple uses permitted)
     (%1 / %2) : divide (n-1)th operand by nth

  -min  (multiple uses permitted)
     min (%1, %2) : smallest of last two operands

  -max  (multiple uses permitted)
     max (%1, %2) : greatest of last two operands

comparison operators

  -lt  (multiple uses permitted)
     (%1 < %2) : less-than operator (true=1, false=0)

  -gt  (multiple uses permitted)
     (%1 > %2) : greater-than operator (true=1, false=0)

  -le  (multiple uses permitted)
     (%1 <= %2) : less-than-or-equal-to operator (true=1, false=0)

  -ge  (multiple uses permitted)
     (%1 >= %2) : greater-than-or-equal-to operator (true=1, false=0)

  -eq  (multiple uses permitted)
     (%1 == %2) : equal-to operator (true=1, false=0)

  -neq  (multiple uses permitted)
     (%1 != %2) : not-equal-to operator (true=1, false=0)

conditional operators

  -if  (multiple uses permitted)
     (%1 ? %2 : %3) : if first operand is true (non-zero), return second
     operand, otherwise return third operand

  -replace  (multiple uses permitted)
     (%1, %2 -> %3) : Wherever first operand is equal to the second operand,
     replace with third operand

power functions

  -sqrt  (multiple uses permitted)
     sqrt (%1) : square root

  -pow  (multiple uses permitted)
     %1^%2 : raise (n-1)th operand to nth power

nearest integer operations

  -round  (multiple uses permitted)
     round (%1) : round to nearest integer

  -ceil  (multiple uses permitted)
     ceil (%1) : round up to nearest integer

  -floor  (multiple uses permitted)
     floor (%1) : round down to nearest integer

logical operators

  -not  (multiple uses permitted)
     !%1 : NOT operator: true (1) if operand is false (i.e. zero)

  -and  (multiple uses permitted)
     (%1 && %2) : AND operator: true (1) if both operands are true (i.e.
     non-zero)

  -or  (multiple uses permitted)
     (%1 || %2) : OR operator: true (1) if either operand is true (i.e.
     non-zero)

  -xor  (multiple uses permitted)
     (%1 ^^ %2) : XOR operator: true (1) if only one of the operands is true
     (i.e. non-zero)

classification functions

  -isnan  (multiple uses permitted)
     isnan (%1) : true (1) if operand is not-a-number (NaN)

  -isinf  (multiple uses permitted)
     isinf (%1) : true (1) if operand is infinite (Inf)

  -finite  (multiple uses permitted)
     finite (%1) : true (1) if operand is finite (i.e. not NaN or Inf)

complex numbers

  -complex  (multiple uses permitted)
     (%1 + %2 i) : create complex number using the last two operands as
     real,imaginary components

  -polar  (multiple uses permitted)
     (%1 /_ %2) : create complex number using the last two operands as
     magnitude,phase components (phase in radians)

  -real  (multiple uses permitted)
     real (%1) : real part of complex number

  -imag  (multiple uses permitted)
     imag (%1) : imaginary part of complex number

  -phase  (multiple uses permitted)
     phase (%1) : phase of complex number (use -abs for magnitude)

  -conj  (multiple uses permitted)
     conj (%1) : complex conjugate

  -proj  (multiple uses permitted)
     proj (%1) : projection onto the Riemann sphere

