1. 目的
2. 脳画像解析における拡散MRIの問題点
2.1. 磁化率効果に起因する画像の幾何学的歪み（位相エンコードをAP方向として撮像した場合）
2.2. 撮像中の頭の動き
3. 使用するソフトウェア
3.1. TOPUP
3.1.1. TOPUP
3.2. –indexについて
3.2.1. TOPUP
3.3. EDDY
3.4. 頭の動き補正の効果
4. サンプルコード
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
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。
5ttgenのヘルプは、次の通り。
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>
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
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
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。
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。
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
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
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。
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>
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
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。
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
1. 目的
2. 準備
2.1. DIPYのインストール
2.2. 使用データ
3. 拡散MRIのノイズ除去
3.1. 必要なパッケージをインポート
3.2. 画像およびMPG軸情報の読み込み
3.3. ノイズ除去（デノイズ）
3.4. NIfTI形式で保存
3.5. 結果
4. おまけ
pip3 install dipy
データを次のフォルダ構造で用意する。
Study/
└── Subject
├── DWI.nii.gz # 拡散MRI
├── DWI_mask.nii.gz # 拡散MRIマスク画像
├── bvals # b-values
└── bvecs # b-vectors
Pythonで以下のコマンドを実行。
import numpy as np import matplotlib.pyplot as plt from time import time from dipy.denoise.localpca import mppca from dipy.core.gradients import gradient_table from dipy.io.image import load_nifti, save_nifti from dipy.io.gradients import read_bvals_bvecs from dipy.segment.mask import median_otsu
DWI_FILE = 'DWI.nii.gz' BVALS_FILE = 'bvals' BVECS_FILE = 'bvecs' data, affine = load_nifti(DWI_FILE) bvals, bvecs = read_bvals_bvecs(BVALS_FILE, BVECS_FILE) gtab = gradient_table(bvals, bvecs)
mppca関数を用いて、Marchenko-Pastur PCAを用いたデノイズをする。
denoised_arr = mppca(data, patch_radius=3)
save_nifti関数で、画像をNIfTI形式で保存する。
save_nifti('DWI_denoised.nii.gz', denoised_arr.astype(np.float32), affine)
拡散強調像（b=2000 s/mm^2）のデノイズ前後の比較と差分画像は、以下。
実際に、デノイズ前（上段）とデノイズ後（下段）でDTIおよびDKIを計算し、比較してみる。
Marchenko-Pastur PCAアルゴリズムは、ノイズの標準偏差も推定することができる。ノイズの標準偏差を算出するためには、return_sigmaのフラグを「True」にする。
denoised_arr, sigma = mppca(data, patch_radius=3, return_sigma=True)
save_nifti('DWI_noise_sigma.nii.gz', sigma .astype(np.float32), affine)
ノイズの標準偏差マップは、以下の通り。
脳領域における平均ノイズ標準偏差は、次のようにして計測できる。
mean_sigma = np.mean(sigma[mask]) print(mean_sigma)
推定した平均ノイズ標準偏差を用いて、b=0 (s/mm^2)画像のSNRを算出する。
b0 = denoised_arr[..., 0] mean_signal = np.mean(b0[mask]) snr = mean_signal / mean_sigma print(snr)
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
拡散MRIのノイズ除去には、MRtrixのdwidenoise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。
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>
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。
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
1. 目的
2. Boundary-Based Registration (BBR)とは
3. コマンド
4. 目的使用例
4.1. 目的前準備
4.2. 目的Boundary-Based Registration: BBB
Doug Greve氏によって開発されたEPI画像（拡散MRIや機能的MRI）用の位置合わせツールで、EPI画像と構造MRI画像（例：T1WI）との位置合わせで、灰白質・白質境界（Boundary）を頼りに位置合わせをする。以下のウェブサイトに、詳細な説明と分かりやすい図がある。
Boundary-Based Registration (BBR) に基づいた構造MRIと拡散MRIの位置合わせをFSLで実装するには、epi_regを用いる。
epi_regのヘルプは、以下の通り。
Usage: epi_reg [options] --epi=<EPI image> --t1=<wholehead T1 image> --t1brain=<brain extracted T1 image> --out=<output name> Optional arguments --fmap=<image> : fieldmap image (in rad/s) --fmapmag=<image> : fieldmap magnitude image - wholehead extracted --fmapmagbrain=<image> : fieldmap magnitude image - brain extracted --gdc=<image> : Gradient-distortion corection warpfield --wmseg=<image> : white matter segmentation of T1 image --echospacing=<val> : Effective EPI echo spacing (sometimes called dwell time) - in seconds --pedir=<dir> : phase encoding direction, dir = x/y/z/-x/-y/-z --weight=<image> : weighting image (in T1 space) --nofmapreg : do not perform registration of fmap to T1 (use if fmap already registered) --noclean : do not clean up intermediate files -v : verbose output -h : display this help message e.g.: epi_reg --epi=example_func --t1=struct --t1brain=struct_brain --out=epi2struct --fmap=fmap_rads --fmapmag=fmap_mag --fmapmagbrain=fmap_mag_brain --echospacing=0.0005 --pedir=-y Note that if parallel acceleration is used in the EPI acquisition then the *effective* echo spacing is the actual echo spacing between acquired lines in k-space divided by the acceleration factor.
