--- a
+++ b/docs/source/usage.rst
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+.. _usage_cli:
+
+Application
+================================================================================
+This page discusses general usage tips for both the command line and graphical user interface (GUI).
+Because the GUI is a wrapper around the command line and is typically easier to use, we will focus on detailing how to
+use DOSMA from the command line.
+
+The details here assume that :ref:`installation <installation>` and :ref:`verification <install-verification>`
+worked as expected.
+
+.. _usage-session:
+
+Starting A Session
+--------------------------------------------------------------------------------
+To being a session using DOSMA from the command-line or GUI, follow the step below:
+
+1. Open new Terminal window.
+2. Activate DOSMA Anaconda environment::
+
+    $ conda activate dosma_env
+
+3. Run DOSMA::
+
+    # Run DOSMA from command line.
+    $ python -m dosma.cli <ARGS...>
+
+    # Or run DOSMA as a user interface (UI).
+    $ python -m dosma.app
+
+.. note::
+
+    If you are using a remote connection, enable X11 port-forwarding to execute Step 4. If it is not enabled, the GUI
+    cannot be used for remote connections.
+
+
+.. _usage-cli:
+
+Command-Line (Terminal)
+--------------------------------------------------------------------------------
+DOSMA was designed to be a script-friendly library, where wrapper scripts can be used to
+execute and parallelize DOSMA computations. As scripting is often done using command-line enabled scripts (bash, shell,
+perl, etc.), DOSMA has a command-line interface.
+
+The DOSMA command-line parser hierarchy funnels from scan type to action. Scan types are specified first and are
+followed by the action to execute and corresponding arguments. For example, the code below computes |T2| for femoral
+cartilage (fc) and patellar cartilage (pc) from qDESS scans::
+
+    $ python -m dosma.cli --dicom subject01/dicoms/qdess --save subject01/data qdess --fc --pc generate_t2_map
+
+Scans
+^^^^^
+:ref:`Scans <scans>` and corresponding actions are specified as subparsers. For example, the code below is the skeleton
+for segmenting tissues in qDESS scans::
+
+    $ python -m dosma.cli qdess segment ....
+
+
+Tissues
+^^^^^^^
+:ref:`Tissues <tissues>` are specified as arguments of the subparser using the tissue's abbreviated name.
+For example, ``... qdess --fc ...`` would be performing a qDESS-specific action on femoral cartilage.
+
+To analyze multiple tissues, add additional flags. For example, the argument below
+generates a |T2| map for both femoral and patellar cartilage::
+
+    $ python -m dosma.cli --dicom subject01/dicoms/qdess --save subject01/data qdess --fc --pc generate_t2_map
+
+Analysis
+^^^^^^^^
+DOSMA also has a command-line subparser for creating visualizations and analyze quantitative values for tissues.
+This subparser is identified by the anatomical region being analyzed.
+
+DOSMA currently supports knee-related analyses. To run the analysis on |T2| for femoral cartilage and tibial cartilage, use the following skeleton::
+
+    $ python -m dosma.cli --load subject01/data knee --fc --tc --t2
+
+Help
+^^^^
+To get more information, start a new session and run ``python -m dosma.cli --help``. To get the help menu of subparsers, add the
+subparser name to the help menu::
+
+    # Print high-level help menu for DOSMA.
+    $ python -m dosma.cli --help
+
+    # Print help menu for qDESS scan.
+    $ python -m dosma.cli qdess --help
+
+    # Print help menu for qDESS segmentation.
+    $ python -m dosma.cli qdess segment --help
+
+Examples
+^^^^^^^^
+We detail some examples that could be useful for analyzing data. Note there any any number of combinations with how the
+data is analyzed. Below are just examples for how they have commonly been used by current users.
+
+We assume the folder structure looks something like below:
+
+::
+
+    research_data
+        ├── patient01
+            ├── qdess (qDESS dicoms)
+            |    └── I0001.dcm
+            |    └── I0002.dcm
+            |    └── I0003.dcm
+            |    ....