exponential functions

  -exp  (multiple uses permitted)
     exp (%1) : exponential function

  -log  (multiple uses permitted)
     log (%1) : natural logarithm

  -log10  (multiple uses permitted)
     log10 (%1) : common logarithm

trigonometric functions

  -cos  (multiple uses permitted)
     cos (%1) : cosine

  -sin  (multiple uses permitted)
     sin (%1) : sine

  -tan  (multiple uses permitted)
     tan (%1) : tangent

  -acos  (multiple uses permitted)
     acos (%1) : inverse cosine

  -asin  (multiple uses permitted)
     asin (%1) : inverse sine

  -atan  (multiple uses permitted)
     atan (%1) : inverse tangent

hyperbolic functions

  -cosh  (multiple uses permitted)
     cosh (%1) : hyperbolic cosine

  -sinh  (multiple uses permitted)
     sinh (%1) : hyperbolic sine

  -tanh  (multiple uses permitted)
     tanh (%1) : hyperbolic tangent

  -acosh  (multiple uses permitted)
     acosh (%1) : inverse hyperbolic cosine

  -asinh  (multiple uses permitted)
     asinh (%1) : inverse hyperbolic sine

  -atanh  (multiple uses permitted)
     atanh (%1) : inverse hyperbolic tangent

Data type options

  -datatype spec
     specify output image data type. Valid choices are: float32, float32le,
     float32be, float64, float64le, float64be, int64, uint64, int64le,
     uint64le, int64be, uint64be, int32, uint32, int32le, uint32le, int32be,
     uint32be, int16, uint16, int16le, uint16le, int16be, uint16be, cfloat32,
     cfloat32le, cfloat32be, cfloat64, cfloat64le, cfloat64be, int8, uint8,
     bit.

Standard options

  -info
     display information messages.

  -quiet
     do not display information messages or progress status; alternatively,
     this can be achieved by setting the MRTRIX_QUIET environment variable to a
     non-empty string.

  -debug
     display debugging messages.

  -force
     force overwrite of output files (caution: using the same file as input and
     output might cause unexpected behaviour).

  -nthreads number
     use this number of threads in multi-threaded applications (set to 0 to
     disable multi-threading).

  -config key value  (multiple uses permitted)
     temporarily set the value of an MRtrix config file entry.

  -help
     display this information page and exit.

  -version
     display version information and exit.

基本的な使い方は、以下の通り。入力画像とバイナリーマスク画像(二値画像)を掛け算することで、マスキングをする。

mrcalc <入力画像> <バイナリーマスク画像> -mult <出力画像>

使用例

頭蓋除去されていないFA画像とマスク画像(緑)を、重ね合わせて表示した画像を以下に示す。

頭蓋除去されていないFA画像(FA.nii.gz)をマスク画像(mask.nii.gz)でマスキングするには、以下のコマンドを実行する。

mrcalc FA.nii.gz  mask.nii.gz -mult FA_masked.nii.gz

マスキングして、頭蓋除去したFA画像は以下。

【FSL/MRtrix】FSL/MRtrixを用いたしきい値処理とマスク画像の作成


1. 目的
2. FSLを用いる場合
2.1. コマンド
2.2. ノイズ除去(デノイズ)
2.3. 複数のラベルから1部のラベルを抽出
3. MRtrixを用いる場合
3.1. コマンド
3.2. ノイズ除去(デノイズ)
3.3. 複数のラベルから1部のラベルを抽出


1. 目的

  • FSL/MRtrixを用いたしきい値処理とマスク画像の作成

2. FSLを用いる場合

2.1. コマンド

FSLfslmathsを用いる。fslmathsは、画像の四則演算からしきい値処理、フィルタリングなど基本的な画像処理を実行することができるコマンドである。

fslmathsのヘルプは、次の通り。

クリックして展開
Usage: fslmaths [-dt <datatype>] <first_input> [operations and inputs] <output> [-odt <datatype>]

Datatype information:
 -dt sets the datatype used internally for calculations (default float for all except double images)
 -odt sets the output datatype ( default is float )
 Possible datatypes are: char short int float double input
 "input" will set the datatype to that of the original image

Binary operations:
  (some inputs can be either an image or a number)
 -add   : add following input to current image
 -sub   : subtract following input from current image
 -mul   : multiply current image by following input
 -div   : divide current image by following input
 -rem   : modulus remainder - divide current image by following input and take remainder
 -mas   : use (following image>0) to mask current image
 -thr   : use following number to threshold current image (zero anything below the number)
 -thrp  : use following percentage (0-100) of ROBUST RANGE to threshold current image (zero anything below the number)
 -thrP  : use following percentage (0-100) of ROBUST RANGE of non-zero voxels and threshold below
 -uthr  : use following number to upper-threshold current image (zero anything above the number)
 -uthrp : use following percentage (0-100) of ROBUST RANGE to upper-threshold current image (zero anything above the number)
 -uthrP : use following percentage (0-100) of ROBUST RANGE of non-zero voxels and threshold above
 -max   : take maximum of following input and current image
 -min   : take minimum of following input and current image
 -seed  : seed random number generator with following number
 -restart : replace the current image with input for future processing operations
 -save : save the current working image to the input filename

