Merge pull request #65 from jhlegarreta/FixCleanupEpisodeTypo

DOC: Fix a typo in image cleanup episode

_episodes/02-Image_Cleanup.md

@@ -15,7 +15,7 @@ keypoints:
 ![course_flow](../fig/episode_2/Course_flow_2.png)
 
 ## 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. 
+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. 
 
@@ -26,7 +26,7 @@ variations in the sensitivity of the reception coil, and the interaction between
 
 - It causes a smooth signal intensity variation within tissue of the same physical properties. 
 
-- The bias field is dependent on the strength of the magnetic field. If it is not corrected for 1.5T or higher MR scanners, it can considerably affect downstream analyses. Stronger magnets will induce higher bias. 
+- The bias field is dependent on the strength of the magnetic field. If it is not corrected for 1.5T or higher MR scanners, it can considerably affect downstream analyses. Stronger magnets will induce higher bias.
 
 - Commonly used tools
     - [ANTs N4 bias correction](https://pubmed.ncbi.nlm.nih.gov/20378467/) (See figure below)
@@ -39,7 +39,7 @@ variations in the sensitivity of the reception coil, and the interaction between
 >
 > > ## 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.  
 > > 
 > {: .solution}
@@ -50,7 +50,7 @@ variations in the sensitivity of the reception coil, and the interaction between
 (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](../fig/episode_2/N4_bias.jpeg)
 
-#### Side-note: [ANTs](http://stnava.github.io/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](https://nipype.readthedocs.io/en/latest/) library. 
+#### Side-note: [ANTs](http://stnava.github.io/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](https://nipype.readthedocs.io/en/latest/) library.
 
 ~~~
 from nipype.interfaces.ants import N4BiasFieldCorrection
@@ -121,7 +121,7 @@ the cerebral cortex and subcortical structures, including the brain stem and cer
 - Note 1: At this point we are NOT trying to extract the brain sulci and gyri (i.e. cortical folds). We are just creating a simple brain mask for computational purposes, which need not capture the precise brain anatomy. Thus you may see some marrow and membrain included in the extracted brain. 
 - Note 2: Brainstem and spinal cord are continuous so a rather arbitrarily cut-off point is selected. 
 
-### Example brain extractions pass / fail 
+### Example brain extractions pass / fail
 
 |        Pass     | Fail          |
 | :-------------: | :-----------: |
@@ -136,14 +136,14 @@ _Source: FSL Introduction to Brain Extraction_
 > 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. 
+> > 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.
 > > 
 > > 
 > {: .solution}
 {: .challenge}
 
 
-#### Side-note: [ANTs](http://stnava.github.io/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](https://nipype.readthedocs.io/en/latest/) library. 
+#### Side-note: [ANTs](http://stnava.github.io/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](https://nipype.readthedocs.io/en/latest/) library.
 
 ~~~
 from nipype.interfaces.ants.segmentation import BrainExtraction