基本的な使い方は、以下の通り。
epi_reg --epi=<b=0画像(spin-echo EPI)> --t1=<頭蓋除去前の3D-T1WI> --t1brain=<頭蓋除去後の3D-T1WI> --echospacing=<echo spacing時間(sec)> --pedir=<位相エンコード方向> --out=<出力画像>
次のファイルを準備する。
. ├── CSF_GM_WM_seg.nii.gz：FASTで作成したCSF, GM, WMのセグメント ├── DWI.nii.gz：拡散MRI画像（b≠0） ├── DWI_b0.nii.gz：拡散MRIのb=0画像（spin-echo EPI） ├── T1_skull_stripped.nii.gz：頭蓋除去後の3D-T1WI └── T1w.nii.gz：頭蓋除去前の3D-T1WI
頭蓋除去とFASTを用いたCSF, GM, WMのセグメントは、以下の記事を参考にするとよい。
また、拡散MRIからb値ごとに画像を抽出する方法は、以下の記事を参考にするとよい。
ここで、構造MRI（3D-T1WI）空間にあるCSF, GM, WMのセグメント（CSF_GM_WM_seg.nii.gz）を、拡散MRIの空間に移動させることを考える。
そのために、まずBBBを使って拡散MRIのb=0画像（spin-echo EPI）を、構造MRI（3D-T1WI）に位置合わせする。
この時、echo spacing時間( sec)--echospacingと位相エンコード方向--pedirを前もって調べておく必要がある。これらの情報は、dcm2niixを用いてDICOM形式からNIfTI形式に変換する際に出力されるJSONファイルに記載されている。dcm2niixの使い方は、以下の記事を参考にするとよい。
準備ができたら、以下のようにepi_regコマンドを実行して、拡散MRIから構造MRIへ位置合わせする変換行列を生成する。
epi_reg --epi=DWI_b0.nii.gz --t1=T1w.nii.gz --t1brain=T1_skull_stripped.nii.gz --echospacing=0.0380544 --pedir=-y --out=DWI2T1w
次に、先ほどの変換行列（拡散MRI空間→構造MRI空間）の逆変換行列（構造MRI空間→拡散MRI空間）を生成する。
convert_xfm -omat T1w2DWI.mat -inverse DWI2T1w.mat
逆変換行列（構造MRI空間→拡散MRI空間）を構造MRI空間にいるCSF, GM, WMのセグメント（CSF_GM_WM_seg.nii.gz）に適用することで、セグメントを拡散MRI空間に移動させる。このとき、-applyxfmと-interp nearestneighbourとしていることに注意する。FLIRTの使い方の詳細はこちら。
flirt -in CSF_GM_WM_seg.nii.gz -ref DWI_b0.nii.gz -out CSF_GM_WM_seg_DWIspace.nii.gz -init T1w2DWI.mat -applyxfm -interp nearestneighbour
処理前後のセグメントの様子をみてみる。
fsleyes DWI.nii.gz \ CSF_GM_WM_seg.nii.gz -cm random \ CSF_GM_WM_seg_DWIspace.nii.gz -cm random
軸位断で後頭葉付近を拡大してみる。
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
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
拡散MRIからb値ごとに画像を抽出するには、MRtrixのdwiextractを用いる。
dwiextractのヘルプは、次の通り。
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
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
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
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
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
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
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。
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>
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
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。
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
注意(16 Apr 2023): FSL 6.0.6 から、CUDA 11以降でもeddy_cuda10.2が動くようになりました。したがって、以下の内容はもう古くなっています。新しい記事をご確認ください。
私のメインマシンは Lin4Neuro 18.04 ですが、そろそろ Lin4Neuro 20.04 への移行を考えています。
今、実験機には NVIDIA GeForce RTX 2070 が備え付けられています。
これを使って、FSL 6.0.5 の eddy をGPUが使えるように設定し、なおかつ、Tensorflow, Pytorch といった Deep Learning のフレームワークも使えるようにしたいと思います。
FSL 6.0.5 にはデフォルトで CUDA 10.2 に対応した eddy_cuda10.2 が配布されています。なので、CUDA 10.2を入れることにします。
なお、これは Ubuntu 18.04 でも全く問題なくできることがわかりましたので、タイトルを変更しました。
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））
MRtrixのmrgridを用いる。
mrgridのヘルプは、次の通り。
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
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
-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
-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
-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
-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
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
FSLのfslroiコマンドを用いる。
fslroiのヘルプは、次の通り。
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>
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
mrconvertのヘルプは、次の通り。
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>
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で画像の切り取り・マスキングをするには、fslmathsと-masオプションを使用する。
fslmathsのヘルプは、次の通り。
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>
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で画像の切り取り・マスキングをするには、mrcalcと-multオプションを使用する。
mrcalcのヘルプは、次の通り。
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>
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。
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
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
FSLのfslmaths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。
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>
ここでは、特にしきい値処理で用いる-thrと-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。
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。
MRtrixのmrthreshold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。
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>
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。
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。
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
1. 目的
2. MRIcronを用いる場合
3. MRIcroGLを用いる場合