Basic unary operations:
 -exp   : exponential
 -log   : natural logarithm
 -sin   : sine function
 -cos   : cosine function
 -tan   : tangent function
 -asin  : arc sine function
 -acos  : arc cosine function
 -atan  : arc tangent function
 -sqr   : square
 -sqrt  : square root
 -recip : reciprocal (1/current image)
 -abs   : absolute value
 -bin   : use (current image>0) to binarise
 -binv  : binarise and invert (binarisation and logical inversion)
 -fillh : fill holes in a binary mask (holes are internal - i.e. do not touch the edge of the FOV)
 -fillh26 : fill holes using 26 connectivity
 -index : replace each nonzero voxel with a unique (subject to wrapping) index number
 -grid <value> <spacing> : add a 3D grid of intensity <value> with grid spacing <spacing>
 -edge  : edge strength
 -tfce <H> <E> <connectivity>: enhance with TFCE, e.g. -tfce 2 0.5 6 (maybe change 6 to 26 for skeletons)
 -tfceS <H> <E> <connectivity> <X> <Y> <Z> <tfce_thresh>: show support area for voxel (X,Y,Z)
 -nan   : replace NaNs (improper numbers) with 0
 -nanm  : make NaN (improper number) mask with 1 for NaN voxels, 0 otherwise
 -rand  : add uniform noise (range 0:1)
 -randn : add Gaussian noise (mean=0 sigma=1)
 -inm <mean> :  (-i i ip.c) intensity normalisation (per 3D volume mean)
 -ing <mean> :  (-I i ip.c) intensity normalisation, global 4D mean)
 -range : set the output calmin/max to full data range

Matrix operations:
 -tensor_decomp : convert a 4D (6-timepoint )tensor image into L1,2,3,FA,MD,MO,V1,2,3 (remaining image in pipeline is FA)

Kernel operations (set BEFORE filtering operation if desired):
 -kernel 3D : 3x3x3 box centered on target voxel (set as default kernel)
 -kernel 2D : 3x3x1 box centered on target voxel
 -kernel box    <size>     : all voxels in a cube of width <size> mm centered on target voxel
 -kernel boxv   <size>     : all voxels in a cube of width <size> voxels centered on target voxel, CAUTION: size should be an odd number
 -kernel boxv3  <X> <Y> <Z>: all voxels in a cuboid of dimensions X x Y x Z centered on target voxel, CAUTION: size should be an odd number
 -kernel gauss  <sigma>    : gaussian kernel (sigma in mm, not voxels)
 -kernel sphere <size>     : all voxels in a sphere of radius <size> mm centered on target voxel
 -kernel file   <filename> : use external file as kernel

Spatial Filtering operations: N.B. all options apart from -s use the default kernel or that _previously_ specified by -kernel
 -dilM    : Mean Dilation of non-zero voxels
 -dilD    : Modal Dilation of non-zero voxels
 -dilF    : Maximum filtering of all voxels
 -dilall  : Apply -dilM repeatedly until the entire FOV is covered
 -ero     : Erode by zeroing non-zero voxels when zero voxels found in kernel
 -eroF    : Minimum filtering of all voxels
 -fmedian : Median Filtering 
 -fmean   : Mean filtering, kernel weighted (conventionally used with gauss kernel)
 -fmeanu  : Mean filtering, kernel weighted, un-normalised (gives edge effects)
 -s <sigma> : create a gauss kernel of sigma mm and perform mean filtering
 -subsamp2  : downsamples image by a factor of 2 (keeping new voxels centred on old)
 -subsamp2offc  : downsamples image by a factor of 2 (non-centred)

