NeuroPoly student feedback: Thomas Dagonneau

[tomDag25]

This issue is the global feedback for the mooc from Thomas Dagonneau.
Disclaimer : I'm not a very good english speaker so I might see some typo where there are not or miss some.

[tomDag25]

Feedback for the ReadMe page :

• - in 1.1 : maybe add (mOOC) after mini Open Online Course
• - feels confusing to speak from a book in 1.2 section when you didn't mention anything about a book previously
• - typo in 1.2 : Quantitative MRI (qMRI) aims to promises precise and reproducible measurements of tissue properties using MRI.
• - typo in 1.3 : Leveraging the NeuroLibre platform, readers can access fully reproduce the material in this book and allows them to engage with real qMRI data through their web browser.
• - typo in 1.3 space after the point : their web browser.This

Global feedback : Good first page : defines what is a mooc and the goal of the mooc

[tomDag25]

Feedback for Introduction to qMRI -> Basics of MRI and qMRI :

• - in the See also : maybe put the name of the article instead of the year ? At first I didn't get that it was a link to the article and I thought the only link was to Nishimura wikipedia page

Global feedback : Clear

[mathieuboudreau]

Tagging @agahkarakuzu as chapter 1 was from his thesis I believe

[tomDag25]

Feedback for Introduction to qMRI -> The million dollar question:

• - not sure if that's the goal but in 1.1 the MR acronym has not the same feature as MRI to explain its signification
• - typo 1.1 : sug- gests (maybe it's normal I don't know much about written reports)
• - Fig1 : would be clearer and easier to read if the conventional MRI was on the right side of the figure since you speak about it before. Else you have to read the figure in the opposite direction to the one suggested by the text. (Else the figure's content is good)
• - typo in 1.1 : an- other

Global feedback : Loved the bean russian roulette example. The historical aspect is very interesting !

[tomDag25]

Feedback for Introduction to qMRI -> A pictorial and historic journey into how MRI works

• - the figure numbers don't seem to be making reference to any figure in the section
• - typo in 1.1.1 : However, in reality, we don’t have access to observe NMR effects at such a fine-grained level
• - 1.1.1 : quantum-jitters -> I don't know what it is so maybe defining it or putting a link to the wikipedia would be great (don't know if it was planned since there is no link in this page)
• - 1.1.1 : Schrödinger’s cat -> put the wikipedia link ? Don't know how obvious this illustration is
• - FIg5 : maybe put it after the text citing it. When you read the page you don't really understand where it's coming from before the text about Bloch

Global feedback : Good ! In the version I reviewed most links where missing and also references but I think it's normal

[tomDag25]

Feedback for Introduction to qMRI -> Measuring and encoding the MRI signal

• - there is no under-title like in other sections
• - in 1.1 I feel like the reader could benefit from seeing the new equation after removing the components = 0 : In this case, the last two terms of the equation vanish, leaving the precessional component of the equation.
• - numbers of cited equations don't refer to the numbers of the equations
• - 1.2 : The analogy with the defibrillator is not presented the same way as the analogy with beans and dating (in a square highlighted)
• - Fig4 : I know what you are talking about so I get it but maybe to emphasise what a gradient is you could put a graph under the MRI scanner showing how the value evolves according to z for example ?
• - typo in 1.4 : ob- servations

Global feedback : Good ! In the version I reviewed most links where missing and also references but I think it's normal

[tomDag25]

Feedback for Introduction to qMRI -> Two MRI sequences and two qMRI measurements

• - I feel like a bit of explanation for the difference between T2 and T2* could be appreciated. For now there is only : T2∗ – the effective T2
• - typo at the end : con- ventional
• [ ]

Global feedback : Good, and I validate both musical bands !

[tomDag25]

Feedback for Introduction to qMRI -> Will qMRI take over the world?

• - the figure numbers don't seem to be making reference to any figure in the section
• - Fig1 : very nice Fig but I don't find it easy to understand
• - typo in 1.1 : Figure 2.23 shows that all
• [ ]
• qMRI methods share a common methodolog
• - typo in 1.1 : chal- lenging
• - 1.2 is just a title ?
• [ ]

Global feedback : Good, 1.2 is missing there is only the title.

[tomDag25]

Feedback for T1 Mapping -> Inversion recovery -> Introduction

• - typo space after the point : inversion recoverytechnique
• - the cited figures don't refer to existing figures
• - change in the way to count figure between the Introduction to qMRI chapter and this one (figure 1 vs figure 2.1)

Global feedback : Good

[tomDag25]

Feedback for T1 Mapping -> Inversion recovery -> Signal Modelling

• - typo : 5_T_1 (2 times)
• - Fig2.3 : maybe choose a default value of T1 so that when you load the page there is a difference between the two curves ?