+            ├── cubequant (CubeQuant dicoms)
+            ├── cones (UTE Cones dicoms)
+        ├── patient02
+            ├── mapss (MAPSS dicoms)
+        ├── patient03
+        ├── weights (segmentation weights)
+            ├── oai_unet2d
+            ├── iwoai-2019-t6
+            ├── iwoai-2019-t6-normalized
+
+
+
+qDESS
+#####
+Analyze patient01's femoral cartilage |T2| properties using qDESS sequence*::
+
+    # 1. Calculate 3D T2 map - suppress fat and fluid to reduce noise
+    $ python -m dosma.cli --dicom research_data/patient01/dess --save research_data/patient01/data qdess --fc t2 --suppress_fat --suppress_fluid
+
+    # 2. Segment femoral cartilage on root sum of squares (RSS) of two echo qDESS echos using OAI 2D U-Net model.
+    $ python -m dosma.cli --dicom research_data/patient01/dess --save research_data/patient01/data qdess --fc segment --rss --weights_dir weights/oai_unet2d --model oai-unet2d
+
+    # 3. Calculate/visualize T2 for femoral cartilage
+    $ python -m dosma.cli --load research_data/patient01/data --save research_data/patient01/data knee --fc --t2
+
+
+CubeQuant
+#########
+Analyze patient01 femoral cartilage |T1rho| properties using Cubequant sequence::
+
+    # 1. Register cubequant volumes (i.e. different spin-lock-times) to one another (intraregistration)
+    $ python -m dosma.cli --dicom research_data/patient01/cubequant --save research_data/patient01/data cubequant intraregister
+
+    # 2. Register cubequant volume to first echo of qDESS sequence
+    $ python -m dosma.cli --load research_data/patient01/data cubequant --fc interregister --target_path research_data/patient01/data/dess/echo1.nii.gz --target_mask research_data/patient01/data/fc/fc.nii.gz
+
+    # 3. Calculate 3D T1-rho map only for femoral cartilage region
+    $ python -m dosma.cli --load research_data/patient01/data cubequant --fc t1_rho  --mask_path research_data/patient01/data/fc/fc.nii.gz
+
+    # 4. Calculate/visualize T1-rho for femoral cartilage
+    $ python -m dosma.cli --load research_data/patient01/data --fc --t1_rho
+
+
+UTE Cones
+#########
+Analyze patient01 femoral cartilage |T2star| properties using UTE Cones sequence::
+
+    # 1. Register cones volume to first echo of qDESS sequence
+    $ python -m dosma.cli --dicom research_data/patient01/cones --save research_data/patient01/data cones --fc interregister --target_path research_data/patient01/data/dess/echo1.nii.gz --target_mask research_data/patient01/data/fc/fc.nii.gz
+
+    # 2. Calculate 3D T2-star map only for femoral cartilage region
+    $ python -m dosma.cli --load research_data/patient01/data cones --fc t2_star --mask_path research_data/patient01/data/fc/fc.nii.gz
+
+    # 3. Calculate/visualize T1-rho for femoral cartilage
+    $ python -m dosma.cli --load research_data/patient01/data knee --fc --t2_star
+
+
+MAPSS
+#####
+Analyze patient02 femoral cartilage |T1rho| and |T2| properties using MAPSS sequence::
+
+    # 1. Fit T1-rho for whole volume
+    $ python -m dosma.cli --dicom research_data/patient02/mapss --save research_data/patient02/data mapss --fc t1_rho
+
+    # 2. Fit T2 for whole volume
+    $ python -m dosma.cli --dicom research_data/patient02/mapss --save research_data/patient02/data mapss --fc t2
+
+    # 3. Manually segment femoral cartilage and store in appropriate folders.
+
+    # 4. Calculate/visualize T1-rho and T2 for femoral cartilage
+    $ python -m dosma.cli --load research_data/patient01/data knee --fc --t2_star
+
+
+.. Substitutions
+.. |T2| replace:: T\ :sub:`2`
+.. |T1| replace:: T\ :sub:`1`
+.. |T1rho| replace:: T\ :sub:`1`:math:`{\rho}`
+.. |T2star| replace:: T\ :sub:`2`:sup:`*`