Dimensionality reduction operations:
  (the "T" can be replaced by X, Y or Z to collapse across a different dimension)
 -Tmean   : mean across time
 -Tstd    : standard deviation across time
 -Tmax    : max across time
 -Tmaxn   : time index of max across time
 -Tmin    : min across time
 -Tmedian : median across time
 -Tperc <percentage> : nth percentile (0-100) of FULL RANGE across time
 -Tar1    : temporal AR(1) coefficient (use -odt float and probably demean first)

Basic statistical operations:
 -pval    : Nonparametric uncorrected P-value, assuming timepoints are the permutations; first timepoint is actual (unpermuted) stats image
 -pval0   : Same as -pval, but treat zeros as missing data
 -cpval   : Same as -pval, but gives FWE corrected P-values
 -ztop    : Convert Z-stat to (uncorrected) P
 -ptoz    : Convert (uncorrected) P to Z
 -rank    : Convert data to ranks (over T dim)
 -ranknorm: Transform to Normal dist via ranks

Multi-argument operations:
 -roi <xmin> <xsize> <ymin> <ysize> <zmin> <zsize> <tmin> <tsize> : zero outside roi (using voxel coordinates). Inputting -1 for a size will set it to the full image extent for that dimension.
 -bptf  <hp_sigma> <lp_sigma> : (-t in ip.c) Bandpass temporal filtering; nonlinear highpass and Gaussian linear lowpass (with sigmas in volumes, not seconds); set either sigma<0 to skip that filter
 -roc <AROC-thresh> <outfile> [4Dnoiseonly] <truth> : take (normally binary) truth and test current image in ROC analysis against truth. <AROC-thresh> is usually 0.05 and is limit of Area-under-ROC measure FP axis. <outfile> is a text file of the ROC curve (triplets of values: FP TP threshold). If the truth image contains negative voxels these get excluded from all calculations. If <AROC-thresh> is positive then the [4Dnoiseonly] option needs to be set, and the FP rate is determined from this noise-only data, and is set to be the fraction of timepoints where any FP (anywhere) is seen, as found in the noise-only 4d-dataset. This is then controlling the FWE rate. If <AROC-thresh> is negative the FP rate is calculated from the zero-value parts of the <truth> image, this time averaging voxelwise FP rate over all timepoints. In both cases the TP rate is the average fraction of truth=positive voxels correctly found.

Combining 4D and 3D images:
 If you apply a Binary operation (one that takes the current image and a new image together), when one is 3D and the other is 4D,
 the 3D image is cloned temporally to match the temporal dimensions of the 4D image.

e.g. fslmaths inputVolume -add inputVolume2 output_volume
     fslmaths inputVolume -add 2.5 output_volume
     fslmaths inputVolume -add 2.5 -mul inputVolume2 output_volume

     fslmaths 4D_inputVolume -Tmean -mul -1 -add 4D_inputVolume demeaned_4D_inputVolume

基本的な使い方は、以下の通り。

fslmaths  <入力画像1> [演算子あるいは入力画像] <出力画像> 

2.2. ノイズ除去(デノイズ)

ここでは、特にしきい値処理で用いる-thr-uthrオプション、さらにバイナリーマスク作成に必要な-binオプションを例にfslmathsコマンドの使い方を解説する。

例えば、拡散MRI(b=0, SE-EPI, DWI_b0.nii.gz)に対して、二値化処理し脳マスク画像を生成する場合、以下のようなコマンドになる。

fslmaths DWI_b0.nii.gz -bin DWI_b0_mask.nii.gz

生成した脳マスク画像(緑)と拡散MRI(b=0, SE-EPI, DWI_b0.nii.gz)を重ね合わせてみる。脳以外の領域に至るまでマスキングしていることが分かる。

脳周囲のノイズ信号値を確認すると、0~30程度であった。

そこで、信号値30以下をカットするようにしきい値処理をするために、-thrオプションを用いる。

fslmaths DWI_b0.nii.gz -thr 30 -bin DWI_b0_mask_thr30.nii.gz

しきい値処理をして生成した脳マスク画像(緑)と拡散MRI(b=0, SE-EPI, DWI_b0.nii.gz)を重ね合わせてみる。ノイズ部分のマスキングが解消されていることが分かる。