ここでは、拡散MRI（b=0, SE-EPI）の脳周囲にあるノイズ除去するため、マスク画像を生成しノイズを除去する方法を解説する。
まず、画像を見てみる。拡散MRI（b=0, SE-EPI）を信号値0-10のスケールで表示すると、以下のような画像が表示される。この内、脳周囲の白い点々（ごま塩ノイズ）は本来観測すべきではない信号、つまりノイズである。これから、このノイズを除去していく。
MRIcronの基本操作は、以下の記事を参考にするとよい。
脳のマスク画像を作るには、ツールタブの「Draw/Intensity filter」を選択。あるいは、「Ctrl + I」を押す。
脳実質が欠けないようにThresholdを設定する。設定ができたら「Save highlighted as NIfTI or VOI」を選択。
保存先を指定して保存する。この時NIfTI形式として保存しておくと、他の脳画像解析ソフトで扱いやすい。
保存した画像を開くと、Intensity filterでしきい値処理された画像が表示される。この時、画像はまだ二値化されていない状態である。
ここで、スケールを最初と同じ0-10に設定して、脳周囲のノイズを確認してみる。脳周囲のノイズを除去されていることが分かる。
この画像を二値化してバイナリーマスク画像を作成したい場合、ツールタブの「Draw/Advanced/Brain mask」を選択し、ファイル名を指定して保存する。
二値化されたバイナリーマスク画像が生成される。
MRIcroGLの基本操作は、以下の記事を参考にするとよい。
脳のマスク画像を作るには、ツールタブの「Draw/Advanced/Intensity Filter」を選択。
「Action: Add to Drawing」となっている状態で、脳実質が欠けないようにしきい値を設定し「Apply」をクリック。
Intensity Filterのしきい値処理が適用されて残った領域が、関心領域として設定される。
関心領域が設定されている状態で、上タブの「Draw/Advanced/Mask Image」から、「Delete/Preserve regions with VOI」を選択。
ここで、スケールを最初と同じ0-10に設定して、脳周囲のノイズを確認してみる。脳周囲のノイズを除去されていることが分かる。
この画像を二値化してバイナリーマスク画像を作成したい場合、ツールタブの「Draw/Save VOI」を選択し、ファイル名を指定して保存する（ショートカットキー：CTRL+S）。
二値化されたバイナリーマスク画像が生成される。
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
1. 目的
2. コマンド
3. 使用例
3.1. 最小値と最大値
3.2. ボクセル数および容積
3.3. 平均値と標準偏差
3.4. マスク画像を用いた計測
FSLのfslstatsコマンドを用いて、定量値を算出することが可能。
fslstatsのヘルプは次の通り。
Usage: fslstats [preoptions] <input> [options] preoption -t will give a separate output line for each 3D volume of a 4D timeseries preoption -K < indexMask > will generate seperate n submasks from indexMask, for indexvalues 1..n where n is the maximum index value in indexMask, and generate statistics for each submask Note - options are applied in order, e.g. -M -l 10 -M will report the non-zero mean, apply a threshold and then report the new nonzero mean -l <lthresh> : set lower threshold -u <uthresh> : set upper threshold -r : output <robust min intensity> <robust max intensity> -R : output <min intensity> <max intensity> -e : output mean entropy ; mean(-i*ln(i)) -E : output mean entropy (of nonzero voxels) -v : output <voxels> <volume> -V : output <voxels> <volume> (for nonzero voxels) -m : output mean -M : output mean (for nonzero voxels) -s : output standard deviation -S : output standard deviation (for nonzero voxels) -w : output smallest ROI <xmin> <xsize> <ymin> <ysize> <zmin> <zsize> <tmin> <tsize> containing nonzero voxels -x : output co-ordinates of maximum voxel -X : output co-ordinates of minimum voxel -c : output centre-of-gravity (cog) in mm coordinates -C : output centre-of-gravity (cog) in voxel coordinates -p <n> : output nth percentile (n between 0 and 100) -P <n> : output nth percentile (for nonzero voxels) -a : use absolute values of all image intensities -n : treat NaN or Inf as zero for subsequent stats -k <mask> : use the specified image (filename) for masking - overrides lower and upper thresholds -d <image> : take the difference between the base image and the image specified here -h <nbins> : output a histogram (for the thresholded/masked voxels only) with nbins -H <nbins> <min> <max> : output a histogram (for the thresholded/masked voxels only) with nbins and histogram limits of min and max Note - thresholds are not inclusive ie lthresh<allowed<uthresh
基本的な使い方は、次の通り。
# 基本 fslstats <入力画像> [オプション] # マスクを適用する場合 fslstats <入力画像> -mas <マスク画像> [オプション]
fslstatsコマンドで、よくある使用例を紹介する。
最小値と最大値は、オプション-Rを用いて計測する。
以下では、頭蓋除去済みの脳画像（T1_skull_stripped.nii.gz）における最小値と最大値を計測している。
fslstats T1_skull_stripped.nii.gz -R
0.000000 1307.000000
ボクセル数および容積の計測は、オプション-Vを用いる。似ているオプションで-vがあるが、オプション-Vでは、信号値が0あるいはnanでない領域を対象に、計測する。他にも小文字・大文字で区別しているオプションがあるが、信号値がある領域のみを対象にしたい場合は、基本的に大文字オプション（例：-V, -M, -S）で計測するとよい。
以下では、灰白質（GM_seg.nii.gz）の容積を計測している。
fslstats GM_seg.nii.gz -V
882175 697814.250000 # 左からボクセル数、容積（mm^3）
平均値を計測するにはオプション-Mを、標準偏差を計測するにはオプション-Sを用いる。
以下では、FA（FA.nii.gz）の平均値を算出している。
fslstats FA.nii.gz -M
0.276669
以下では、FA（FA.nii.gz）の標準偏差を算出している。
fslstats FA.nii.gz -S
0.186857
計測したい領域がある場合、オプション-kで領域（マスク画像）を指定して計測するとよい。
例えば、白質（WM_seg.nii.gz）領域における、FAの平均値を計測したい場合、次のようになる。
fslstats FA.nii.gz -k WM_seg.nii.gz -M
0.507313
計測したい領域が複数あり、それらの領域にインデックス（値）が割り振られている画像（例：CSF=1, GM=2, WM=3の画像）があるとき、オプション-Kを用いると便利である。
例えば、白質を48領域分割したアトラス（JHU-ICBM-labels-1mm_indiv.nii.gz）を用いて平均値を計測する場合、次のようになる。
fslstats -K JHU-ICBM-labels-1mm_indiv.nii.gz FA.nii.gz -M
0.491841
0.512530
0.591286
0.667444
... [省略]
0.294148
1. 目的
2. 必要なファイル
3. 実行
4. 実際に実行されているコマンド
5. 出力画像
必要なファイルは次の通り。
BEDPOSTXの使い方はこちら。
. ├── DTI.bedpostX # BEDPOSTXの出力フォルダ ├── T1.nii.gz # BET前のBrain 3D-T1WI (FNIRT用) └── T1_brain.nii.gz # BET後のBrain 3D-T1WI (FLIRT用)