Global feedback : Good

[tomDag25]

Feedback for T1 Mapping -> Inversion recovery -> Data Fitting

• - typo : 5_T_1, 1.5_T_1
• - only paper without a link : Barral et al. 2010

Global feedback : Good

[tomDag25]

Feedback for T1 Mapping -> Inversion recovery -> Benefits and Pitfalls

• - References formating changed : green arrow

Global feedback : Good

[tomDag25]

Feedback for T1 Mapping -> Inversion recovery -> Other Saturation-Recovery T1 Mapping techniques

Global feedback : Good

[tomDag25]

Feedback for T1 Mapping -> Variable Flip Angle -> Introduction

Global feedback : Good

[tomDag25]

Feedback for T1 Mapping -> Variable Flip Angle -> Signal Modelling

• - typo : using Bloch simulations (orange) -> it's red
• - I think the text after Figure 2.9 should be in the legend of the figure ?
• - References with a green arrow

Global feedback : Good but not sure about the Fig 2.9 legend

[agahkarakuzu]

@tomDag25 thanks for the feedback! I will reflect these when it nears minor tweaks phase.

[tomDag25]

Feedback for T1 Mapping -> Variable Flip Angle -> Data fitting

• - the link for linear least square doesn't work
• - no figure 2.12 ?
• - References with a green arrow
• - Problem with the order of elements at the end of the page

Global feedback : Good but no figure 2.12

[tomDag25]

Feedback for T1 Mapping -> Variable Flip Angle -> Benefits and Pitfalls

Global feedback : Good

[tomDag25]

Feedback for T1 Mapping -> MP2RAGE -> Introduction

• - Figure 1 -> No link and also no figure 1 ? Probably figure 2.14

Global feedback : good

[tomDag25]

Feedback for T1 Mapping -> MP2RAGE -> Signal Modelling

• - in the following part I would keep using markdown for formatting, as a reader it feels strange to change in the middle of the text : no partial Fourier or parallel imaging acceleration), then these values are TA = TI1 - (n/2)TR, TB = TI2 - (TI1 + nTR), and TC = TRMP2RAGE - (TI1 + (n/2)TR), where n is the number of voxels acquired in the 3D phase encode direction varied within each GRE block. The value m{sub}`1z,ss is the steady-state longitudinal magnetization prior to the inversion pulse, and is given by:
• - typo : From Equations 2.13, 2.14, 2.15, and 2.13
• [ ]

Global feedback : good but there seem to be a problem with the markdown in this page

[tomDag25]

Feedback for T1 Mapping -> MP2RAGE -> Data Fitting

• - no link : (e.g. Levenberg-Marquardt)
• - the whole paragraph is allready in the Introduction : MP2RAGE is an extension of the conventional MPRAGE pulse sequence widely used in clinical studies Haase et al., 1989Mugler & Brookeman, 1990. A simplified version of the MP2RAGE pulse sequence is shown in Figure 2.14. MP2RAGE can be seen as a hybrid between the inversion recovery and VFA pulse sequences: a 180° inversion pulse is used to prepare the magnetization for T1 sensitivity at the beginning of each TRMP2RAGE, and then two images are acquired at different inversion times using gradient recalled echo (GRE) imaging blocks with low flip angles and short repetition times (TR). During a given GRE imaging block, each excitation pulse is followed by a constant in-plane (“y”) phase encode weighting (varied for each TRMP2RAGE), but with different 3D (“z”) phase encoding gradients (varied at each TR). The center of k-space for the 3D phase encoding direction is acquired at the TI for each GRE imaging block. The main motivation for developing the MP2RAGE pulse sequence was to provide a metric similar to MPRAGE, but with self-bias correction of the static (B0) and receive (B1-) magnetic fields, and a first order correction of the transmit magnetic field (B1+). However, because two images at different TIs are acquired (unlike MPRAGE, which only acquires data at a single TI), information about the T1 values can also be inferred, thus making it possible to generate quantitative T1 maps using this data.

