diff --git a/.gitignore b/.gitignore index 46df245e..66c26b04 100644 --- a/.gitignore +++ b/.gitignore @@ -16,4 +16,7 @@ dist/ .pytest_cache/ _.py _.ipynb -.idea \ No newline at end of file +.idea + +# sphinx documentation +docs/_build/* \ No newline at end of file diff --git a/docs/diffusion.rst b/docs/diffusion.rst index a143ed99..ba95a7d6 100644 --- a/docs/diffusion.rst +++ b/docs/diffusion.rst @@ -30,7 +30,7 @@ Output Summary Table * - Tractography - tensor field - fiber tracts - - + - * - Graph Generation - fiber tracts, parcellations - connectome @@ -40,7 +40,7 @@ Output Summary Table Output subject-level pipeline summary ===================================== -.. figure:: ../_static/ndmg-d-detailed-pipeline.jpg +.. figure:: ./_static/ndmg-d-detailed-pipeline.jpg :align: left :figwidth: 700px @@ -58,7 +58,7 @@ The transform mapping the T1w volume to the template is then applied to the dMRI Finally, `NDMG` produces a QA plot showing three slices of the first BO volume of the aligned dMRI image overlaid on the MNI152 template in the three principle coordinate planes (Figure S2). -.. figure:: ../_static/registration-qa.png +.. figure:: ./_static/registration-qa.png :align: left :figwidth: 700px @@ -74,7 +74,7 @@ Once the dMRI volumes have been aligned to the template, `NDMG` begins diffusion While high-dimensional diffusion models, such as orientation distribution functions (ODFs) or q-ball, enable reconstruction of crossing fibers and complex fiber trajectories, these methods are designed for images with a large number of diffusion volumes/directions for a given image. Because `NDMG` is designed to be robust across a wide range of dMRI studies, including diffusion tensor imaging, `NDMG` uses a lower-dimensional tensor model. The model, described in detail on DiPy_'s website, computes a 6-component tensor for each voxel in the image. This reduces the dMRI image stack to a single 6-dimensional image that can be used for tractography. `NDMG` generates a QA plot showing slices of the FA map derived from the tensors in nine panels, as below (Figure S3) -.. figure:: ../_static/tensor-qa.png +.. figure:: ./_static/tensor-qa.png :align: left :figwidth: 700px diff --git a/docs/index.rst b/docs/index.rst index 2f1f9874..4f56527a 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -9,7 +9,7 @@ Overview of m2g NeuroData’s MR Graphs package, `m2g`, is the successor of the MRCAP, MIGRAINE, and m2g pipelines. m2g combines dMRI, fMRI, and sMRI data from a single subject to estimate a high-level connectome reliably and scalably. -The m2g pipeline has been developed as a one-click solution for human connectome estimation by providing robust and reliable estimates of connectivity across a wide range of datasets. The pipelines are explained and derivatives analyzed in our pre-print, available on BiorXiv_. +The m2g pipeline has been developed as a one-click solution for human connectome estimation by providing robust and reliable estimates of connectivity across a wide range of datasets. The pipelines are explained and derivatives analyzed in our pre-print, available on BioRxiv_. 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