これに加えて、b0画像から脳を抽出した画像、nodif_brain.nii.gz を DTI.bedpostX にコピーしておく必要がある。
ターミナル（端末）でfslと入力。
fsl
Windowが立ち上がったら、「FDT diffusion」を選択。
各項目ごとにファイルを選択。注意すべきことは次の通り。
以上の設定ができたら、「Go」を選択して位置合わせを実行する。
上記の操作を実行すると、ターミナル上にFSLのコマンドが生成され位置合わせが実行される。その時のコマンドは次の通り。
flirt -in DTI.bedpostX/nodif_brain \
-ref T1_brain.nii.gz \
-omat DTI.bedpostX/xfms/diff2str.mat \
-searchrx -180 180 -searchry -180 180 -searchrz -180 180 \
-dof 6 -cost corratio
convert_xfm -omat DTI.bedpostX/xfms/str2diff.mat \
-inverse DTI.bedpostX/xfms/diff2str.mat
flirt -in T1_brain.nii.gz \
-ref /opt/fsl/data/standard/MNI152_T1_2mm_brain \
-omat DTI.bedpostX/xfms/str2standard.mat \
-searchrx -180 180 -searchry -180 180 -searchrz -180 180 \
-dof 12 -cost corratio
convert_xfm -omat DTI.bedpostX/xfms/standard2str.mat \
-inverse DTI.bedpostX/xfms/str2standard.mat
convert_xfm -omat DTI.bedpostX/xfms/diff2standard.mat \
-concat DTI.bedpostX/xfms/str2standard.mat DTI.bedpostX/xfms/diff2str.mat
convert_xfm -omat DTI.bedpostX/xfms/standard2diff.mat \
-inverse DTI.bedpostX/xfms/diff2standard.mat
fnirt --in=T1.nii.gz \
--aff=DTI.bedpostX/xfms/str2standard.mat \
--cout=DTI.bedpostX/xfms/str2standard_warp \
--config=T1_2_MNI152_2mm
invwarp -w DTI.bedpostX/xfms/str2standard_warp \
-o DTI.bedpostX/xfms/standard2str_warp \
-r T1_brain.nii.gz
convertwarp -o DTI.bedpostX/xfms/diff2standard_warp \
-r /opt/fsl/data/standard/MNI152_T1_2mm \
-m DTI.bedpostX/xfms/diff2str.mat \
-w DTI.bedpostX/xfms/str2standard_warp
convertwarp -o DTI.bedpostX/xfms/standard2diff_warp \
-r DTI.bedpostX/nodif_brain_mask \
-w DTI.bedpostX/xfms/standard2str_warp \
--postmat=DTI.bedpostX/xfms/str2diff.mat
処理が終わると、BEDPOSTXの出力フォルダ（DTI.bedpostX）に位置合わせの出力ファイルが保存される。
DTI.bedpostX/xfms/ ├── diff2standard.mat ├── diff2standard_warp.nii.gz ├── diff2str.mat ├── eye.mat ├── standard2diff.mat ├── standard2diff_warp.nii.gz ├── standard2str.mat ├── standard2str_warp.nii.gz ├── str2diff.mat ├── str2standard.mat └── str2standard_warp.nii.gz
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
BEDPOSTXの実行には、次のようなファイルが必要。
さらに、ファイル名は次のようにしておく必要がある。
Sub001/ ├── bvals # DWIのGradient Table ├── bvecs # DWIのGradient Table ├── data.nii.gz # DWI └── nodif_brain_mask.nii.gz # b=0のマスク
BEDPOSTXは、次のコマンドで実行できる。
bedpostx Sub001 # Usage: bedpostx <subject directory> [options]
次のように、データを用意する。
$ tree HC003/ HC003/ ├── HC003.bval # DWIのGradient Table ├── HC003.bvec # DWIのGradient Table ├── drHC003.nii.gz # DWI └── maskdrHC003.nii.gz # b=0のマスク
この時、BEDPOSTXを実行するために必要なファイルが揃っているかをbedpostx_datacheckで確認することができる。
$ bedpostx_datacheck HC003/ HC003//data does not exist HC003//nodif_brain_mask does not exist num lines in HC003//bvals cat: HC003//bvals: No such file or directory 0 num words in HC003//bvals cat: HC003//bvals: No such file or directory 0 num lines in HC003//bvecs cat: HC003//bvecs: No such file or directory 0 num words in HC003//bvecs cat: HC003//bvecs: No such file or directory 0
ファイル名を修正。
tree HC003 HC003/ ├── bvals ├── bvecs ├── data.nii.gz └── nodif_brain_mask.nii.gz
再度、bedpostx_datacheckを実行する。
$ bedpostx_datacheck HC003/ HC003//data data_type INT16 dim1 120 dim2 120 dim3 84 dim4 137 datatype 4 pixdim1 1.700000 pixdim2 1.700000 pixdim3 1.700000 pixdim4 0.000000 cal_max 0.000000 cal_min 0.000000 file_type NIFTI-1+ HC003//nodif_brain_mask data_type INT16 dim1 120 dim2 120 dim3 84 dim4 1 datatype 4 pixdim1 1.700000 pixdim2 1.700000 pixdim3 1.700000 pixdim4 0.000000 cal_max 0.000000 cal_min 0.000000 file_type NIFTI-1+ num lines in HC003//bvals 1 num words in HC003//bvals 137 num lines in HC003//bvecs 3 num words in HC003//bvecs 411
BEDPOSTXデータのチェックができたら、BEDPOSTXを実行する。
$ bedpostx HC003/ subjectdir is /home/neuro/Documents/Yuya_S/temp/bedpostx/bedpostx_dir/HC003 Making bedpostx directory structure Queuing preprocessing stages ...
BEDPOSTXを実行すると、「
$ ls HC003 HC003.bedpostX
BEDPOSTXで出力されるファイルは次の通り。
結果を確認するには、以下のコマンドを実行。
cd HC003.bedpostX fsleyes mean_fsumsamples.nii.gz \ dyads1.nii.gz -ot linevector -xc 1 0 0 -yc 1 0 0 -zc 1 0 0 -lw 2 \ dyads2_thr0.05.nii.gz -ot linevector -xc 0 1 0 -yc 0 1 0 -zc 0 1 0 -lw 2 \ dyads3_thr0.05.nii.gz -ot linevector -xc 0 0 1 -yc 0 0 1 -zc 0 0 1 -lw 2
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
1. 目的
2. FreeSurferの概要
3. 準備するデータ
4. 実行
5. 結果
5.1. aparc.stats
5.2. wmparc.stats
準備中。。。
準備するデータは、3D-T1WIのみである。
. └── Subj001.nii.gz
FreeSurferのrecon-allの基本的な使い方は、次の通り。Subjects DIR-sdは、被験者データが集められているフォルダを指定する。
recon-all -i <Input 3D-T1WI> -subjid <Subject ID> -all -sd .<Subject DIR>
例えば、次のようにコマンドを打ち込むことで、FreeSurferを実行できる。
recon-all -i Subj001.nii.gz -subjid Subj001 -all -sd .
