library(knitr) knitr::opts_chunk$set(comment = "")
Much of this work has been adapted by the FSL guide for DTI reconstruction: http://camino.cs.ucl.ac.uk/index.php?n=Tutorials.DTI. We will show you a few steps that have been implemented in
The data located in this tutorial is located at http://cmic.cs.ucl.ac.uk/camino//uploads/Tutorials/example_dwi.zip. It contains 3 files:
4Ddwi_b1000.nii.gz- a 4D image of the DWI data.
brain_mask.nii.gz- A brain mask of the DTI data
grad_dirs.txt- a 3 column text file with the b-vectors as the first 3 columns
First, we download the data into a temporary directory the unzip it:
tdir = tempdir() tfile = file.path(tdir, "example_dwi.zip") download.file("http://cmic.cs.ucl.ac.uk/camino//uploads/Tutorials/example_dwi.zip", destfile = tfile) files = unzip(zipfile = tfile, exdir = tdir, overwrite = TRUE)
dtifit requires the b-values and b-vectors to be separated, and this data has b-values of $1000$ when the b-vectors is not zero. This is very important and you must know where your b-values and b-vectors are when doing your analyses and what units they are in.
library(rcamino) b_data_file = grep("[.]txt$", files, value = TRUE) scheme_file = camino_pointset2scheme(infile = b_data_file, bvalue = 1e9)
Here we ensure that the number of b-values/b-vectors is the same as the number of time points in the 4D image.
library(neurobase) img_fname = grep("4Ddwi_b1000", files, value = TRUE) img = readnii(img_fname) ntim(img) grads = readLines(b_data_file) length(grads) # cleanup rm(list= "img"); gc()
We will save the result in a temporary file (
outfile), but also return the result as a
retimg = TRUE. We will use the first volume as the reference as is the default in FSL. Note FSL is zero-indexed so the first volume is the zero-ith index:
float_fname = camino_image2voxel(infile = img_fname, outputdatatype = "float")
Note, from here on forward we will use either the filename for the output of the eddy current correction or the eddy-current-corrected
mask_fname = grep("mask", files, value = TRUE) model_fname = camino_modelfit( infile = float_fname, scheme = scheme_file, mask = mask_fname, outputdatatype = "double" )
fa_fname = camino_fa(infile = model_fname)
library(neurobase) fa_img_name = camino_voxel2image(infile = fa_fname, header = img_fname, gzip = TRUE, components = 1) fa_img = readnii(fa_img_name)
We can chain Camino commands using the
magrittr pipe operation (
library(magrittr) fa_img2 = model_fname %>% camino_fa() %>% camino_voxel2image(header = img_fname, gzip = TRUE, components = 1) %>% readnii all.equal(fa_img, fa_img2)
ortho2, we can visualize these FA maps:
Similar to getting FA maps, we can get mean diffusivity (MD) maps, read them into
R, and visualize them using
md_img = model_fname %>% camino_md() %>% camino_voxel2image(header = img_fname, gzip = TRUE, components = 1) %>% readnii ortho2(md_img)
camino_dt2nii, we can export the diffusion tensors into NIfTI files. We see the result is the filenames of the NIfTI files, and that they all exist (otherwise there'd be an errors.)
nifti_dt = camino_dt2nii( infile = model_fname, inputmodel = "dt", header = img_fname, gzip = TRUE ) stopifnot(all(file.exists(nifti_dt))) print(nifti_dt)
We can read these DT images into
R again using
readnii, but we must set
drop_dim = FALSE for diffusion tensor images because the pixel dimensions are zero and
readnii assumes you want to drop "empty" dimensions
dt_imgs = lapply(nifti_dt, readnii, drop_dim = FALSE)
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