DOC: reviewed more text highlights [skip ci]

documentation/bids.rst

@@ -5,7 +5,7 @@
 BIDS and BIDS App standards
 *******************************************
 
-``MIALSRTK BIDS App`` adopts the :abbr:`BIDS (Brain Imaging Data Structure)` standard for data organization and is developed following the BIDS App standard. This means that ``MIALSRTK BIDS App`` handles dataset formatted following the BIDS App standard and provides a processing workflow containerized in Docker container image (promoting portability and reproduciblity) that can be run with a set of arguments defined by the BIDS App standard directly from the terminal or a script (See :ref:`cmdusage` section for more details). 
+`MIALSRTK BIDS App` adopts the :abbr:`BIDS (Brain Imaging Data Structure)` standard for data organization and is developed following the BIDS App standard. This means that `MIALSRTK BIDS App` handles dataset formatted following the BIDS App standard and provides a processing workflow containerized in Docker container image (promoting portability and reproduciblity) that can be run with a set of arguments defined by the BIDS App standard directly from the terminal or a script (See :ref:`cmdusage` section for more details). 
 
 For more information about BIDS and BIDS-Apps, please consult the `BIDS Website <https://bids.neuroimaging.io/>`_, the `Online BIDS Specifications <https://bids-specification.readthedocs.io/en/stable/>`_, and the `BIDSApps Website <https://bids-apps.neuroimaging.io/>`_. `HeuDiConv <https://github.com/nipy/heudiconv>`_ can assist you in converting DICOM brain imaging data to BIDS. A nice tutorial can be found @ `BIDS Tutorial Series: HeuDiConv Walkthrough <http://reproducibility.stanford.edu/bids-tutorial-series-part-2a/>`_ .
 
@@ -46,7 +46,7 @@ The BIDS App accepts BIDS datasets that adopt the following organization, naming
         code/
             participants_params.json
 
-where ``participants_params.json`` is the MIALSRTK BIDS App configuration file, which following a specific schema (See :ref:`config schema <config>`), and which defines multiple processing parameters (such as the ordered list of scans or the weight of regularization).
+where ``participants_params.json`` is the MIALSRTK BIDS App configuration file, which follows a specific schema (See :ref:`config schema <config>`), and which defines multiple processing parameters (such as the ordered list of scans or the weight of regularization).
 
 .. important:: 
     Before using any BIDS App, we highly recommend you to validate your BIDS structured dataset with the free, online `BIDS Validator <http://bids-standard.github.io/bids-validator/>`_.

documentation/contributing.rst

@@ -95,7 +95,7 @@ Before you submit a pull request, check that it meets these guidelines:
 How to build the BIDS App locally
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-1. Go to the clone directory of your fork and run the script ``build_bidsapp.sh`` ::
+1. Go to the clone directory of your fork and run the script `build_bidsapp.sh` ::
 
     cd mialsuperresolutiontoolkit
     sh build_bidsapp.sh
@@ -107,27 +107,27 @@ Note that the tag of the version of the image will be extracted from ``pymialsrt
 How to build the documentation locally
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-1. Install the MIALSRTK conda environment ``pymialsrtk-env`` with sphinx and all extensions to generate the documentation::
+1. Install the `MIALSRTK` conda environment `pymialsrtk-env` with sphinx and all extensions to generate the documentation::
 
     cd mialsuperresolutiontoolkit
     conda env create -f docker/bidsapp/environment.yml
 
-2. Activate the MIALSRTK conda environment ``pymialsrtk-env`` and install ``pymialsrtk`` ::
+2. Activate the MIALSRTK conda environment `pymialsrtk-env` and install `pymialsrtk` ::
 
     conda activate pymialsrtk-env
     python setup.py install
 
-3. Run the script ``build_sphinx_docs.sh`` to generate the HTML documentation in ``documentation/_build/html``::
+3. Run the script `build_sphinx_docs.sh` to generate the HTML documentation in ``documentation/_build/html``::
 
     bash build_sphinx_docs.sh
 
 .. note::
-	Make sure to have activated the conda environment ``pymialsrtk-env`` before running the script ``build_sphinx_docs.sh``.
+	Make sure to have activated the conda environment `pymialsrtk-env` before running the script `build_sphinx_docs.sh`.
 