FreeSurferの処理が完了すると、Subj001/mriフォルダに灰白質（aparc+aseg.mgz）と白質wmparc.mgzが各脳領域ごとに分割された画像が生成される。これを脳画像に重ねて表示するには、次のコマンドを実行する。
freeview -v Subj001/mri/brain.mgz \ Subj001/mri/aparc+aseg.mgz:colormap:lut:opacity=0.2 \ Subj001/mri/wmparc.mgz:colormap:lut:opacity=0.2
以下のような画像が表示される。
また、各脳領域の厚さ・面積・体積・脳回の曲率等の情報がSubj001/statsフォルダに保存される。
aparc.statsには、皮質および深部灰白質の構造情報が記載されている。また、aparc.statsは、左半球 (lh) と右半球 (rh) ごとに保存される（例: lh.aparc.stats）。
lh.aparc.statsの中身は、次の通り。
# Table of FreeSurfer cortical parcellation anatomical statistics # # CreationTime 2020/12/09-20:01:37-GMT # generating_program mris_anatomical_stats # cvs_version $Id: mris_anatomical_stats.c,v 1.79 2016/03/14 15:15:34 greve Exp $ # mrisurf.c-cvs_version $Id: mrisurf.c,v 1.781.2.6 2016/12/27 16:47:14 zkaufman Exp $ # cmdline mris_anatomical_stats -th3 -mgz -cortex ../label/lh.cortex.label -f ../stats/lh.aparc.stats -b -a ../label/lh.aparc.annot -c ../label/aparc.annot.ctab Subj001 lh white # sysname Linux # hostname neuro # machine x86_64 # user neuro # # SUBJECTS_DIR /home/neuro/Documents/Yuya_S/FreeSurfer/2_ANALYZE # anatomy_type surface # subjectname Subj001 # hemi lh # AnnotationFile ../label/lh.aparc.annot # AnnotationFileTimeStamp 2020/12/10 04:34:33 # Measure Cortex, NumVert, Number of Vertices, 134464, unitless # Measure Cortex, WhiteSurfArea, White Surface Total Area, 91536.6, mm^2 # Measure Cortex, MeanThickness, Mean Thickness, 2.5011, mm # Measure BrainSeg, BrainSegVol, Brain Segmentation Volume, 1249289.000000, mm^3 # Measure BrainSegNotVent, BrainSegVolNotVent, Brain Segmentation Volume Without Ventricles, 1227719.000000, mm^3 # Measure BrainSegNotVentSurf, BrainSegVolNotVentSurf, Brain Segmentation Volume Without Ventricles from Surf, 1227368.805429, mm^3 # Measure Cortex, CortexVol Total cortical gray matter volume, 509668.845191, mm^3 # Measure SupraTentorial, SupraTentorialVol, Supratentorial volume, 1091814.805429, mm^3 # Measure SupraTentorialNotVent, SupraTentorialVolNotVent, Supratentorial volume, 1073466.805429, mm^3 # Measure EstimatedTotalIntraCranialVol, eTIV, Estimated Total Intracranial Volume, 1529098.631452, mm^3 # NTableCols 10 # TableCol 1 ColHeader StructName # TableCol 1 FieldName Structure Name # TableCol 1 Units NA # TableCol 2 ColHeader NumVert # TableCol 2 FieldName Number of Vertices # TableCol 2 Units unitless # TableCol 3 ColHeader SurfArea # TableCol 3 FieldName Surface Area # TableCol 3 Units mm^2 # TableCol 4 ColHeader GrayVol # TableCol 4 FieldName Gray Matter Volume # TableCol 4 Units mm^3 # TableCol 5 ColHeader ThickAvg # TableCol 5 FieldName Average Thickness # TableCol 5 Units mm # TableCol 6 ColHeader ThickStd # TableCol 6 FieldName Thickness StdDev # TableCol 6 Units mm # TableCol 7 ColHeader MeanCurv # TableCol 7 FieldName Integrated Rectified Mean Curvature # TableCol 7 Units mm^-1 # TableCol 8 ColHeader GausCurv # TableCol 8 FieldName Integrated Rectified Gaussian Curvature # TableCol 8 Units mm^-2 # TableCol 9 ColHeader FoldInd # TableCol 9 FieldName Folding Index # TableCol 9 Units unitless # TableCol 10 ColHeader CurvInd # TableCol 10 FieldName Intrinsic Curvature Index # TableCol 10 Units unitless # ColHeaders StructName NumVert SurfArea GrayVol ThickAvg ThickStd MeanCurv GausCurv FoldInd CurvInd bankssts 1706 1193 3135 2.764 0.427 0.112 0.019 15 1.3 caudalanteriorcingulate 1136 745 1942 2.368 0.668 0.158 0.026 24 1.1 caudalmiddlefrontal 3765 2557 6665 2.419 0.421 0.116 0.020 35 3.2 cuneus 2066 1395 2739 1.905 0.546 0.151 0.034 29 3.0 entorhinal 637 497 2185 3.351 0.914 0.119 0.024 5 0.7 fusiform 4365 3016 9776 2.901 0.595 0.135 0.029 65 5.2 inferiorparietal 6556 4462 12218 2.510 0.460 0.122 0.024 81 6.2 inferiortemporal 5440 3693 12466 2.853 0.620 0.126 0.027 75 6.2 isthmuscingulate 1738 1138 2947 2.372 0.814 0.127 0.030 24 2.0 lateraloccipital 7720 5101 12698 2.301 0.458 0.137 0.028 103 8.8 lateralorbitofrontal 3955 2697 7867 2.600 0.577 0.134 0.031 55 5.0 lingual 5496 3835 8069 2.020 0.578 0.144 0.035 77 7.4 medialorbitofrontal 3388 2238 6023 2.430 0.704 0.120 0.032 47 4.0 middletemporal 5073 3489 12496 2.881 0.655 0.130 0.026 75 5.4 parahippocampal 1129 718 2446 2.897 0.776 0.090 0.021 8 0.8 paracentral 2097 1424 3854 2.502 0.497 0.119 0.026 20 2.0 parsopercularis 2432 1679 4984 2.566 0.543 0.115 0.021 28 2.1 parsorbitalis 977 