Align and Bias Correct Your Brain!

Once you’re files are ready to by preprocessed (Yes, there’s a word for the processing that takes place before the processing). They need to be aligned to a common image space.
Here’s my handy script:
#!/bin/bash

#SBATCH --time=30:00
#SBATCH --ntasks=1
#SBATCH --nodes=1
#SBATCH --mem-per-cpu=8192M
#SBATCH -o /fslhome/zach8769/logfiles/oasis_longitudinal/output_prepT1_0.txt
#SBATCH -e /fslhome/zach8769/logfiles/oasis_longitudinal/error_prepT1_0.txt
#SBATCH -J "prepT1_0"
#SBATCH --mail-user=GalileoHumpkins@gmail.com
#SBATCH --mail-type=BEGIN
#SBATCH --mail-type=END
#SBATCH --mail-type=FAIL

export PBS_NODEFILE=`/fslapps/fslutils/generate_pbs_nodefile`
export PBS_JOBID=$SLURM_JOB_ID
export PBS_O_WORKDIR="$SLURM_SUBMIT_DIR"
export PBS_QUEUE=batch
export OMP_NUM_THREADS=$SLURM_CPUS_ON_NODE

ROOT=/fslhome/GalileoHumpkins/
DATASET=${ROOT}compute/oasis_longitudinal/
TEMPLATE=${ROOT}compute/templates/MICCAI2012-Multi-Atlas-Challenge-Data/
export ANTSPATH=/fslhome/zach8769/bin/antsbin/bin/
PATH=${ANTSPATH}:${PATH}

IFS=$'\n'
array=( $(find ${DATASET}/*/t1/*/ -type f -name t1.nii.gz) )
for i in 0;
  do
  SUB=$(dirname ${array[$i]})

  ${ANTSPATH}antsRegistrationSyNQuick.sh -d 3 \
    -f ${TEMPLATE}T_template0.nii.gz \
    -m ${SUB}/t1.nii.gz \
    -o ${SUB}/rigid_ \
    -t r

  N4BiasFieldCorrection -d 3 \
    -i ${SUB}/rigid_Warped.nii.gz \
    -o ${SUB}/n4.nii.gz \
    -s 8 -b [200] -c [50x50x50x50,0.000001]
    
  N4BiasFieldCorrection -d 3 \
    -i ${SUB}/n4.nii.gz \
    -o ${SUB}/n4.nii.gz \
    -s 4 -b [200] -c [50x50x50x50,0.000001]

  N4BiasFieldCorrection -d 3 \
    -i ${SUB}/n4.nii.gz \
    -o ${SUB}/n4.nii.gz \
    -s 2 -b [200] -c [50x50x50x50,0.000001]

  ResampleImage 3 \
    ${SUB}/n4.nii.gz \
    ${SUB}/n4_resliced.nii.gz 1x1x1

  rm ${SUB}/rigid_0GenericAffine.mat
  rm ${SUB}/rigid_InverseWarped.nii.gz
done
Th N4BiasFieldCorrection command accounts for different intensities in the scan that result from bias in the signal as a result of their location in 3 dimensions (there are a lot more details but they’re beyond the scope of this post). The ResampleImage command ensures that all images have the same voxel spacing. As a whole, this script ensures that image spacing and intensity is as comparable as possible before any further processes that compare anatomical differences are used.

After
Before

Comments

Post a Comment

Popular posts from this blog

Image Manipulation and Statistics

The following represents the current extent to which the iClass package I’ve been developing has been tested. The goal is to impliment it in ANTsR eventually when I’m not in the midst of medical school classes. For now this can be used if ANTsR is already installed and the following code is run to download the package from github. ## install.packages("devtools") devtools::install_github("Tokazama/iClass") library(ANTsR) library(h5) home <- '/Volumes/SANDISK/datasets/ucsd/' ucsd <- read.csv(paste(home, 'spreadsheets/ucsdWOna.csv', sep = ""))[, -1] iGroup The first thing I want to do is create an object that represents my image information in a convenient way. I can do this using the iGroup class. The following demonstrates how I do so with whole brain morphometry images. wblist <- c() boolwb <- rep(FALSE, nrow(ucsd)) for (i in 1:nrow(ucsd)) { tmppath <- pas...
Read more

Making a Brain Template

Image
My last post was on how to obtain a representative sample to create a brain template. This post shows how to create the actual template. This step is fairly straightforward because most of the heavy lifting is already performed by antsMultivariateTemplateContstruction.sh . Here’s the entirety of the script so that you have an idea of how it all looks together. #!/bin/bash #SBATCH --time=75:00:00 #SBATCH --ntasks=1 #SBATCH --nodes=1 #SBATCH --mem-per-cpu=32768M #SBATCH -o /fslhome/username/logfiles/dataset/output_temp.txt #SBATCH -e /fslhome/username/logfiles/dataset/error_temp.txt #SBATCH -J "ucsdtemp" #SBATCH --mail-user=LongbranchPennywhistle@gmail.com #SBATCH --mail-type=BEGIN #SBATCH --mail-type=END #SBATCH --mail-type=FAIL export PBS_NODEFILE=`/fslapps/fslutils/generate_pbs_nodefile` export PBS_JOBID=$SLURM_JOB_ID export PBS_O_WORKDIR="$SLURM_SUBMIT_DIR" export PBS_QUEUE=batch export OMP_NUM_THREADS...
Read more

Visualizing Cortical Thickness In a Population

Image
Today my goal is to develop some methods for comparing an individual's neuroanatomy to a population's and make some graphs that help convey my findings. To demonstrate this I will be using the Pediatric Template of Brain Perfusion (PTBP), which has already been processed and can be found here . I'll start off by loading some libraries I'll need and the CSV file. The CSV file contain demographics data and anatomically specific information about the cortical thickness, fractional anistrophy, cerebral blood flow, and blood oxygenation level. For now I'm just going to focus on cortical thickness (found by the prefix “Thick*”). Picture of amygdala from recent paper on the effects of cannabis suppressPackageStartupMessages(library(dplyr)) suppressPackageStartupMessages(library(ggplot2)) home &lt;- '/Users/zach8769/' ptbp &lt;- read.csv(paste(home, 'Desktop/ptbp/data/ptbp_summary_demographics.csv', sep = ""), header = TRUE) thick_pt...
Read more