 Not listed as a contributor?
 ----------------------------
 
-This is easy, MIALSRTK has the `all contributors bot <https://allcontributors.org/docs/en/bot/usage>`_ installed.
+This is easy, `MIALSRTK` has the `all contributors bot <https://allcontributors.org/docs/en/bot/usage>`_ installed.
 
 Just comment on Issue or Pull Request (PR), asking `@all-contributors` to add you as contributor::
 

documentation/index.rst

@@ -45,9 +45,9 @@ Introduction
 
 The `Medical Image Analysis Laboratory Super-Resolution ToolKit (MIALSRTK)` consists of a set of C++ and Python3 image processing and worflow tools necessary to perform motion-robust super-resolution fetal MRI reconstruction. 
 
-The *original* `C++ MIALSRTK library` includes all algorithms and methods for brain extraction, intensity standardization, motion estimation and super-resolution. It uses the CMake build system and depends on the open-source image processing Insight ToolKit (ITK) library, the command line parser TCLAP library and OpenMP for multi-threading. 
+The *original* `C++ MIALSRTK` library includes all algorithms and methods for brain extraction, intensity standardization, motion estimation and super-resolution. It uses the CMake build system and depends on the open-source image processing Insight ToolKit (ITK) library, the command line parser TCLAP library and OpenMP for multi-threading. 
 
-MIALSRTK has been extended with the `pymialsrtk` Python library following recent advances in standardization of neuroimaging data organization and processing workflows (See :ref:`BIDS and BIDS App standards <cmpbids>`). This library has a modular architecture built on top of the Nipype dataflow library which consists of (1) processing nodes that interface with each of the MIALSRTK C++ tools and (2) a processing pipeline that links the interfaces in a common workflow. 
+`MIALSRTK` has been extended with the `pymialsrtk` Python3 library following recent advances in standardization of neuroimaging data organization and processing workflows (See :ref:`BIDS and BIDS App standards <cmpbids>`). This library has a modular architecture built on top of the Nipype dataflow library which consists of (1) processing nodes that interface with each of the MIALSRTK C++ tools and (2) a processing pipeline that links the interfaces in a common workflow. 
 
 The processing pipeline with all dependencies including the C++ MIALSRTK tools are encapsulated in a Docker image container, which is now distributed as a `BIDS App` which handles datasets organized following the BIDS standard. See :ref:`BIDS App usage <cmdusage>` for more details.
 
@@ -56,7 +56,7 @@ All these design considerations allow us not only to (1) represent the entire pr
 Aknowledgment
 --------------
 
-If your are using the MIALSRTK BIDS App in your work, please acknowledge this software and its dependencies. See :ref:`Citing <citing>` for more details.
+If your are using `MIALSRTK` in your work, please acknowledge this software and its dependencies. See :ref:`Citing <citing>` for more details.
 
 License information
 --------------------

documentation/outputs.rst

@@ -25,7 +25,7 @@ BIDS derivatives entities
 
 See `Original BIDS Entities Appendix <https://bids-specification.readthedocs.io/en/v1.4.1/99-appendices/09-entities.html>`_ for more description.
 
-.. note:: A new entity ``id-<label>`` has been introduced to distinguish between outputs when the pipeline is run with multiple configurations (such a new order of scans) on the same subject.
+.. note:: A new entity `id-<label>` has been introduced to distinguish between outputs when the pipeline is run with multiple configurations (such a new order of scans) on the same subject.
 
 Main MIALSRTK BIDS App Derivatives
 ==========================================
@@ -77,7 +77,7 @@ Anatomical derivatives
 Nipype Workflow Derivatives
 ==========================================
 
-The execution of the Nipype workflow (pipeline) involves the creation of a number of intermediate outputs for each subject ``sub-<label>`` and each run ``rec-<id_label>`` which are written to ``<bids_dataset/derivatives>/nipype/sub-<label>/rec-<id_label>/srr_pipeline`` where ``<id_label>`` corresponds to the label used previously for the entity ``id-<label>``: 
+The execution of the Nipype workflow (pipeline) involves the creation of a number of intermediate outputs for each subject `sub-<label>` and each run `rec-<id_label>` which are written to ``<bids_dataset/derivatives>/nipype/sub-<label>/rec-<id_label>/srr_pipeline`` where `<id_label>` corresponds to the label used previously for the entity `id-<label>`: 
 
 .. image:: images/nipype_wf_derivatives.png
     :width: 600