670 2470 2.621 0.598 0.152 0.037 19 1.7 parstriangularis 1970 1406 4121 2.453 0.467 0.134 0.033 30 2.6 pericalcarine 2208 1531 1980 1.593 0.432 0.154 0.037 31 3.3 postcentral 7825 5228 12158 2.086 0.553 0.120 0.022 89 7.0 posteriorcingulate 1949 1342 3783 2.631 0.793 0.152 0.037 35 2.5 precentral 8007 5252 14452 2.557 0.498 0.109 0.021 69 6.6 precuneus 6465 4325 11805 2.509 0.500 0.122 0.026 72 6.6 rostralanteriorcingulate 1421 964 3191 2.943 0.740 0.130 0.032 27 2.0 rostralmiddlefrontal 8606 6003 15441 2.212 0.553 0.140 0.034 140 12.7 superiorfrontal 9981 7035 21086 2.588 0.559 0.133 0.029 118 11.8 superiorparietal 8285 5587 13849 2.271 0.415 0.123 0.023 96 7.4 superiortemporal 6222 4206 13988 2.956 0.616 0.115 0.024 78 6.3 supramarginal 6623 4574 13160 2.579 0.520 0.130 0.028 92 7.8 frontalpole 347 235 1010 2.809 0.788 0.178 0.056 11 0.8 temporalpole 673 484 1882 3.001 0.960 0.165 0.071 18 1.8 transversetemporal 692 435 1114 2.318 0.447 0.096 0.018 5 0.4 insula 3655 2496 7826 3.170 0.729 0.121 0.033 37 4.8
wmparc.statsには、白質の構造情報が記載されている。
# Title Segmentation Statistics # # generating_program mri_segstats # cvs_version $Id: mri_segstats.c,v 1.121 2016/05/31 17:27:11 greve Exp $ # cmdline mri_segstats --seg mri/wmparc.mgz --sum stats/wmparc.stats --pv mri/norm.mgz --excludeid 0 --brainmask mri/brainmask.mgz --in mri/norm.mgz --in-intensity-name norm --in-intensity-units MR --subject Subj001 --surf-wm-vol --ctab /opt/freesurfer/WMParcStatsLUT.txt --etiv # sysname Linux # hostname neuro # machine x86_64 # user neuro # anatomy_type volume # # SUBJECTS_DIR /home/neuro/Documents/Yuya_S/FreeSurfer/2_ANALYZE # subjectname Subj001 # Measure VentricleChoroidVol, VentricleChoroidVol, Volume of ventricles and choroid plexus, 18348.000000, mm^3 # Measure lhCerebralWhiteMatter, lhCerebralWhiteMatterVol, Left hemisphere cerebral white matter volume, 247050.231251, mm^3 # Measure rhCerebralWhiteMatter, rhCerebralWhiteMatterVol, Right hemisphere cerebral white matter volume, 245715.728986, mm^3 # Measure CerebralWhiteMatter, CerebralWhiteMatterVol, Total cerebral white matter volume, 492765.960237, mm^3 # Measure Mask, MaskVol, Mask Volume, 1604897.000000, mm^3 # Measure EstimatedTotalIntraCranialVol, eTIV, Estimated Total Intracranial Volume, 1529098.631452, mm^3 # SegVolFile mri/wmparc.mgz # SegVolFileTimeStamp 2020/12/10 05:19:19 # ColorTable /opt/freesurfer/WMParcStatsLUT.txt # ColorTableTimeStamp 2017/01/19 07:00:02 # InVolFile mri/norm.mgz # InVolFileTimeStamp 2020/12/09 23:13:15 # InVolFrame 0 # PVVolFile mri/norm.mgz # PVVolFileTimeStamp 2020/12/09 23:13:15 # ExcludeSegId 0 # Only reporting non-empty segmentations # VoxelVolume_mm3 1 # TableCol 1 ColHeader Index # TableCol 1 FieldName Index # TableCol 1 Units NA # TableCol 2 ColHeader SegId # TableCol 2 FieldName Segmentation Id # TableCol 2 Units NA # TableCol 3 ColHeader NVoxels # TableCol 3 FieldName Number of Voxels # TableCol 3 Units unitless # TableCol 4 ColHeader Volume_mm3 # TableCol 4 FieldName Volume # TableCol 4 Units mm^3 # TableCol 5 ColHeader StructName # TableCol 5 FieldName Structure Name # TableCol 5 Units NA # TableCol 6 ColHeader normMean # TableCol 6 FieldName Intensity normMean # TableCol 6 Units MR # TableCol 7 ColHeader normStdDev # TableCol 7 FieldName Itensity normStdDev # TableCol 7 Units MR # TableCol 8 ColHeader normMin # TableCol 8 FieldName Intensity normMin # TableCol 8 Units MR # TableCol 9 ColHeader normMax # TableCol 9 FieldName Intensity normMax # TableCol 9 Units MR # TableCol 10 ColHeader normRange # TableCol 10 FieldName Intensity normRange # TableCol 10 Units MR # NRows 70 # NTableCols 10 # ColHeaders Index SegId NVoxels Volume_mm3 StructName normMean normStdDev normMin normMax normRange 1 3001 3524 3508.1 wm-lh-bankssts 98.6405 7.9080 71.0000 118.0000 47.0000 2 3002 3041 3012.8 wm-lh-caudalanteriorcingulate 106.1743 9.7316 71.0000 126.0000 55.0000 3 3003 6943 6929.7 wm-lh-caudalmiddlefrontal 95.6736 9.8735 63.0000 117.0000 54.0000 4 3005 2121 2119.2 wm-lh-cuneus 91.1391 10.4037 66.0000 114.0000 48.0000 5 3006 1049 1088.4 wm-lh-entorhinal 80.1049 8.8624 56.0000 112.0000 56.0000 6 3007 6820 6751.2 wm-lh-fusiform 90.8783 10.4333 56.0000 114.0000 58.0000 7 3008 9829 9877.3 wm-lh-inferiorparietal 96.3825 10.2446 63.0000 120.0000 57.0000 8 3009 7034 7002.1 