2.3. 複数のラベルから1部のラベルを抽出

以下のような、CSF/GM/WMのラベル(CSF: 1, GM: 2, WM: 3)があったとする。

この内、GMのみを抽出したい場合、下限値-thrおよび上限値-uthr共に信号値2になるように設定すればよい。

fslmaths CSF_GM_WM_seg.nii.gz -thr 2 -uthr 2 GM.nii.gz

CSF/GM/WMのラベルからGMのラベルのみが抽出される。

3. MRtrixを用いる場合

3.1. コマンド

MRtrixmrthresholdを用いる。mrthresholdは、画像のしきい値処理に用いるコマンドである。

mrthresholdのヘルプは、次の通り。

クリックして展開
USAGE

     mrthreshold [ options ] input [ output ]

        input        the input image to be thresholded

        output       the (optional) output binary image mask


DESCRIPTION

     The threshold value to be applied can be determined in one of a number of
     ways:

     - If no relevant command-line option is used, the command will
     automatically determine an optimal threshold;

     - The -abs option provides the threshold value explicitly;

     - The -percentile, -top and -bottom options enable more fine-grained
     control over how the threshold value is determined.

     The -mask option only influences those image values that contribute toward
     the determination of the threshold value; once the threshold is
     determined, it is applied to the entire image, irrespective of use of the
     -mask option. If you wish for the voxels outside of the specified mask to
     additionally be excluded from the output mask, this can be achieved by
     providing the -out_masked option.

     The four operators available through the "-comparison" option ("lt", "le",
     "ge" and "gt") correspond to "less-than" (<), "less-than-or-equal" (<=),
     "greater-than-or-equal" (>=) and "greater-than" (>). This offers
     fine-grained control over how the thresholding operation will behave in
     the presence of values equivalent to the threshold. By default, the
     command will select voxels with values greater than or equal to the
     determined threshold ("ge"); unless the -bottom option is used, in which
     case after a threshold is determined from the relevant lowest-valued image
     voxels, those voxels with values less than or equal to that threshold
     ("le") are selected. This provides more fine-grained control than the
     -invert option; the latter is provided for backwards compatibility, but is
     equivalent to selection of the opposite comparison within this selection.

     If no output image path is specified, the command will instead write to
     standard output the determined threshold value.

Threshold determination mechanisms

  -abs value
     specify threshold value as absolute intensity

  -percentile value
     determine threshold based on some percentile of the image intensity
     distribution

  -top count
     determine threshold that will result in selection of some number of
     top-valued voxels

  -bottom count
     determine & apply threshold resulting in selection of some number of
     bottom-valued voxels (note: implies threshold application operator of "le"
     unless otherwise specified)

Threshold determination modifiers

  -allvolumes
     compute a single threshold for all image volumes, rather than an
     individual threshold per volume

  -ignorezero
     ignore zero-valued input values during threshold determination

  -mask image
     compute the threshold based only on values within an input mask image

Threshold application modifiers

  -comparison choice
     comparison operator to use when applying the threshold; options are:
     lt,le,ge,gt (default = "le" for -bottom; "ge" otherwise)

  -invert
     invert the output binary mask (equivalent to flipping the operator;
     provided for backwards compatibility)

  -out_masked
     mask the output image based on the provided input mask image

  -nan
     set voxels that fail the threshold to NaN rather than zero (output image
     will be floating-point rather than binary)

Standard options

  -info
     display information messages.

  -quiet
     do not display information messages or progress status; alternatively,
     this can be achieved by setting the MRTRIX_QUIET environment variable to a
     non-empty string.

  -debug
     display debugging messages.

  -force
     force overwrite of output files (caution: using the same file as input and
     output might cause unexpected behaviour).

  -nthreads number
     use this number of threads in multi-threaded applications (set to 0 to
     disable multi-threading).

  -config key value  (multiple uses permitted)
     temporarily set the value of an MRtrix config file entry.

  -help
     display this information page and exit.

  -version
     display version information and exit.

