Resources and Goals

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 rcamino: camino_pointset2scheme, camino_modelfit, camino_fa, camino_md, and camino_dteig.

Data Location

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:

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, "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)

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

/tmp/Rtmpu8nUfi/Rinst64164244e0c8/rcamino/camino/bin/pointset2scheme -inputfile '/tmp/Rtmp3VqWMB/grad_dirs.txt' -bvalue 1000000000 -outputfile /tmp/Rtmp3VqWMB/file66153ff883d9.scheme

library(neurobase)

Loading required package: oro.nifti

oro.nifti 0.9.7

img_fname = grep("4Ddwi_b1000", files, value = TRUE)
img = readnii(img_fname)
ntim(img)

[1] 33

grads = readLines(b_data_file)
length(grads)

[1] 33

# cleanup
rm(list= "img"); gc()

         used (Mb) gc trigger  (Mb)  max used   (Mb)
Ncells 520671 27.9     940480  50.3    750400   40.1
Vcells 693962  5.3  115850709 883.9 143603106 1095.7

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")

/tmp/Rtmpu8nUfi/Rinst64164244e0c8/rcamino/camino/bin/image2voxel -inputfile '/tmp/Rtmp3VqWMB/4Ddwi_b1000.nii.gz' -outputfile '/tmp/Rtmp3VqWMB/file6615c832a64.Bfloat' -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"
  )

/tmp/Rtmpu8nUfi/Rinst64164244e0c8/rcamino/camino/bin/modelfit -inputfile '/tmp/Rtmp3VqWMB/file6615c832a64.Bfloat' -outputfile '/tmp/Rtmp3VqWMB/file66152c611ee5.Bdouble' -inputdatatype float -schemefile /tmp/Rtmp3VqWMB/file66153ff883d9.scheme -bgmask /tmp/Rtmp3VqWMB/brain_mask.nii.gz -maskdatatype float -model dt

fa_fname = camino_fa(infile = model_fname)

cat '/tmp/Rtmp3VqWMB/file66152c611ee5.Bdouble' |  /tmp/Rtmpu8nUfi/Rinst64164244e0c8/rcamino/camino/bin/fa -inputmodel dt -outputdatatype double > '/tmp/Rtmp3VqWMB/file661556b497f2.Bdouble'

library(neurobase)
fa_img_name = camino_voxel2image(infile = fa_fname, 
                            header = img_fname, 
                            gzip = TRUE, 
                            components = 1)

/tmp/Rtmpu8nUfi/Rinst64164244e0c8/rcamino/camino/bin/voxel2image -inputfile /tmp/Rtmp3VqWMB/file661556b497f2.Bdouble -header /tmp/Rtmp3VqWMB/4Ddwi_b1000.nii.gz -outputroot /tmp/Rtmp3VqWMB/file6615477b856a_ -components 1 -gzip 

fa_img = readnii(fa_img_name)

library(magrittr)
fa_img2 = model_fname %>% 
  camino_fa() %>% 
  camino_voxel2image(header = img_fname, gzip = TRUE, components = 1) %>% 
  readnii

cat '/tmp/Rtmp3VqWMB/file66152c611ee5.Bdouble' |  /tmp/Rtmpu8nUfi/Rinst64164244e0c8/rcamino/camino/bin/fa -inputmodel dt -outputdatatype double > '/tmp/Rtmp3VqWMB/file6615561f9e47.Bdouble'

/tmp/Rtmpu8nUfi/Rinst64164244e0c8/rcamino/camino/bin/voxel2image -inputfile /tmp/Rtmp3VqWMB/file6615561f9e47.Bdouble -header /tmp/Rtmp3VqWMB/4Ddwi_b1000.nii.gz -outputroot /tmp/Rtmp3VqWMB/file66152a8795a9_ -components 1 -gzip 

all.equal(fa_img, fa_img2)

[1] TRUE

ortho2(fa_img)

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

cat '/tmp/Rtmp3VqWMB/file66152c611ee5.Bdouble' |  /tmp/Rtmpu8nUfi/Rinst64164244e0c8/rcamino/camino/bin/md -inputmodel dt -outputdatatype double > '/tmp/Rtmp3VqWMB/file66156da31a3d.Bdouble'

/tmp/Rtmpu8nUfi/Rinst64164244e0c8/rcamino/camino/bin/voxel2image -inputfile /tmp/Rtmp3VqWMB/file66156da31a3d.Bdouble -header /tmp/Rtmp3VqWMB/4Ddwi_b1000.nii.gz -outputroot /tmp/Rtmp3VqWMB/file66153fab13bc_ -components 1 -gzip 

ortho2(md_img)

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
)

/tmp/Rtmpu8nUfi/Rinst64164244e0c8/rcamino/camino/bin/dt2nii -inputfile /tmp/Rtmp3VqWMB/file66152c611ee5.Bdouble -header /tmp/Rtmp3VqWMB/4Ddwi_b1000.nii.gz -inputmodel dt -outputroot /tmp/Rtmp3VqWMB/file66156ade22df_ -gzip 

stopifnot(all(file.exists(nifti_dt)))
print(nifti_dt)

[1] "/tmp/Rtmp3VqWMB/file66156ade22df_exitcode.nii.gz"
[2] "/tmp/Rtmp3VqWMB/file66156ade22df_lns0.nii.gz"    
[3] "/tmp/Rtmp3VqWMB/file66156ade22df_dt.nii.gz"      

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)