wm-lh-inferiortemporal 86.2688 12.7662 51.0000 113.0000 62.0000 9 3010 4036 4077.5 wm-lh-isthmuscingulate 105.1016 9.5987 37.0000 124.0000 87.0000 10 3011 9108 9298.5 wm-lh-lateraloccipital 90.5220 9.1699 63.0000 114.0000 51.0000 11 3012 6634 6595.1 wm-lh-lateralorbitofrontal 99.7825 11.2891 66.0000 126.0000 60.0000 12 3013 6526 6406.8 wm-lh-lingual 89.2861 10.1055 51.0000 117.0000 66.0000 13 3014 4776 4756.7 wm-lh-medialorbitofrontal 100.5992 12.2001 24.0000 127.0000 103.0000 14 3015 5454 5569.1 wm-lh-middletemporal 85.9773 9.9489 57.0000 112.0000 55.0000 15 3016 1589 1661.2 wm-lh-parahippocampal 89.3222 8.3148 65.0000 112.0000 47.0000 16 3017 4042 4074.3 wm-lh-paracentral 92.4000 8.4372 66.0000 115.0000 49.0000 17 3018 3752 3724.1 wm-lh-parsopercularis 95.7439 10.4939 68.0000 119.0000 51.0000 18 3019 934 925.5 wm-lh-parsorbitalis 84.3351 10.0562 61.0000 106.0000 45.0000 19 3020 2889 2864.7 wm-lh-parstriangularis 91.3375 11.1110 62.0000 117.0000 55.0000 20 3021 3880 3534.2 wm-lh-pericalcarine 90.3379 10.1190 62.0000 113.0000 51.0000 21 3022 9048 9133.1 wm-lh-postcentral 90.5553 10.1126 63.0000 117.0000 54.0000 22 3023 4979 4930.6 wm-lh-posteriorcingulate 102.9735 9.9418 48.0000 122.0000 74.0000 23 3024 14565 14538.0 wm-lh-precentral 93.4461 9.1777 63.0000 119.0000 56.0000 24 3025 10517 10495.6 wm-lh-precuneus 99.6304 9.6149 36.0000 154.0000 118.0000 25 3026 2602 2514.4 wm-lh-rostralanteriorcingulate 101.8966 14.8008 35.0000 134.0000 99.0000 26 3027 12713 12821.3 wm-lh-rostralmiddlefrontal 98.1206 10.5230 65.0000 120.0000 55.0000 27 3028 17112 17150.4 wm-lh-superiorfrontal 94.3707 10.5009 61.0000 123.0000 62.0000 28 3029 12917 13041.8 wm-lh-superiorparietal 96.7138 10.0283 62.0000 117.0000 55.0000 29 3030 8258 8541.9 wm-lh-superiortemporal 93.0829 10.6918 63.0000 118.0000 55.0000 30 3031 9396 9420.8 wm-lh-supramarginal 96.7631 10.6806 63.0000 121.0000 58.0000 31 3032 214 221.1 wm-lh-frontalpole 92.0561 9.4181 74.0000 116.0000 42.0000 32 3033 624 687.3 wm-lh-temporalpole 79.2388 7.5813 57.0000 110.0000 53.0000 33 3034 642 622.6 wm-lh-transversetemporal 94.6511 8.5727 75.0000 115.0000 40.0000 34 3035 10580 10410.0 wm-lh-insula 96.7245 10.8609 58.0000 124.0000 66.0000 35 4001 2904 2910.9 wm-rh-bankssts 95.7104 7.8693 67.0000 110.0000 43.0000 36 4002 2504 2512.0 wm-rh-caudalanteriorcingulate 105.3910 9.3044 69.0000 123.0000 54.0000 37 4003 7042 6997.0 wm-rh-caudalmiddlefrontal 96.0454 9.8148 65.0000 116.0000 51.0000 38 4005 2412 2543.3 wm-rh-cuneus 88.8429 8.8231 63.0000 113.0000 50.0000 39 4006 692 766.1 wm-rh-entorhinal 78.1604 8.9138 59.0000 113.0000 54.0000 40 4007 7157 7093.4 wm-rh-fusiform 89.5639 9.5493 59.0000 111.0000 52.0000 41 4008 12012 12138.6 wm-rh-inferiorparietal 94.7903 9.8456 65.0000 115.0000 50.0000 42 4009 6736 6716.1 wm-rh-inferiortemporal 88.8744 10.5262 59.0000 110.0000 51.0000 43 4010 3673 3687.3 wm-rh-isthmuscingulate 103.4236 10.2888 26.0000 124.0000 98.0000 44 4011 9750 9938.5 wm-rh-lateraloccipital 89.0627 8.8456 53.0000 109.0000 56.0000 45 4012 6075 6100.6 wm-rh-lateralorbitofrontal 94.8914 10.2953 62.0000 118.0000 56.0000 46 4013 6756 6633.9 wm-rh-lingual 86.4899 10.0740 11.0000 111.0000 100.0000 47 4014 3719 3732.0 wm-rh-medialorbitofrontal 94.9559 11.6434 30.0000 119.0000 89.0000 48 4015 5794 5999.5 wm-rh-middletemporal 88.0036 9.7096 59.0000 110.0000 51.0000 49 4016 1574 1625.5 wm-rh-parahippocampal 88.7154 8.8436 44.0000 112.0000 68.0000 50 4017 4166 4199.9 wm-rh-paracentral 93.8337 8.2716 69.0000 116.0000 47.0000 51 4018 3510 3504.4 wm-rh-parsopercularis 96.5960 10.2199 66.0000 117.0000 51.0000 52 4019 1053 1065.9 wm-rh-parsorbitalis 86.1054 9.5682 62.0000 109.0000 47.0000 53 4020 3448 3427.2 wm-rh-parstriangularis 91.6995 10.0940 62.0000 114.0000 52.0000 54 4021 2888 2670.2 wm-rh-pericalcarine 86.4228 8.9538 26.0000 108.0000 82.0000 55 4022 7978 7981.5 wm-rh-postcentral 90.6651 10.5013 63.0000 116.0000 53.0000 56 4023 4712 4661.9 wm-rh-posteriorcingulate 103.0671 8.9448 49.0000 124.0000 75.0000 57 4024 14926 14925.4 wm-rh-precentral 94.0721 8.5665 67.0000 117.0000 50.0000 58 4025 10712 10698.6 wm-rh-precuneus 99.2039 9.2602 54.0000 121.0000 67.0000 59 4026 1870 1846.9 wm-rh-rostralanteriorcingulate 103.8059 10.5876 70.0000 131.0000 61.0000 60 4027 11835 12171.9 wm-rh-rostralmiddlefrontal 96.1585 