基本的な使い方は、以下の通り。

mrthreshold [オプション]  <入力画像> <出力画像>

3.2. ノイズ除去(デノイズ)

例えば、拡散MRI(b=0, SE-EPI, DWI_b0.nii.gz)に対して、二値化処理し脳マスク画像を生成する場合、以下のようなコマンドになる。

ここで、-absはしきい値を設定するオプションであり、-comparisonはしきい値に対してどのような操作を実行するのかを指定するオプションである。例えば、-comparisonでは、の4種類(“lt”, “le”, “ge”, “gt”)の操作ができ、それぞれ“less-than” (<), “less-than-or-equal” (<=), “greater-than-or-equal” (>=), “greater-than” (>)を意味する。

mrthreshold -abs 0 -comparison gt DWI_b0.nii.gz DWI_b0_mask.nii.gz

生成した脳マスク画像(緑)と拡散MRI(b=0, SE-EPI, DWI_b0.nii.gz)を重ね合わせてみる。脳以外の領域に至るまでマスキングしていることが分かる。

脳周囲のノイズ信号値を確認すると、0~30程度であった。

そこで、信号値30以下をカットするようにしきい値処理をするために、-abs 30とする。

mrthreshold -abs 30 -comparison gt DWI_b0.nii.gz DWI_b0_mask_thr30.nii.gz

しきい値処理をして生成した脳マスク画像(緑)と拡散MRI(b=0, SE-EPI, DWI_b0.nii.gz)を重ね合わせてみる。ノイズ部分のマスキングが解消されていることが分かる。

3.3. 複数のラベルから1部のラベルを抽出

以下のような、CSF/GM/WMのラベル(CSF: 1, GM: 2, WM: 3)があったとする。

この内、WMのみを抽出したい場合、次のようにコマンドを実行する。

mrthreshold -abs 2 -comparison gt CSF_GM_WM_seg.nii.gz WM.nii.gz

CSF/GM/WMのラベルからWMのラベルのみが抽出される。

著者情報: 斎藤 勇哉

順天堂大学医学部 大学院医学研究科 放射線診断学講座所属
脳MRI 画像解析が専門であり、テーマは①神経変性疾患の機序解明、②医用人工知能の開発、③多施設データのハーモナイゼーション、④速読が脳に与える影響や学習効果、⑤SNS解析を用いたマーケティング戦略の改善
医療分野に関わらず、自然言語処理・スクレイピング・データ分析・Web アプリ開発を得意とし、企業や他大学の研究を支援。
主な使用言語は、Python、Shell Script、MATLAB、HTML、CSS

Ubuntu18.04上のFSL6.0.4でeddy_cudaを使う方法

FSLにはeddyという拡散MRI画像の渦電流を補正するプログラムが搭載されています。
かつてはeddy_correctというシンプルなプログラムでしたが、
今のeddyは、計算量がとてつもなく大きな(=処理時間がかかる)プログラムとなっています。

Liux版のFSLには、eddy_openmp というCPU版と、eddy_cuda{8.0,9.1}というGPU版があります。

Ubuntu 18.04 が搭載されているLinuxで NVIDIA製のグラフィックボードが搭載されている場合、eddy_cudaを比較的簡単にセットアップできるので紹介します。

注意:NVIDIAのドライバを入れる時点で、ディスプレイの解像度が変になることがあります。現在の実働マシンに使う場合は相当注意しながら行ってください。個々人の環境があまりにも違うのでこの方法で不具合が起こっても責任は負いかねます。(すでに3台のマシンでセットアップを行い問題ないことを確認していますが…)

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fslroiやmrconvertを使ってNIfTI画像からスライスを1枚だけ削除する方法

拡散画像からZ軸(上下)方向に1枚だけスライスを除く必要がありました。

どんな方法があるか調べていたところ、fslroiがいいなと思いました。
そして、慶応大学病院の上田先生からMrtrixについてくるmrconvertでも同様のことができることを教わりました。
自分の備忘録も兼ねてここに記載しておきます。

実際に試せるように、サンプル画像を準備しました。この画像をベースに説明します。

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