DOC: Improve documentation at Getting Started section

docs/getting_started.md

@@ -27,54 +27,77 @@ The example above creates an `asldata.ASLData` object, called `data`, which will
 
 The following examples can represents some situations on ASL data processing:
 
-1. Load a pCASL and M0 data for basic CBF/ATT mapping
+1. Load a pCASL and M0 data and reconstruct a basic CBF/ATT mapping
 
 ```python
 from asltk.asldata import ASLData
+from asltk.reconstruction import CBFMapping
 
+data = ASLData(
+    pcasl='./tests/files/pcasl_mte.nii.gz',
+    m0='path/to/m0.nii.gz',
+    ld_values=[100.0, 150.0, 250.0],
+    pld_values=[100.0, 120.0, 150.0],
+    te_values=[12.5, 36.7, 85.5])
+
+cbf = CBFMapping(data) # insert the ASLData in the CBFMapping instance
+out = cbf.create_map() # Effectivelly creates the CBF/ATT maps
 
+out.get('cbf') # Get the CBF map image
+out.get('att') # Get the ATT map image
 ```
 
+!!! info
+    This example uses a multiTE-ASL data to reconstruct the CBF/ATT maps. However, this is only a simple representation. The same output can be found using a simple pCASL aquisition without the TE values being acquired. In this case, the `te_values` passed to the `ASLDATA` constructor is not necessary.
+
 2.  Load a pCASL and M0 data for multiTE-ASL processing obtaingin the T1 exchange from blood to Grey Matter mapping
 
 ```python
 from asltk.asldata import ASLData
+from asltk.reconstruction import CBFMapping
+
+data = ASLData(
+    pcasl='./tests/files/pcasl_mte.nii.gz',
+    m0='path/to/m0.nii.gz',
+    ld_values=[100.0, 150.0, 250.0],
+    pld_values=[100.0, 120.0, 150.0],
+    te_values=[12.5, 36.7, 85.5])
 
+cbf = CBFMapping(data) # insert the ASLData in the CBFMapping instance
+out = cbf.create_map() # Effectivelly creates the CBF/ATT maps
 
+out.get('cbf') # Get the CBF map image
+out.get('att') # Get the ATT map image
 ```
 
+!!! note
+    By default, all the `reconstruction` classes uses a multiprocessing strategy for CPU threads. This will accelerate the mapping creation. As a internal standard, all the available CPU cores in the machine is recruited. If there is a limitation to your machine/experiment that is not allowed such quantity of CPUs, then a lower value can be set using the `cores` parameter in the `create_map` method.
 
 ## Load and process an image
 
 As as standard notation, the `asltk` library assumes that all the image data files are storaged and manipulated using `numpy` objects. Therefore, the following code snippets can represents the `load_image` and `save_image` pattern adopted in the tool:
 
-1. Loading and image
+1. Loading and saving an image
 
 ```python
-from asltk import utils
+from asltk.utils import load_image, save_image
 
-img = utils.load_image('path/to/pcasl.nii.gz')
+img = load_image('path/to/pcasl.nii.gz') #Loading an image
 type(img)
 < numpy.ndarray >
+
+save_image(img, 'path/to/save/the/image.nrrd') # Saving the image using NRRD file format
+
 ```
 
 !!! warning
-    The `asltk` uses the `SimpleITK` library to load and save images due to it's long list of image format options, e.g. NifTI, Nrrd, MHA, etc. However, in order to transpose to `numpy.array` data format for image processing, it is important to note that the image space rasteting format relies as follows:
+    The `asltk` uses the `SimpleITK` library to load and save images due to it's long list of image format options, e.g. NifTI, NRRD, MHA, etc. However, in order to transpose to `numpy.array` data format for image processing, it is important to note that the image space rasteting format relies as follows:
     ```
     SimpleITK -> (x,y,z,...,n_d)
     Numpy -> (n_d, ..., z,y,x)
     ```
     Where the `n_d` represents a higher order dimension in the data.
 
-2.  Saving and image
-
-```python
-from asltk import utils
-
-img = utils.load_image('path/to/pcasl.nii.gz')
-utils.save_image(img, 'path/to/save/image.nii.gz')
-```
-
 Basically, one can use out tool for the general pipeline:
 
 * Loading a data and metadata,