update all

README.md

@@ -116,10 +116,27 @@ You can install the package in any project with the following command :
 - enter the Julia package manager by typing `]` in the REPL. (the REPL should turn in blue)
 - if you want to activate an environment, type : `activate .` (otherwise the package will be installed in the global environment)
 - In order to add our unregistered package, type `add https://github.com/CRMSB/SEQ_BRUKER_a_MP2RAGE_CS_360`
-- if you want to use the package : `using SEQ_BRUKER_a_MP2RAGE_CS_360`
+- if you want to use the package in your script just add the following line : `using SEQ_BRUKER_a_MP2RAGE_CS_360`
 
 ## How to use the package
 
+Follow the example in the [documentation](https://crmsb.github.io/SEQ_BRUKER_a_MP2RAGE_CS_360/dev/generated/examples/simple_reco/) 
+
+**Steps :**
+- Define the path to the bruker dataset
+```julia
+path_bruker = joinpath(datadir, "MP2RAGE_FULLY")
+```
+- Perform the reconstruction 
+```julia
+d = reconstruction_MP2RAGE(path_bruker; mean_NR=true)
+```
+- write the results in the qBIDS format
+```julia
+subject_name = "sub_01"
+dir_path = "" # directory path where the files will be create
+write_bids_MP2RAGE(d,subject_name,dir_path)
+```
 
 ## Version
 

docs/lit/examples/advanced_reco.jl

@@ -12,14 +12,12 @@ using SEQ_BRUKER_a_MP2RAGE_CS_360
 using CairoMakie # plotting
 
 # In addition we load the package internally used to perform the reconstruction
+using SEQ_BRUKER_a_MP2RAGE_CS_360.MRIReco
+using SEQ_BRUKER_a_MP2RAGE_CS_360.MRIReco.RegularizedLeastSquares
 
 
-
-# ## Loading Package
-using LazyArtifacts # loading data
-using SEQ_BRUKER_a_MP2RAGE_CS_360
-using CairoMakie # plotting
-
+# ## Download the datasets
+# if you run the literate example offline change the following line by : `MP2_artifacts = artifact"MP2RAGE_data"
 datadir = Main.MP2_artifacts
 @info "The test data is located at $datadir."
 
@@ -28,8 +26,7 @@ path_bruker = joinpath(datadir, "MP2RAGE_CS2")
 
 # ## Compressed-sensing reconstruction
 # In order to use an advanced reconstruction we will pass some parameters that will be used by the reconstruction package MRIReco.jl
-using SEQ_BRUKER_a_MP2RAGE_CS_360.MRIReco
-using SEQ_BRUKER_a_MP2RAGE_CS_360.MRIReco.RegularizedLeastSquares
+
 
 # We have to create a parameter dictionnary that will be used. If you need more information about it take a look at [MRIReco.jl](https://github.com/MagneticResonanceImaging/MRIReco.jl)
 
@@ -41,7 +38,7 @@ CS[:iterations] = 30
 
 d = reconstruction_MP2RAGE(path_bruker; mean_NR=true,paramsCS = CS)
 
-# for comparison purpose let's perform the undersampled reconstruction (without the paramCS keyword)
+# for comparison purposes let's perform the undersampled reconstruction (without the paramCS keyword)
 d_under = reconstruction_MP2RAGE(path_bruker; mean_NR=true)
 
 
@@ -56,10 +53,10 @@ begin
   h=heatmap!(ax,abs.(d["im_reco"][:,:,60,1,1,1]),colormap=:grays)
 
 
-  ax=Axis(f[2,1],title="UNIT1 undersampled")
+  ax=Axis(f[2,1],title="T₁ map undersampled")
   h=heatmap!(ax,d_under["T1map"][:,:,60,1,1],colorrange = (500,2000))
 
-  ax=Axis(f[2,2],title="UNIT1 CS")
+  ax=Axis(f[2,2],title="T₁ map CS")
   h=heatmap!(ax,d["T1map"][:,:,60,1,1],colorrange = (500,2000))
 
   for ax in f.content   # hide decoration befor adding colorbar
@@ -77,4 +74,4 @@ end
 
 subject_name = "sub_01_cs"
 dir_path = "" # directory path where the files will be create
-write_bids_MP2RAGE(d,subject_name,dir_path)
+write_bids_MP2RAGE(d,subject_name,dir_path)

docs/lit/examples/simple_reco.jl

@@ -13,31 +13,31 @@ using SEQ_BRUKER_a_MP2RAGE_CS_360
 using CairoMakie # plotting
 
 # ## Download the datasets
-
+# if you run the literate example offline change the following line by : `MP2_artifacts = artifact"MP2RAGE_data"
 datadir = Main.MP2_artifacts
 @info "The test data is located at $datadir."
 
