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+.. _introduction:
+
+**This guide is still under construction**
+
+Introduction
+================================================================================
+DOSMA is an open-source Python library and application for medical image analysis.
+
+DOSMA is designed to streamline medical image analysis by standardizing medical image
+I/O, simplifying array-like operations on medical images, and deploying state-of-the-art
+image analysis algorithms. Because DOSMA is a framework, it is built to be flexible enough
+to write analysis protocols that can be run for different imaging modalities and scan sequences.
+
+For example, we can build the analysis workflow for a combination
+of quantitative DESS, CubeQuant (3D fast spin echo), and ultra-short echo time Cones scans for multiple patients
+(shown below) can be done in 7 lines of code:
+
+.. figure:: figures/workflow.png
+   :align: center
+   :alt: Example workflow for analyzing multiple scans per patient
+   :figclass: align-center
+
+   Example quantitative knee MRI workflow for analyzing 1. quantitative DESS (qDESS),
+   a |T2|-weighted sequence, 2. CubeQuant, a |T1rho|-weighted sequence, and 3. ultra-short echo
+   time (UTE) Cones, a |T2star| weighted sequence.
+
+Workflow
+--------------------------------------------------------------------------------
+DOSMA uses various modules to handle MSK analysis for multiple scan types and tissues:
+
+- **Scan** modules declare scan-specific actions (fitting, segmentation, registration, etc).
+- **Tissue** modules handle visualization and analysis optimized for different tissues.
+- **Analysis** modules abstract different methods for performing different actions (different segmentation methods, fitting methods, etc.)
+
+**Note**: DOSMA is still in beta, and APIs are subject to change.
+
+Features
+--------------------------------------------------------------------------------
+
+Dynamic Input/Output (I/O)
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+Reading and writing medical images relies on standardized data formats.
+The Digital Imaging and Communications in Medicine (DICOM) format has been the international
+standard for medical image I/O. However, header information is memory intensive and
+and may not be useful in cases where only volume information is desired.
+
+The Neuroimaging Informatics Technology Initiative (NIfTI) format is useful in these cases.
+It stores only volume-specific header information (rotation, position, resolution, etc.) with
+the volume.
+
+DOSMA supports the use of both formats. However, because NIfTI headers do not contain relevant scan
+information, it is not possible to perform quantitative analysis that require this information.
+Therefore, we recommend using DICOM inputs, which is the standard output of acquisition systems,
+when starting processing with DOSMA.
+
+By default,  volumes (segmentations, quantitative maps, etc.) are written in the NIfTI format.
+The default output file format can be changed in the :ref:`preferences <faq-citation>`.
+
+Array-Like Medical Images
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Medical images are spatially-aware pixel arrays with metadata. DOSMA supports array-like
+operations (arithmetic, slicing, etc.) on medical images while preserving spatial attributes and
+accompanying metadata with the :class:`MedicalVolume` data structure. It also supports intelligent
+reformatting, fast low-level computations, and native GPU support.
+
+
+Disclaimers
+--------------------------------------------------------------------------------
+
+Using Deep Learning
+^^^^^^^^^^^^^^^^^^^
+All weights/parameters trained for any task are likely to be most closely correlated to data used for training.
+If scans from a particular sequence were used for training, the performance of those weights are likely optimized
+for that specific scan prescription (resolution, TR/TE, etc.). As a result, they may not perform as well on segmenting images
+acquired using different scan types.
+
+If you do train weights for any deep learning task that you would want to include as part of this repo, please provide
+a link to those weights and detail the scanning parameters/sequence used to acquire those images.
+
+.. Substitutions
+.. |T2| replace:: T\ :sub:`2`
+.. |T1| replace:: T\ :sub:`1`
+.. |T1rho| replace:: T\ :sub:`1`:math:`{\rho}`
+.. |T2star| replace:: T\ :sub:`2`:sup:`*`