Global feedback : good but repeating the paragraph highlighted above feels strange for the reader

[tomDag25]

Feedback for T1 Mapping -> MP2RAGE -> Benefits and Pitfalls

Global feedback : good

[tomDag25]

Feedback for T2 Mapping -> Introduction

Global feedback : good

[tomDag25]

Feedback for T2 Mapping -> T2 mapping vs T2-weighted imaging

Global feedback : good

[tomDag25]

Feedback for T2 Mapping -> Monoexponential T2 mapping -> Introduction

Global feedback : good

[tomDag25]

Feedback for T2 Mapping -> Monoexponential T2 mapping -> Signal modelling

Global feedback : good

[tomDag25]

Feedback for T2 Mapping -> Monoexponential T2 mapping -> Data fitting -> Data fitting

Global feedback : good

[tomDag25]

Feedback for T2 Mapping -> Monoexponential T2 mapping -> Data fitting -> T2*

• - eq 3.4 is the same as eq 3.2 and I'm not sure that it is normal
• - I'm not sure to understand this part : Until now, we have assumed that the transverse signal (Mxy) decays exponentially with the echo time divided by the T2 constant (see Eq. 3.3). However, in practice, other factors such as B0 inhomogeneities can cause a more rapid loss of the transverse signal; this results in a faster transverse decay, which is referred to as T2* relaxation (see Figure 3.1). The relation between T2 and T2* is described as follows Brown et al., 2014:

Global feedback : I don't know much about the subject but this part wasn't so clear to me

[tomDag25]

Feedback for B0 Mapping -> Dual Echo B0 Mapping -> Signal Theory

• - follows the following equation (not considering transient effects such as eddy currents) in the rotating frame of reference. -> would put the parentheses at the end of the sentence
• - typo missing gamma: e, is the gyromagnetic

Global feedback: good

[tomDag25]

Feedback for B0 Mapping -> Dual Echo B0 Mapping -> Single frequency population

Global feedback: good

[tomDag25]

Feedback for B0 Mapping -> Dual Echo B0 Mapping -> Multi frequency population

Global feedback: good

[tomDag25]

Feedback for B0 Mapping -> Dual Echo B0 Mapping -> Benefits and Pitfalls

• - advanced B0 mapping section -> put the advanced in the link

Global feedback: good

[tomDag25]

Feedback for B0 Mapping -> Phase unwrapping -> Introduction

• - typo: (offset less than )

• Global feedback: good, maybe it would be good to help the reader understand to put a little animation what phase unwrapping means: for example a point going around the circle and a curve that is drawn base on the coordinate of the points with a jump when passing from +pi to -pi ?

code for the figure:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation, PillowWriter

# Parameters
radius = 1
num_points = 200
theta = np.linspace(-np.pi, np.pi, num_points)
x = radius * np.cos(theta)
y = radius * np.sin(theta)

# Data for the curve
curve_x = np.linspace(0, 30, num_points * 3)  # Extended for three circles
curve_y = np.zeros_like(curve_x)

# Create figure and axes
fig, (ax_circle, ax_curve) = plt.subplots(1, 2, figsize=(10, 5))

# Circle plot
circle_line, = ax_circle.plot(x, y, 'b-', label='Circle')  # Full circle
circle_point, = ax_circle.plot([], [], 'ro', label='Point')  # Moving point

ax_circle.set_xlim(-1.5, 1.5)
ax_circle.set_ylim(-1.5, 1.5)
ax_circle.set_aspect('equal')
ax_circle.set_title('Circle')
ax_circle.legend()

# Curve plot
curve_line, = ax_curve.plot([], [], 'g-', label='Curve')
ax_curve.set_xlim(0, 30)
ax_curve.set_ylim(-np.pi, np.pi)
ax_curve.axhline(0, color='k', linewidth=0.5, linestyle='--')  # Reference line
ax_curve.set_title('Curve')
ax_curve.legend()

# Animation function
def update(frame):
    index = frame % num_points  # Cycle through the circle
    full_rotation = frame // num_points  # Count rotations
    # Update circle
    circle_point.set_data(x[index], y[index])
    
    # Update curve
    curve_y[frame] = theta[index]
    curve_line.set_data(curve_x[:frame+1], curve_y[:frame+1])
    return circle_point, curve_line

# Total frames for three full circles
total_frames = num_points * 3

# Animation
ani = FuncAnimation(fig, update, frames=total_frames, interval=20, blit=True)

# Save animation as a GIF for three full circles
ani.save('/content/circle_curve_three_rounds.gif', writer=PillowWriter(fps=15))
print("Animation saved as 'circle_curve_three_rounds.gif'")

[tomDag25]

Feedback for B0 Mapping -> Phase unwrapping -> Temporal unwrapping

• - (>=2) -> put in markdown
• - Eq. 1 does not exist
• - Not sure that's a problem but Fig 5.10 is too wide

Global feedback: good

[tomDag25]

Feedback for B0 Mapping -> Phase unwrapping -> Spatial unwrapping

• - Fig 5.12 not displayed

Global feedback: good

[tomDag25]

Feedback for B0 Mapping -> Phase unwrapping -> Phase unwrapping ambiguity

Global feedback: good

[tomDag25]

Feedback for B0 Mapping -> Phase unwrapping -> Problematic phase map properties

Global feedback: good

[tomDag25]

