knitr::opts_chunk$set(comment = "")

Resources and Goals

Much of this work has been adapted by the FSL guide for DTI reconstruction: We will show you a few steps that have been implemented in rcamino: camino_pointset2scheme, camino_modelfit, camino_fa, camino_md, and camino_dteig.

Data Location

The data located in this tutorial is located at It contains 3 files:

  1. 4Ddwi_b1000.nii.gz - a 4D image of the DWI data.
  2. brain_mask.nii.gz - A brain mask of the DTI data
  3. grad_dirs.txt - a 3 column text file with the b-vectors as the first 3 columns

Reading in the Data

First, we download the data into a temporary directory the unzip it:

tdir = tempdir()
tfile = file.path(tdir, "")
              destfile = tfile)
files = unzip(zipfile = tfile, exdir = tdir, overwrite = TRUE)

Making b-vectors and b-values

As 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.

b_data_file = grep("[.]txt$", files, value = TRUE)
scheme_file = camino_pointset2scheme(infile = b_data_file,
                                bvalue = 1e9)

Checking our data

Here we ensure that the number of b-values/b-vectors is the same as the number of time points in the 4D image.

img_fname = grep("4Ddwi_b1000", files, value = TRUE)
img = readnii(img_fname)
grads = readLines(b_data_file)
# cleanup
rm(list= "img"); gc()

Running Image Conversion

We will save the result in a temporary file (outfile), but also return the result as a nifti object ret, as 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 nifti object.

Fit the diffusion tensor

mask_fname = grep("mask", files, value = TRUE)
model_fname = camino_modelfit(
  infile = float_fname,
  scheme = scheme_file,
  mask = mask_fname,
  outputdatatype = "double"

Getting FA vlaues

fa_fname = camino_fa(infile = model_fname)

Converting FA values back into an image

fa_img_name = camino_voxel2image(infile = fa_fname, 
                            header = img_fname, 
                            gzip = TRUE, 
                            components = 1)
fa_img = readnii(fa_img_name)

Converting with piping

We can chain Camino commands using the magrittr pipe operation (%>%):

fa_img2 = model_fname %>% 
  camino_fa() %>% 
  camino_voxel2image(header = img_fname, gzip = TRUE, components = 1) %>% 
all.equal(fa_img, fa_img2)

Visualizing FA images

Using ortho2, we can visualize these FA maps:


Getting MD vlaues

Similar to getting FA maps, we can get mean diffusivity (MD) maps, read them into R, and visualize them using ortho2:

md_img = model_fname %>% 
  camino_md() %>% 
  camino_voxel2image(header = img_fname, gzip = TRUE, components = 1) %>% 

Export DTs to NIfTI

Using 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

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)

neuroconductor-devel-releases/rcamino documentation built on Feb. 14, 2020, 1:33 p.m.