dcm2niix -r y /path/to/DICOMS – this will simply rename rather than convert the data. It sorts each session into a separate folder, which makes subsequent conversion much easier.
FSL 6.0.5 ships eddy_cuda10.2 which literally uses CUDA 10.2.
I explored a way to use eddy_cuda10.2, PyTorch and Tensorflow concurrently. Below is How-To for Ubuntu 18.04/20.04.
b0 to T1WIとT1WI to 標準脳のWarp Fieldを使って、拡散定量値マップを標準空間に移動
標準空間上にあるすべての被験者の拡散定量値マップをひとつの4D volume dataにまとめる
平均灰白質マスクmean_GM_maskで、3の拡散定量値マップをマスク処理
function gbss_2_DWIwpreproc() {
echo "Diffusion MAP preprocessing..."
# Diffusion MAP preprocessing
for SUBJ in ${SUBJ_LIST}; do
## Register b0 to MNI
### Register b0 to T1WI
epi_reg --epi=b0/${SUBJ} \
--t1=T1w/${SUBJ} --t1brain=T1w/${SUBJ}_skull_stripped \
--echospacing=${ECHOSPACING} --pedir=${PEDIR} \
--wmseg=T1w//${SUBJ}_fast_seg_2.nii.gz \
--out=b0/${SUBJ}_BBR
for MAP in ${MAP_LIST}; do
### applywarp to diffusion map and move it to T1w based on epi_reg
flirt -in ${MAP}/${SUBJ} \
-out ${MAP}/${SUBJ}_inT1 \
-ref T1w/${SUBJ}_skull_stripped \
-init b0/${SUBJ}_BBR.mat -applyxfm
### applywarp to dMAP in T1w and move it to MNI
applywarp --ref=${FSLDIR}/data/standard/MNI152_T1_2mm_brain \
--in=${MAP}/${SUBJ}_inT1 \
--warp=T1w/${SUBJ}_indiv2MNI_warp \
--out=${MAP}/${SUBJ}_inMNI
done
done
## 4D all_${dMAP}
for MAP in ${MAP_LIST}; do
### Merge dMAP in MNI into one volume, all_${MAP}
fslmerge -t stats/all_${MAP} $(imglob ${MAP}/*_inMNI.nii.gz)
### Masking dMAP in MNI using mean GM mask
fslmaths stats/all_${MAP} -mas stats/mean_GM_mask stats/all_${MAP}
done
}
import numpy as np
import matplotlib.pyplot as plt
import dipy.reconst.dki as dki
from dipy.core.gradients import gradient_table
from dipy.io.gradients import read_bvals_bvecs
from dipy.io.image import load_nifti, save_nifti
from dipy.segment.mask import median_otsu
from dipy.segment.mask import median_otsu
import numpy as np
from dipy.io.image import load_nifti, save_nifti
from dipy.io.gradients import read_bvals_bvecs
from dipy.core.gradients import gradient_table
import dipy.reconst.dti as dti
SYNOPSIS
Diffusion (kurtosis) tensor estimation
USAGE
dwi2tensor [ options ] dwi dt
dwi the input dwi image.
dt the output dt image.
DESCRIPTION
By default, the diffusion tensor (and optionally its kurtosis) is fitted
to the log-signal in two steps: firstly, using weighted least-squares
(WLS) with weights based on the empirical signal intensities; secondly, by
further iterated weighted least-squares (IWLS) with weights determined by
the signal predictions from the previous iteration (by default, 2
iterations will be performed). This behaviour can be altered in two ways:
* The -ols option will cause the first fitting step to be performed using
ordinary least-squares (OLS); that is, all measurements contribute equally
to the fit, instead of the default behaviour of weighting based on the
empirical signal intensities.
* The -iter option controls the number of iterations of the IWLS
prodedure. If this is set to zero, then the output model parameters will
be those resulting from the first fitting step only: either WLS by
default, or OLS if the -ols option is used in conjunction with -iter 0.
