update README

README.md

@@ -7,16 +7,22 @@
 This project is an improvement of the [Python source code](http://www.neuroimaging.at/pages/qsm.php) (Cython)  
 The parallel CPU version is about twice as fast as the Cython version, the GPU version is about 10x faster than the Cython version (on a RTX 3060 Laptop GPU 6GB)  
 
+## References
+
+- Bredies, K., Ropele, S., Poser, B. A., Barth, M., & Langkammer, C. (2014). Single-step quantitative susceptibility mapping using total generalized variation and 3D EPI. In Proceedings of the 22nd Annual Meeting ISMRM (Vol. 62, p. 604).
+
+- Langkammer, C., Bredies, K., Poser, B. A., Barth, M., Reishofer, G., Fan, A. P., ... & Ropele, S. (2015). Fast quantitative susceptibility mapping using 3D EPI and total generalized variation. Neuroimage, 111, 622-630.
+
 ## Setup
 
 1. Install [Julia](https://julialang.org/downloads/) (v1.9+ recommended)
 2. Install this package  
     Open the julia REPL and type:
 
     ```julia
-        import Pkg
-        Pkg.add(url="https://github.com/korbinian90/QuantitativeSusceptibilityMappingTGV.jl")
-        Pkg.add("MriResearchTools") # for nifti handling
+    import Pkg
+    Pkg.add(url="https://github.com/korbinian90/QuantitativeSusceptibilityMappingTGV.jl")
+    Pkg.add("MriResearchTools") # for nifti handling
     ```
 
 ## Example to run TGV
@@ -26,32 +32,32 @@ The parallel CPU version is about twice as fast as the Cython version, the GPU v
 2. Run this in the REPL or a julia file
 
     ```julia
-        using QuantitativeSusceptibilityMappingTGV, MriResearchTools
-
-        mask = niread("<mask-path>") .!= 0; # convert to boolean
-        phase = readphase("<phase-path>");
-        
-        voxel_size = header(phase).pixdim[2:4] # in [mm]
-        TE = 0.004 # in [s]
-        fieldstrength = 3 # in [T]
-        
-        # Automatically runs on GPU, if a CUDA device is detected
-        chi = qsm_tgv(phase, mask, res; TE=TE, fieldstrength=fieldstrength);
-
-        savenii(chi, "chi", "<folder-to-save>")
+    using QuantitativeSusceptibilityMappingTGV, MriResearchTools
+
+    mask = niread("<mask-path>") .!= 0; # convert to boolean
+    phase = readphase("<phase-path>");
+    
+    voxel_size = header(phase).pixdim[2:4] # in [mm]
+    TE = 0.004 # in [s]
+    fieldstrength = 3 # in [T]
+    
+    # Automatically runs on GPU, if a CUDA device is detected
+    chi = qsm_tgv(phase, mask, res; TE=TE, fieldstrength=fieldstrength);
+
+    savenii(chi, "chi", "<folder-to-save>")
     ```
 
     ```julia
-        # Run on CPU in parallel
-        chi = qsm_tgv(phase, mask, res; TE, fieldstrength, alpha=(0.0015, 0.0005), gpu=false);
+    # Run on CPU in parallel
+    chi = qsm_tgv(phase, mask, res; TE, fieldstrength, alpha=(0.0015, 0.0005), gpu=false);
     ```
 
     ```julia
-        # Convenient way to obtain settings from JSON file
-        using JSON
-        settings = JSON.parse(read("<phase-json>", String))
-        fieldstrength = settings["MagneticFieldStrength"]
-        TE = settings["EchoTime"]
+    # Convenient way to obtain settings from JSON file
+    using JSON
+    settings = JSON.parse(read("<phase-json>", String))
+    fieldstrength = settings["MagneticFieldStrength"]
+    TE = settings["EchoTime"]
     ```
 
     It uses the number of CPU threads julia was started with. You can use `julia --threads=auto` or set it to a specific number of threads.
@@ -61,14 +67,14 @@ The first execution might take some time to compile the kernels (~1min).
 ## Self contained example to test if everything works
 
 ```julia
-   using QuantitativeSusceptibilityMappingTGV
+using QuantitativeSusceptibilityMappingTGV
 
-    sz = (20, 20, 20)
-    res = [1, 1, 1]
-    TE = 0.01
+sz = (20, 20, 20)
+res = [1, 1, 1]
+TE = 0.01
 
-    phase = randn(sz)
-    mask = trues(sz)
+phase = randn(sz)
+mask = trues(sz)
 
-    chi = qsm_tgv(phase, mask, res; TE)
+chi = qsm_tgv(phase, mask, res; TE)
 ```