KomaMRI v0.9

JuliaHealth · Sep 30, 2024 · ed6d9c7 · ed6d9c7
1 parent 1708855
commit ed6d9c7
Show file tree

Hide file tree

Showing 6 changed files with 8 additions and 8 deletions.
diff --git a/KomaMRIBase/Project.toml b/KomaMRIBase/Project.toml
@@ -1,7 +1,7 @@
 name = "KomaMRIBase"
 uuid = "d0bc0b20-b151-4d03-b2a4-6ca51751cb9c"
 authors = ["Carlos Castillo Passi <[email protected]>"]
-version = "0.9.0-DEV"
+version = "0.9.0"
 
 [deps]
 Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"

diff --git a/KomaMRICore/Project.toml b/KomaMRICore/Project.toml
@@ -1,7 +1,7 @@
 name = "KomaMRICore"
 uuid = "4baa4f4d-2ae9-40db-8331-a7d1080e3f4e"
 authors = ["Carlos Castillo Passi <[email protected]>"]
-version = "0.9.0-DEV"
+version = "0.9.0"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"

diff --git a/KomaMRIFiles/Project.toml b/KomaMRIFiles/Project.toml
@@ -1,7 +1,7 @@
 name = "KomaMRIFiles"
 uuid = "fcf631a6-1c7e-4e88-9e64-b8888386d9dc"
 authors = ["Carlos Castillo Passi <[email protected]>"]
-version = "0.9.0-DEV"
+version = "0.9.0"
 
 [deps]
 FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"

diff --git a/KomaMRIPlots/Project.toml b/KomaMRIPlots/Project.toml
@@ -1,7 +1,7 @@
 name = "KomaMRIPlots"
 uuid = "76db0263-63f3-4d26-bb9a-5dba378db904"
 authors = ["Carlos Castillo Passi <[email protected]>"]
-version = "0.9.0-DEV"
+version = "0.9.0"
 
 [deps]
 Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"

diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "KomaMRI"
 uuid = "6a340f8b-2cdf-4c04-99be-4953d9b66d0a"
 authors = ["Carlos Castillo Passi <[email protected]>"]
-version = "0.9.0-DEV"
+version = "0.9.0"
 
 [deps]
 AssetRegistry = "bf4720bc-e11a-5d0c-854e-bdca1663c893"

diff --git a/examples/3.tutorials/lit-06-DiffusionMotion.jl b/examples/3.tutorials/lit-06-DiffusionMotion.jl
@@ -5,7 +5,7 @@ using PlotlyJS # hide
 using Random # hide
 
 # The purpose of this tutorial is to showcase the simulation of diffusion-related effects. 
-# For this, we are going to define a `Path <: Motion` to simulate the Brownian motion of spins.
+# For this, we are going to define a [`Path`](@ref) motion to simulate the Brownian motion of spins.
 # This is not the most efficient way of simulating diffusion, but it is a good way to understand the phenomenon.
 # In particular, we will going to simulate isotropic diffusion, characterized by the Apparent Diffusion Coefficient (ADC).
 
@@ -34,7 +34,7 @@ D = 2e-9               # Diffusion Coefficient of water in m^2/s
 T = 100e-3             # Duration of the motion
 Nt = 100               # Number of time steps
 Δt = T / (Nt - 1)      # Time sep
-Δr = sqrt(2 * D * Δt) # √ Mean square displacement
+Δr = sqrt(2 * D * Δt)  # √ Mean square displacement
 
 # Random walk is defined as the cumulative sum of random displacements:
 rng = MersenneTwister(1234) # Setting up the random seed
@@ -117,7 +117,7 @@ function bvalue(seq)
     block, axis = 2, 1 # Gx from second block
     G = seq.GR[axis, block].A
     δ = seq.GR[axis, block].T
-    Δ = dur(seq[1:2]) # Because there is no space in between
+    Δ = dur(seq[2:3]) # Because there are no gaps
     b = (2π * γ * G * δ)^2 * (Δ - δ/3)
     return b * 1e-6
 end