The tensor coefficients are stored in the output image as follows:
volumes 0-5: D11, D22, D33, D12, D13, D23
If diffusion kurtosis is estimated using the -dkt option, these are stored
as follows:
volumes 0-2: W1111, W2222, W3333
volumes 3-8: W1112, W1113, W1222, W1333, W2223, W2333
volumes 9-11: W1122, W1133, W2233
volumes 12-14: W1123, W1223, W1233
OPTIONS
-ols
perform initial fit using an ordinary least-squares (OLS) fit (see
Description).
-mask image
only perform computation within the specified binary brain mask image.
-b0 image
the output b0 image.
-dkt image
the output dkt image.
-iter integer
number of iterative reweightings for IWLS algorithm (default: 2) (see
Description).
-predicted_signal image
the predicted dwi image.
DW gradient table import options
-grad file
Provide the diffusion-weighted gradient scheme used in the acquisition in
a text file. This should be supplied as a 4xN text file with each line is
in the format [ X Y Z b ], where [ X Y Z ] describe the direction of the
applied gradient, and b gives the b-value in units of s/mm^2. If a
diffusion gradient scheme is present in the input image header, the data
provided with this option will be instead used.
-fslgrad bvecs bvals
Provide the diffusion-weighted gradient scheme used in the acquisition in
FSL bvecs/bvals format files. If a diffusion gradient scheme is present in
the input image header, the data provided with this option will be instead
used.
Standard options
-info
display information messages.
-quiet
do not display information messages or progress status; alternatively,
this can be achieved by setting the MRTRIX_QUIET environment variable to a
non-empty string.
-debug
display debugging messages.
-force
force overwrite of output files (caution: using the same file as input and
output might cause unexpected behaviour).
-nthreads number
use this number of threads in multi-threaded applications (set to 0 to
disable multi-threading).
-config key value (multiple uses permitted)
temporarily set the value of an MRtrix config file entry.
-help
display this information page and exit.
-version
display version information and exit.
tensor2metricのヘルプは、次の通り。
クリックして展開
SYNOPSIS
Generate maps of tensor-derived parameters
USAGE
tensor2metric [ options ] tensor
tensor the input tensor image.
OPTIONS
-adc image
compute the mean apparent diffusion coefficient (ADC) of the diffusion
tensor. (sometimes also referred to as the mean diffusivity (MD))
-fa image
compute the fractional anisotropy (FA) of the diffusion tensor.
-ad image
compute the axial diffusivity (AD) of the diffusion tensor. (equivalent to
the principal eigenvalue)
-rd image
compute the radial diffusivity (RD) of the diffusion tensor. (equivalent
to the mean of the two non-principal eigenvalues)
-cl image
compute the linearity metric of the diffusion tensor. (one of the three
Westin shape metrics)
-cp image
compute the planarity metric of the diffusion tensor. (one of the three
Westin shape metrics)
-cs image
compute the sphericity metric of the diffusion tensor. (one of the three
Westin shape metrics)
-value image
compute the selected eigenvalue(s) of the diffusion tensor.
-vector image
compute the selected eigenvector(s) of the diffusion tensor.
-num sequence
specify the desired eigenvalue/eigenvector(s). Note that several
eigenvalues can be specified as a number sequence. For example, '1,3'
specifies the principal (1) and minor (3) eigenvalues/eigenvectors
(default = 1).
-modulate choice
specify how to modulate the magnitude of the eigenvectors. Valid choices
are: none, FA, eigval (default = FA).
-mask image
only perform computation within the specified binary brain mask image.
Standard options
-info
display information messages.
-quiet
do not display information messages or progress status; alternatively,
this can be achieved by setting the MRTRIX_QUIET environment variable to a
non-empty string.
-debug
display debugging messages.
-force
force overwrite of output files (caution: using the same file as input and
output might cause unexpected behaviour).
-nthreads number
use this number of threads in multi-threaded applications (set to 0 to
disable multi-threading).
-config key value (multiple uses permitted)
temporarily set the value of an MRtrix config file entry.
-help
display this information page and exit.
-version
display version information and exit.