9.2493 66.0000 115.0000 49.0000 61 4028 17809 18126.3 wm-rh-superiorfrontal 94.0811 9.3702 63.0000 119.0000 56.0000 62 4029 13213 13320.4 wm-rh-superiorparietal 95.6500 9.6784 63.0000 116.0000 53.0000 63 4030 7743 7980.9 wm-rh-superiortemporal 93.0976 9.4358 65.0000 113.0000 48.0000 64 4031 9885 9909.1 wm-rh-supramarginal 96.7241 10.3698 63.0000 118.0000 55.0000 65 4032 240 265.8 wm-rh-frontalpole 91.2417 7.4440 75.0000 107.0000 32.0000 66 4033 723 784.5 wm-rh-temporalpole 78.9391 8.1127 57.0000 98.0000 41.0000 67 4034 585 577.1 wm-rh-transversetemporal 96.0513 7.4146 73.0000 112.0000 39.0000 68 4035 11695 11518.1 wm-rh-insula 94.4427 11.1743 41.0000 120.0000 79.0000 69 5001 37165 37072.8 Left-UnsegmentedWhiteMatter 103.1510 9.9691 27.0000 127.0000 100.0000 70 5002 35548 35459.7 Right-UnsegmentedWhiteMatter 102.0210 9.6094 23.0000 126.0000 103.0000
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
1. 目的
2. 準備
2.1. DIPYのインストール
2.2. 使用データ
3. 拡散MRIのノイズ除去
3.1. 必要なパッケージをインポート
3.2. 画像およびMPG軸情報の読み込み
3.3. マスク画像の作成
3.4. ギブズのリンギングアーチファクト除去
3.5. NIfTI形式で保存
3.6. 結果
pip3 install dipy
データを次のフォルダ構造で用意する。
Study/
└── Subject
├── DWI.nii.gz # 拡散MRI
├── DWI_mask.nii.gz # 拡散MRIマスク画像
├── bvals # b-values
└── bvecs # b-vectors
Pythonで以下のコマンドを実行。
from dipy.denoise.gibbs import gibbs_removal import matplotlib.pyplot as plt import numpy as np from dipy.segment.mask import median_otsu from dipy.io.image import load_nifti, save_nifti from dipy.io.gradients import read_bvals_bvecs from dipy.core.gradients import gradient_table
DWI_FILE = 'DWI.nii.gz' BVALS_FILE = 'bvals' BVECS_FILE = 'bvecs' # Import data data, affine = load_nifti(DWI_FILE) bvals, bvecs = read_bvals_bvecs(BVALS_FILE, BVECS_FILE) gtab = gradient_table(bvals, bvecs)
median_otsu関数を用いて、b=0画像からマスク画像を生成する。vol_idxには、b0 volumeのvolume indexを渡す。
maskdata, mask = median_otsu(
data, vol_idx=np.where(bvals == 0)[0])
gibbs_removal関数を用いて、リンギングアーチファクトを除去する。
data_corrected = gibbs_removal(maskdata)
save_nifti関数で、画像をNIfTI形式で保存する。
save_nifti('DWI_degibbs.nii.gz', data_corrected.astype(np.float32), affine)
補正前後の画像は、以下の通り。
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
1. 目的
2. コマンド
3. 使用例
3.1. 構造MRI（3D-T1WI）への適用
3.2. 拡散MRIへの適用
ギブズのリンギングアーチファクト（Gibbs ringing）を除去するには、MRtrixのmrdegibbsを用いる。
mrdegibbsのヘルプは、以下の通り。
SYNOPSIS
Remove Gibbs Ringing Artifacts
USAGE
mrdegibbs [ options ] in out
in the input image.
out the output image.
DESCRIPTION
This application attempts to remove Gibbs ringing artefacts from MRI
images using the method of local subvoxel-shifts proposed by Kellner et
al. (see reference below for details).
This command is designed to run on data directly after it has been
reconstructed by the scanner, before any interpolation of any kind has
taken place. You should not run this command after any form of motion
correction (e.g. not after dwifslpreproc). Similarly, if you intend
running dwidenoise, you should run denoising before this command to not
alter the noise structure, which would impact on dwidenoise's performance.
Note that this method is designed to work on images acquired with full
k-space coverage. Running this method on partial Fourier ('half-scan')
data may lead to suboptimal and/or biased results, as noted in the
original reference below. There is currently no means of dealing with
this; users should exercise caution when using this method on partial
Fourier data, and inspect its output for any obvious artefacts.
OPTIONS
-axes list
select the slice axes (default: 0,1 - i.e. x-y).
-nshifts value
discretization of subpixel spacing (default: 20).
-minW value
left border of window used for TV computation (default: 1).
-maxW value
right border of window used for TV computation (default: 3).
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。
mrdegibbs <入力画像> <出力画像> -axes 0,1 # Axial収集 mrdegibbs <入力画像> <出力画像> -axes 0,2 # Coronal収集 mrdegibbs <入力画像> <出力画像> -axes 1,2 # Sagittal収集
3D-T1WI（T1w.nii.gz）を入力としてmrdegibbsを実行する。
mrdegibbs T1w.nii.gz T1w_unringed.nii.gz -axes 0,1
実行した結果は、以下の通り。
mrdegibbsは、拡散MRIにも適用できる。ただし以下の条件がある。
dwidenoiseでノイズを除去した後に実行dwifslpreprocのような歪み・頭の動き補正をする前に実行dwidenoiseでノイズ除去された拡散強調像（DWI_denoised.nii.gz）を入力としてmrdegibbsを実行するには、以下のコマンドを実行する。
mrdegibbs DWI_denoised.nii.gz DWI_denoised_unringed.nii.gz -axes 0,1
実行した結果は、以下の通り。
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