 # If you want to perform your own reconstruction, you can change the following line in order to point to another a bruker dataset
 path_bruker = joinpath(datadir, "MP2RAGE_FULLY")
 
 # ## Perform the reconstruction 
-# this function will perform a standard reconstruction without compressed-sensing. If your data are subsampled, results will be undersampled reconstruction.
+# this function will perform a standard reconstruction without compressed-sensing. If your data are subsampled it will result in subsampling artifacts (blurring + noise-like)
 #
-# the keyword mean_NR=true will average the image before performing the MP2RAGE/T1 maps estimation.
+# the keyword mean_NR=true will average the images accross the number of repetition dimension before performing the MP2RAGE/T1 maps estimation.
 # Otherwise an image/T₁ map will be generated for each Number Of Repetition (NR)
 d = reconstruction_MP2RAGE(path_bruker; mean_NR=true)
 
 
 # the result is a dictionnary with the following fields :
-# - "im_reco" : (x,y,z,Number of Repetition,TI) Complex
-# - "MP2RAGE" : (x,y,z,TI) Float
-# - "T1map" : (x,y,z,Number of Repetition) Float
+# - "im_reco" : (x,y,z,Number of Channel , Number of Repetition,TI) Complex
+# - "MP2RAGE" : (x,y,z,Number of Channel , Number of Repetition) Float
+# - "T1map" : (x,y,z,Number of Channel , Number of Repetition) Float
 # - "params_prot"
 # - "params_reco"
 # - "params_MP2RAGE"
 # 
-# im_reco corresponds to the TI₁ and \TI₂ images in the complex format with 6 dimensions :
-# (x,y,z,Number of Repetition,TI)
+# im_reco corresponds to the TI₁ and TI₂ images in the complex format with 6 dimensions :
+# (x,y,z, Number of Channel , Number of Repetition,TI)
 
 
 # We can check the results
@@ -53,7 +53,7 @@ begin
   ax=Axis(f[2,1],title="UNIT1 / MP2RAGE")
   h=heatmap!(ax,d["MP2RAGE"][:,:,60,1,1],colormap=:grays)
 
-  ax=Axis(f[2,2],title="UNIT1 / MP2RAGE")
+  ax=Axis(f[2,2],title="T₁ map")
   h=heatmap!(ax,d["T1map"][:,:,60,1,1],colorrange = (500,2000))
 
   for ax in f.content   # hide decoration befor adding colorbar
@@ -64,8 +64,8 @@ begin
   f
 end
 