Feedback for B0 Mapping -> Phase unwrapping -> Software

• - last paragraph: make it as a list since you are listing it will be easier to read

Global feedback: good

[tomDag25]

Feedback for B0 Mapping -> Advanced B0 Mapping Methods -> Introduction

Global feedback: good

[tomDag25]

Feedback for B0 Mapping -> Advanced B0 Mapping Methods -> Multi-echo B0 Mapping

• - typo missing word ?: the phase changes per unit time
• - typo: δTE should be less than but greater than zero

Global feedback: good

[tomDag25]

Feedback for B0 Mapping -> Advanced B0 Mapping Methods -> Reducing eddy currents

Global feedback: good

[tomDag25]

Feedback for B0 Mapping -> Advanced B0 Mapping Methods -> Realtime B0 mapping

• - EPI abreviation without legend

Global feedback: good

[tomDag25]

Feedback for B0 Mapping -> Conclusion

Global feedback: good but there is no Conclusion in other parts, I find it nice but maybe try to have the same structure in every chapter

[tomDag25]

Feedback for Magnetization Transfer Imaging -> Quantitative Magnetization Transfer -> Signal Modelling

• - typo: of the the

Global feedback: Maybe I didn't pay attention in other pages but I saw in this one that you don't use the same formating when declaring a variable and using it in an eq. Maybe do all in latex. Else good

[tomDag25]

Feedback for Magnetization Transfer Imaging -> Quantitative Magnetization Transfer -> Introduction

Global feedback: Good.

[tomDag25]

Feedback for Magnetization Transfer Imaging -> Quantitative Magnetization Transfer -> Data fitting

Global feedback: Good.

[tomDag25]

Feedback for Magnetization Transfer Imaging -> Quantitative Magnetization Transfer -> Summary

Global feedback: Good.

[tomDag25]

Feedback for Magnetization Transfer Imaging -> Magnetization Transfer Ratio -> Introduction

• - I find this sentence really long and I misread 70s and 80s at first thinking you were speaking of seconds so maybe not use that: Although the images acquired by clinical MRI machines can only be generated from signal from mobile hydrogen, these do interact with nearby molecules and atoms via the electromagnetic fields they mutually generate, and in the 70s and 80s a cross-relaxation mechanism was discovered that sensitizes mobile protons to nearby targeted semi-solid molecules, such as myelin Edzes & Samulski, 1977Edzes & Samulski, 1978Wolff & Balaban, 1989.
• - add definition: MS

Global feedback: Good.

[tomDag25]

Feedback for Magnetization Transfer Imaging -> Magnetization Transfer Ratio -> MTR in theory

• - maybe put a link to this equation: Δx⋅Δp ≥ constant , this whole part is pretty dense...

Global feedback: good.

[tomDag25]

Feedback for Magnetization Transfer Imaging -> Magnetization Transfer Ratio -> MTR in practice

• - two table 2
• - typo: substantial Figure 6.11 illustrates how

Global feedback: good.

[tomDag25]

Feedback for Magnetization Transfer Imaging -> Magnetization Transfer Ratio -> Parting Thoughts

• - typo: segment -> section

Global feedback: good.

[tomDag25]

Feedback for Magnetization Transfer Imaging -> Magnetization Transfer Saturation -> Introduction

• - has been shown to be: -> eq missing

Global feedback: good.

[tomDag25]

Feedback for Magnetization Transfer Imaging -> Magnetization Transfer Saturation -> Theory

• - typo: into [4]
• - typo: equation [1].

Global feedback: I feel like it's a bit dense, at least for me that doesn't do much MRI physics, maybe split it in multiple chapters or make a short version with key take aways for people that are not necessary in the field.

[tomDag25]

Feedback for Magnetization Transfer Imaging -> Magnetization Transfer Saturation -> Simulations

Global feedback:

[tomDag25]

Global feedback: I found the course really cool ! I learned a lot and I feel like most of the content is not to hard to get. The global improvements I would suggest is standardizing the organisation between different parts: as a reader it can be surprising if not.
Else I think it's really good ! Don't hesitate if you want to rereview some parts or explain more my feedback !

[samuellestonge]

Thanks a lot for your feedback, @tomDag25 !

[mathieuboudreau]

@tomDag25

• typo : using Bloch simulations (orange) -> it's red

I dunno, to me it's orange.

Though it is right on the border of red and orange on the colorwheel, when a large section is "painted" with it I clearly would say orange more than red,

Maybe one of us has a slight color blindness? haha

[tomDag25]

@mathieuboudreau just checked it on my screen and I think it comes from the fact I didn’t zoom in. I agree when zooming on the fig that it’s orange but when looking at it without zooming I thought it was red I don’t know why... Maybe I’m in fact a bit color blind haha