Thomas Dagonneau feedback changes - intro and T1 chapter (#17)

* TD feedback - index

* TD feedback 01-intro

* TD feedback 02-million-dollar

* TD feedback,03 journey

* TD feedback 04-encode

* TD feedback 05-sequences

* TD feedback 06-qmri

* TD feedback - IR intro

* TD feedback - IR signal mod

* TD feedback - IR data fit

* TD feedback - benefitspitfalls

* Fix missing italics

* TD feedback -VFA signal modelling

* TD feedback VFA datafit

* TD feedback MP2RAGE intro

* TD feedback mp2rage signal

* TD feedback: Mp2rage datafitting

1 Introduction to qMRI/01-intro.md

@@ -7,13 +7,13 @@ authors:
       - NeuroPoly Lab, Polytechnique Montreal, Quebec, Canada
 ---
 
-This section starts by explaining the distinction between MRI and quantitative MRI (qMRI), which is of essence to the central premise of this MOOC. 
+This section starts by explaining the distinction between MRI and quantitative MRI (qMRI), which is of essence to the central premise of this mOOC. 
 
 Next, it aims at delivering an intuitive understanding of how MRI works by using cartoons, simulations and example applications, all introduced in the context of overarching concepts from physics and everyday life. 
 
 ::: {admonition} See also
 :class: seealso
-For a more theoretical introductory explanation, the reader is referred to [Nishimura](https://en.wikipedia.org/wiki/Dwight_Nishimura) [-@Nishimura:1996uc].
+For a more theoretical introductory explanation, the reader is referred to [@Nishimura:1996uc].
 :::
 
 After covering the basics of MRI, the relationship between data acquisition and parameter estimation will be explained based on two basic qMRI applications: _T_{sub}`1` and _T_{sub}`2` mapping. 

1 Introduction to qMRI/02-million_dollar.md

@@ -23,7 +23,7 @@ We will start answering this question by looking at the most prominent use cases
 In the clinics, MRI stands out as one of the most preferred imaging methods, because it can generate detailed images with superb soft tissue contrast, without using ionizing radiation or cutting open the human body. Surprisingly, MRI scanners have also been extensively used in food science to study soft tissue. For example, several studies used MRI to observe how moisture migrates towards the center of jellybeans over time [@Troutman:2001vk; @Ziegler:2003th]
 
 Be it in diagnostic radiology, or in food science, it is the superior soft tissue contrast that makes MRI appealing. In routine diagnostic readings, the radiologists browse through MR images to capture abnormalities that may be resolved by conventional MRI contrasts, i.e. `T1-` or `T2-weighted` images. As a result, the detection of pathological patterns depends on a radiologists’ visual assessment, which is then transferred to a written report – _a narration of observations_ – such as:
- > _T_{sub}`2` hyperintense appearance in the left parieto-occipital lobe sug- gests hemorrhagic infarction [Fig. %sf](#intFig1). 
+ > _T_{sub}`2` hyperintense appearance in the left parieto-occipital lobe suggests hemorrhagic infarction [Fig. %sf](#intFig1). 
  
  Here, the word `hyperintense` implies a relative comparison. [Fig. %se](#intFig1) illustrates that cropping the tumorous region away from the image removes the basis of comparison and makes the hyperintense appearance irrelevant. This is because the pixel brightness of conventional MR images is assigned using an arbitrary scale consisting of shades of gray. Due to the lack of a calibrated measurement scale, conventional MRI is considered to be qualitative.
 
@@ -37,7 +37,7 @@ An illustrative comparison between the conventional and quantitative MRI (qMRI).
 
 Using the same MRI scanner, it is possible to assign meaningful numbers to the images and this approach turns out to be the most common MRI method in food engineering [@Mariette:2012tv; @Ziegler:2003th]. [](#intFig1) illustrates the added value of quantitative MRI (qMRI) when applied to a sample familiar to everyone: a jellybean. The moisturization map indicates that the jellybean has formed a crispy shell while remaining chewy at the center, which is the desired texture [Fig. %sb](#intFig1). Given that the level of chewiness is determined by a threshold on a standardized measurement scale, a randomly selected part of the image can be still characterized by comparing selected pixel values against the established threshold [](#intFig1). This feature of qMRI offers an objective insight into how the texture of this soft confection changes over time, which would help determine its best before date ([](#intFig1), prognosis).
 
-The ability to reveal what underpins the appearance of visually similar samples is yet an- other powerful feature of qMRI. In a [Bean-Boozled](https://en.wikipedia.org/wiki/Jelly_Belly) challenge, which is a Russian roulette of jellybean flavors, tasty flavors are mixed with nauseous look-alikes [@Gambon:2015uq]. For example, a green jellybean may taste like lime (tasty) or lawn clippings (nauseous) in the Bean-Boozled game [](#intFig2). Therefore, no matter how experienced the player is, the chances of picking up a lime-flavored bean is as good as tossing a coin. Conventional MR images of a handful of green jellybeans do not offer a distinguishing feature, but only reveal their structure. As a result, the chances of making an unfortunate choice remain the same [](#intFig2).
+The ability to reveal what underpins the appearance of visually similar samples is yet another powerful feature of qMRI. In a [Bean-Boozled](https://en.wikipedia.org/wiki/Jelly_Belly) challenge, which is a Russian roulette of jellybean flavors, tasty flavors are mixed with nauseous look-alikes [@Gambon:2015uq]. For example, a green jellybean may taste like lime (tasty) or lawn clippings (nauseous) in the Bean-Boozled game [](#intFig2). Therefore, no matter how experienced the player is, the chances of picking up a lime-flavored bean is as good as tossing a coin. Conventional MR images of a handful of green jellybeans do not offer a distinguishing feature, but only reveal their structure. As a result, the chances of making an unfortunate choice remain the same [](#intFig2).
 
 ```{figure} ./img/int_fig2.jpg
 :label: intFig2