-# The Lookup table used for the reconstruction is stored in the dictionnary (LUT)
-# First columns is the range of T1.
+# The Lookup table used for the reconstruction is stored in the dictionnary (LUT).
+# First dimension is the range of T1 and the 2nd is the expected value of the MP2RAGE signal between -0.5 to 0.5.
 f=Figure()
 ax = Axis(f[1,1],xlabel="T₁ [ms]")
 lines!(ax,d["LUT"])
@@ -94,7 +94,9 @@ sub_01/
    └─ sub_01_inv-2-phase_MP2RAGE.nii.gz
 ```
 
-If you want to generate the T1 map with another tools like qMRLab
-the required MP2RAGE parameters are stored in the **MP2RAGE.json** file.
+For simplicity the T₁ map is stored in the anat/ folder like the ones created by Siemens.
+
+If you want to generate the T1 map with another tool like qMRLab
+the required MP2RAGE parameters are stored in the **MP2RAGE.json** file. In that case the data are supposed to be stored in a derivatives folder (see[qBIDS format recommandation](https://bids-specification.readthedocs.io/en/stable/appendices/qmri.html))
 =#
 

docs/make.jl

@@ -22,6 +22,7 @@ makedocs(;
         "Home" => "index.md",
         "Examples" =>["generated/examples/simple_reco.md",
                         "generated/examples/advanced_reco.md"],
+        "API" => "api.md"
     ],
 )
 

src/BIDS.jl

@@ -7,12 +7,13 @@ This function writes data from a dictionary (`d`) in BIDS (Brain Imaging Data St
 
 **Arguments:**
 
-* `d` (Dict): A dictionary containing the data to be written. Expected keys:
+* `d` (Dict): A dictionary containing the data to be written. Expected key-value pairs:
     * `im_reco` (Array): 5D array containing the reconstructed images.
-    * `MP2RAGE` (Array): T1 map image.
-    * `T1maps` (Array): Additional T1 map data (optional).
-    * `params_prot` (Dict): Dictionary containing acquisition parameters.
-    * `params_MP2RAGE` (Dict): Dictionary containing MP2RAGE specific parameters.
+    * `MP2RAGE` (Array): Combined MP2RAGE image data.
+    * `T1map` (Array): Calculated T1 map from MP2RAGE images.
+    * `params_prot` (Dict): Protocol parameters extracted from the Bruker file.
+    * `params_MP2RAGE` (Struct): Dictionary containing MP2RAGE specific parameters.
+
 * `subname` (AbstractString): The name of the subject.
 * `folder` (AbstractString, optional): The folder where the BIDS data will be written. Defaults to the current directory.
 

src/bruker_sequence.jl

@@ -3,7 +3,7 @@ export RawAcquisitionData_MP2RAGE
 """
     RawAcquisitionData_MP2RAGE(b::BrukerFile)
 
-Convert a Bruker dataset acquired with the a_MP2RAGE_CS_360 sequence into a 
+Convert a Bruker dataset acquired with the a\\_MP2RAGE\\_CS\\_360 sequence into a 
 `RawAcquisitionData` object compatible with the MRIReco functions.
 
 Input : 

src/reconstruction.jl

@@ -1,4 +1,41 @@
-export reconstruction_MP2RAGE
+export reconstruction_MP2RAGE, params_from_seq
+
+"""
+    reconstruction_MP2RAGE(path_bruker::String; mean_NR::Bool = false, paramsCS::Dict = Dict())
+
+Reconstructs MP2RAGE MRI data from a specified Bruker file path, returning images and T1 maps.
+
+# Arguments
+- `path_bruker::String`: Path to the Bruker file containing MRI acquisition data.
+- `mean_NR::Bool`: If `true`, calculates the mean of the reconstructed data accross the repetition (default: `false`).
+- `paramsCS::Dict`: Optional dictionary for customizing reconstruction parameters. These values override the default parameter dictionary (`params`).
+
+# Returns
+A dictionary with the following key-value pairs:
+- `"im_reco"`: Reconstructed MP2RAGE image array, permuted to match the expected order.
+- `"MP2RAGE"`: Combined MP2RAGE image data.
+- `"T1map"`: Calculated T1 map from MP2RAGE images.
+- `"params_reco"`: Dictionary of parameters used for reconstruction.
+- `"params_MP2RAGE"`: Sequence parameters derived from the Bruker file.
+- `"params_prot"`: Protocol parameters extracted from the Bruker file.
+- `"LUT"`: Lookup table with T1 range and associated values.
+
+# Description
+The function performs the following steps:
+1. Reads acquisition data from the Bruker file at `path_bruker`.
+2. Constructs calibration data using an ESPIRiT sensitivity map from low-resolution data.
+3. Sets reconstruction parameters, allowing for custom parameters specified in `paramsCS`.
+4. Reconstructs the MP2RAGE image data and optionally averages across repeated measurements.
+5. Permutes the resulting array dimensions for compatibility.
+6. Extracts T1 maps from MP2RAGE images and constructs a lookup table (`LUT`).
+
+# Example
+```julia
+d = reconstruction_MP2RAGE("path/to/bruker_data", mean_NR = true, paramsCS = Dict(:iterations => 5))
+println(d["T1map"])
+```
+
+"""
 
 function reconstruction_MP2RAGE(path_bruker;mean_NR::Bool = false,paramsCS=Dict())
   b = BrukerFile(path_bruker)
@@ -45,6 +82,17 @@ function reconstruction_MP2RAGE(path_bruker;mean_NR::Bool = false,paramsCS=Dict(
   return d
 end
 
+"""
+    params_from_seq(b::BrukerFile)
+
+Extracts MP2RAGE sequence parameters from a Bruker file and returns them in a `ParamsMP2RAGE` structure.
+
+# Arguments
+- `b::BrukerFile`: Bruker file containing sequence parameter information.
+
+# Returns
+A `ParamsMP2RAGE` structure containing key sequence timings and settings for MP2RAGE reconstruction.
+"""
 function params_from_seq(b::BrukerFile)
   return ParamsMP2RAGE(
       parse(Float64,b["EffectiveTI"][1]),