bugfixes; text edits

Author: JakobAsslaender

Fit_qMT_to_literatureT1.jl

@@ -286,7 +286,7 @@ extrema(T1_literature)
 variation(T1_literature)
 
 # ### Median absolute deviation
-# In the paper, the median absolute deviation wrt. the mean value is used, as it is more robust to outliers compared to the mean absolute deviation or the standard deviation. Note, however, that the median absolute deviation of the mono-exponential fit is dominated by an outlier, artificially inflating the corresponding reduction.
+# In the paper, the median absolute deviation from the mean value is used, as it is more robust to outliers compared to the mean absolute deviation or the standard deviation. Note, however, that the median absolute deviation of the mono-exponential fit is dominated by an outlier, artificially inflating the corresponding reduction.
 
 # A mono-exponential model explains the following fraction of the T₁ variability in the literature:
 1 - mad(fit_mono.resid;   center=mean(fit_mono.resid))    / mad(T1_literature; center=mean(T1_literature))
@@ -334,7 +334,7 @@ write(io, "\n") #src
 
 for i_seq in eachindex(T1_literature) #src
     write(io, @sprintf("%1.3f ", T1_literature[i_seq])) #src
-    [write(io, @sprintf("%1.3f ", T1_simulated[i_fit][i_seq])) for i_fit in eachindex(T1_simulated)] #src
+    [write(io, @sprintf("%1.3f ", T1_simulated_v[i_fit][i_seq])) for i_fit in eachindex(T1_simulated_v)] #src
     write(io, @sprintf("%s ", string(seq_type[i_seq])[1])) #src
     write(io, "\n") #src
 end #src
@@ -358,11 +358,11 @@ println("model & \$T_1^s\$ constraint & \$\\Delta\$AIC & \$\\Delta\$BIC \\\\") #
 println("\\midrule") #src
 println("mono-exponential   & none            & 0           & 0           \\\\") #src
 i = findfirst(fit_name .== "unconstr_Graham") #src
-println(@sprintf("Graham's & none & %1.1f & %1.1f \\\\", ΔAIC[i], ΔBIC[i])) #src
+println(@sprintf("Graham's & none & %1.1f & %1.1f \\\\", ΔAIC_v[i], ΔBIC_v[i])) #src
 i = findfirst(fit_name .== "constr_gBloch") #src
-println(@sprintf("generalized Bloch & \$T_1^s = T_1^f\$ & %1.1f & %1.1f \\\\", ΔAIC[i], ΔBIC[i])) #src
+println(@sprintf("generalized Bloch & \$T_1^s = T_1^f\$ & %1.1f & %1.1f \\\\", ΔAIC_v[i], ΔBIC_v[i])) #src
 i = findfirst(fit_name .== "unconstr_gBloch") #src
-println(@sprintf("generalized Bloch & none & %1.1f & %1.1f \\\\", ΔAIC[i], ΔBIC[i])) #src
+println(@sprintf("generalized Bloch & none & %1.1f & %1.1f \\\\", ΔAIC_v[i], ΔBIC_v[i])) #src
 println("\\bottomrule") #src
 
 

Project.toml

@@ -1,3 +1,5 @@
+version = "0.2"
+
 [deps]
 ApproxFun = "28f2ccd6-bb30-5033-b560-165f7b14dc2f"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"

README.md

@@ -2,25 +2,25 @@
 |:------------------------- |:------------------------------|
 | [![][docs-img]][docs-url] | [![][arXiv-img1]][arXiv-url1] |
 
-This code reproduces all results in the paper [Magnetization transfer explains most of the T1 variability in the MRI literature][arXiv-url1]. Please go to the [Documentation][docs-url] for a detailed description of the code.
+This code reproduces all results in the paper [Magnetization transfer explains most of the T1 variability in the MRI literature][arXiv-url1]. Please refer to the [Documentation][docs-url] for a detailed description of the code.
 
 The code is written in the open-source language Julia and is structured as follows:
-- Fit_qMT_to_literatureT1.jl is the main script and implements the pulse sequence simulations and the mono-exponential fitting routines. It also calls the fit of the qMT models to the variable literature T1 estimates.
-- helper_functions.jl contains implementations of RF pulses and some helper functions, which are less relevant for understanding the simulations
-- Project.toml and Manifest.toml contain information about the packages used by the simulation, facilitating their automated installation.
+- `T1_mapping_methods.jl` implements the pulse sequence simulations and the mono-exponential fitting routines of each T₁-mapping method.
+- `Fit_qMT_to_literatureT1.jl` is the main script that performs the fit of the qMT models to the variable literature T₁ estimates.
+- `helper_functions.jl` contains implementations of RF pulses, their propagators, and some helper functions, which are less relevant for understanding the simulations.
+- `Project.toml` and `Manifest.toml` contain information about the packages used by the simulation, facilitating their automated installation.
 
-Julia can be downloaded from the website https://julialang.org or, on Unix systems, installed by simply calling
+Julia can be downloaded from https://julialang.org or, on Unix systems, by simply calling
 
     `curl -fsSL https://install.julialang.org | sh`
 
 from the command line.
 
-In order to run the simulations, place all four files in the same folder and call
+In order to run the simulations, place all five files in the same folder, `cd` into this folder, and call
 
-    `julia -iq --threads=auto some_path/Fit_qMT_to_literatureT1.jl`
-
-replacing `some_path` with the path to the files. For a more interactive interface, the code can be called from Visual Studio Code with the Julia extension.
+    `julia -iq --threads=auto Fit_qMT_to_literatureT1.jl`
 
+For a more interactive interface, the code can be called from Visual Studio Code with the Julia extension.
 
 [docs-img]: https://img.shields.io/badge/docs-dev-blue.svg
 [docs-url]: https://jakobasslaender.github.io/T1variability/dev/

docs/make.jl

@@ -51,7 +51,6 @@ makedocs(;
     authors="Jakob Asslaender <jakob.asslaender@nyumc.org> and contributors",
     sitename="T₁ variability",
     format=Documenter.HTML(;
-        inventory_version=0.1, # TODO
         prettyurls=get(ENV, "CI", "false") == "true",
         canonical="https://JakobAsslaender.github.io/T1variability",
         assets=String[],

helper_functions.jl

@@ -59,7 +59,7 @@ const R2sl_2 = precompute_R2sl(TRF_min=10e-6, TRF_max=20e-6, ω1_max=π / 10e-6,
 const R2sl_3 = precompute_R2sl(TRF_max=1e-3, ω1_max=π / 500e-6, T2s_min=12e-6, T2s_max=13e-6, B1_max=1.1)[1]
 nothing #hide #md
 
-# We implemented different methods for the function `RF_pulse_propagator` that infered with Julia's multiple dispatch logic based on the type of the input parameters. The functions in this section take the variable `model` of type `gBloch` and implement the generalized Bloch model. The first method further takes the variable `ω1` of the abstract type `Number`, i.e., it implements pulse propagators for a constant ω₁.
+# We implemented different methods for the function `RF_pulse_propagator` that inferred with Julia's multiple dispatch logic based on the type of the input parameters. The functions in this section take the variable `model` of type `gBloch` and implement the generalized Bloch model. The first method further takes the variable `ω1` of the abstract type `Number`, i.e., it implements pulse propagators for a constant ω₁.
 function RF_pulse_propagator(ω1::Number, B1, ω0, TRF, m0s, R1f, R2f, Rx, R1s, T2s, model::gBloch; spoiler=true)
     if TRF >= 1e-6 && TRF <= 3e-6
         R2s = R2sl_1(TRF, abs(ω1 * TRF), B1, T2s)