This lesson is still being designed and assembled (Pre-Alpha version)

sMRI Clean-up

Overview

Teaching: 30 min
Exercises: 15 min
Questions
  • What are the sources of noise and artifacts in MR images?

  • How do we extract/mask the brain?

Objectives
  • Visualize bias fields and motion artifacts

  • Generate brain masks

You Are Here!

course_flow

Why do we need image clean-ups?

Correcting or cleaning-up certain artifacts from the raw (i.e. acquired) MR scans is crucial for the successful processing of subsequent image normalization tasks as well as the downstream statistical analyses. Some version (i.e. custom algorithm) of these two tasks is implemented in all commonly deployed processing pipelines such as FreeSurfer, FSL etc.

In this episode we will look at two common image clean-up tasks 1) Intensity normalization 2) Brain extraction.

Intensity normalization (a.k.a bias field correction; a.k.a. intensity inhomogeneity correction)

Bias field correction quiz

What is the difference between bias field and image noise?

Solution

Bias field is modeled as multiplicative factor, whereas noise is typically assumed as additive and spatially independent (Gaussian) factor.

i.e. v(x) = u(x)f(x) + n(x), where v is the given image, u is the uncorrupted image, f is the bias field, and n is the noise.

ANTs N4 correction

(a) Acquired T1w image (b) Estimated the bias field which can then be used to “correct” the image. (c) Bias field viewed as a surface to show the low frequency modulation. N4_bias

Side-note: ANTs is a software comprising several tools and image processing algorithms. ANTs can be run independently or we can import ANTs scripts in python using nipype library.

from nipype.interfaces.ants import N4BiasFieldCorrection

n4 = N4BiasFieldCorrection()
n4.inputs.dimension = 3
n4.inputs.input_image = 'structural.nii'
n4.inputs.bspline_fitting_distance = 300
n4.inputs.shrink_factor = 3
n4.inputs.n_iterations = [50,50,30,20]
n4.cmdline 

'N4BiasFieldCorrection --bspline-fitting [ 300 ] -d 3 --input-image structural.nii --convergence [ 50x50x30x20 ] --output structural_corrected.nii --shrink-factor 3'

Impact of correction (source: Despotović et al.)

The top figure panel shows original and bias field corrected MR image slices. The middle figure panel shows the difference in the intensty histograms for the two image slices. And the bottom figure panel shows the impact of bias correction on a subsequent image segmentation task.

bias_correction

Visualizing “before” and “after” (see ../code/2_sMRI_image_cleanup.ipynb for detailed example.)

import nibabel as nib
from nilearn import plotting

import nibabel as nib from nilearn import plotting

T1_orig = subject_dir + 'orig.mgz'
T1_corrected = subject_dir + 'nu.mgz'
T1_img_orig = nib.load(T1_orig)
T1_img_corrected = nib.load(T1_corrected)

# plot
cut_coords = (-85,-2,-5)
plotting.plot_anat(T1_orig, title="T1_orig", cut_coords=cut_coords, vmax=255)
plotting.plot_anat(T1_corrected, title="T1_corrected_img", cut_coords=cut_coords, vmax=255)
Before After
nilearn_bias_orig nilearn_bias_corr

Brain extraction (a.k.a skull-stripping)

Example brain extractions pass / fail

Pass Fail
Drawing Drawing

Source: FSL Introduction to Brain Extraction

Brain extraction quiz

Can we use this brain-mask as an estimate for brain volume?

Solution

Brain mask at this stage only offers a crude estimate about total brain volume. It can be used for quality control (e.g. detecting preprocessing algorithm failures). More accurate estimates of total brain and intracranial volumes are calculated in subsequent steps, which are used as covariates or normalizing factors in statistical analysis.

Side-note: ANTs is a software comprising several tools and image processing algorithms. ANTs can be run independently or we can import ANTs scripts in python using nipype library.

from nipype.interfaces.ants.segmentation import BrainExtraction
brainextraction = BrainExtraction()
brainextraction.inputs.dimension = 3
brainextraction.inputs.anatomical_image ='T1.nii.gz'
brainextraction.inputs.brain_template = 'study_template.nii.gz'
brainextraction.inputs.brain_probability_mask ='ProbabilityMaskOfStudyTemplate.nii.gz'
brainextraction.cmdline
'antsBrainExtraction.sh -a T1.nii.gz -m ProbabilityMaskOfStudyTemplate.nii.gz
-e study_template.nii.gz -d 3 -s nii.gz -o highres001_'

ANTs Brain Extraction

ANTs_brain_extract

FSL BET

FSL_brain_extract

Visualizing “before” and “after” (see ../code/2_sMRI_image_cleanup.ipynb for detailed example.)

from nipype.interfaces.ants.segmentation import BrainExtraction

import nibabel as nib from nilearn import plotting

T1_normalized = subject_dir + 'T1.mgz'
T1_brain_extract = subject_dir + 'brainmask.mgz'
T1_img_normalized = nib.load(T1_normalized)
T1_img_brain_extract = nib.load(T1_brain_extract)

# plot
cut_coords = (-85,-2,-5)
plotting.plot_anat(T1_img_normalized, title="T1_img_normalized", cut_coords=cut_coords, vmax=255)
plotting.plot_anat(T1_img_brain_extract, title="T1_img_brain_extract", cut_coords=cut_coords, vmax=255)

Before After
nilearn_brain_orig nilearn_brain_extract

Key Points

  • Presence of artifacts can lead to flawed analysis and incorrect findings