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‎joss/nt.bib

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@@ -49,3 +49,27 @@ @article{jenkinson_fsl_2012
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volume = 62,
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year = 2012
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}
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@article{avants_symmetric_2008,
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author = {Avants, B.B. and Epstein, C.L. and Grossman, M. and Gee, J.C.},
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doi = {10.1016/j.media.2007.06.004},
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issn = {1361-8415},
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journal = {Medical Image Analysis},
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number = 1,
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pages = {26-41},
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shorttitle = {Symmetric diffeomorphic image registration with cross-correlation},
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title = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain},
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url = {http://www.sciencedirect.com/science/article/pii/S1361841507000606},
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volume = 12,
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year = 2008
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}
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@book{friston_statistical_2006,
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address = {London},
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author = {Friston, Karl J. and Ashburner, John and Kiebel, Stefan J. and Nichols, Thomas E. and Penny, William D.},
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isbn = {978-0-12-372560-8},
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publisher = {Academic Press},
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shorttitle = {Statistical parametric mapping},
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title = {Statistical parametric mapping : the analysis of functional brain images},
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year = 2006
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}

‎joss/paper.md

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---
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title: 'Nitransforms: A Python tool to read, represent, manipulate, and apply $n$-dimensional spatial transforms'
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title: 'NiTransforms: A Python tool to read, represent, manipulate, and apply $n$-dimensional spatial transforms'
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tags:
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- Python
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- neuroimaging
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# Summary
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Spatial transforms formalize mappings between coordinates of objects in
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biomedical images. Transforms typically are the outcome of image registration
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methodologies, which estimate the alignment between two images. Image
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registration is a prominent task present in image processing. In neuroimaging,
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the proliferation of image registration software implementations has resulted
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in a disparate collection of structures and file formats used to preserve and
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communicate the transformation. This assortment of formats presents the
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challenge of compatibility between tools and endangers the reproducibility of
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results.
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Spatial transforms formalize mappings between coordinates of objects in biomedical images.
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Transforms typically are the outcome of image registration methodologies, which estimate the alignment between two images.
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Image registration is a prominent task present in image processing.
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In neuroimaging, the proliferation of image registration software implementations has resulted in a disparate collection of structures and file formats used to preserve and communicate the transformation.
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This assortment of formats presents the challenge of compatibility between tools and endangers the reproducibility of results.
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`nitransforms` is a Python tool capable of reading and writing tranforms
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produced by the most popular neuroimaging software (AFNI [-@cox_software_1997],
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FSL [-@jenkinson_fsl_2012], FreeSurfer [-@fischl_freesurfer_2012], ITK, and SPM).
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Additionally, the tool provides seamless conversion between these formats, as
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well as the ability of applying the transforms to other images. `nitransforms`
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is inspired by `NiBabel` [@brett_nibabel_2006], a Python package with a
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collection of tools to read, write and handle neuroimaging data, and will be
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included as a new module.
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_NiTransforms_ is a Python tool capable of reading and writing tranforms produced by the most popular neuroimaging software (AFNI [@cox_software_1997], FSL [@jenkinson_fsl_2012], FreeSurfer [@fischl_freesurfer_2012], ITK via ANTs [@avants_symmetric_2008], and SPM [@friston_statistical_2006]).
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Additionally, the tool provides seamless conversion between these formats, as well as the ability of applying the transforms to other images.
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_NiTransforms_ is inspired by `NiBabel` [@brett_nibabel_2006], a Python package with a collection of tools to read, write and handle neuroimaging data, and will be included as a new module.
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# Spatial transforms
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# Software Architecture
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Let $\vec{x}$ represent the coordinates of a point in the reference coordinate system $R$, and $\vec{x}'$ its projection on to another coordinate system $M$:
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There are four main components within the tool: an `io` submodule to handle
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the structure of the various file formats, a `base` submodule where abstract
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classes are defined, a `linear` submodule implementing $n$-dimensional linear
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transforms, and a `nonlinear` submodule for both parametric and non-parametric
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nonlinear transforms. Furthermore, `nitranforms` provides a straightforward
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API (Application Programming Interface) that allows researchers to map point
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sets via transforms, as well as apply transforms (i.e., mapping the coordinates
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and interpolating the data) to data structures with ease.
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$T\colon R \subset \mathbb{R}^n \to M \subset \mathbb{R}^n$
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To ensure the consistency and uniformity of internal operations, all transforms
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are defined using a left-handed coordinate system of physical coordinates. In
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words from the neuroimaging domain, the coordinate system of transforms is
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_RAS+_ (or positive directions point to the Righthand for the first axis,
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Anterior for the second, and Superior for the third axis). The internal
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representation of transform coordinates is the most relevant design decision,
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and implies that a conversion of coordinate system is necessary to correctly
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interpret transforms generated by other software. When a transform that is
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defined in another coordinate system is loaded, it is automatically converted
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into _RAS+_ space.
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$\vec{x} \mapsto \vec{x}' = f(\vec{x}).$
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`nitransforms` was developed using a test-driven development paradigm, with the
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battery of tests being written prior to the software implementations. Two
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categories of tests were used: unit tests and cross-tool comparison tests. Unit
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tests evaluate the formal correctness of the implementation, while cross-tool
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comparison tests assess the correct implementation of third-party software.
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The testing suite is incorporated into a continuous integration framework, which
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assesses the continuity of the implementation along the development life and
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ensures that code changes and additions do not break existing functionalities.
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In an image registration problem, $M$ is a moving image from which we want to sample data in order to bring the image into spatial alignment with the reference image $R$.
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Hence, $f$ here is the spatial transformation function that maps from coordinates in $R$ to coordinates in $M$.
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There are a multiplicity of image registration algorithms and corresponding image transformation models to estimate linear and nonlinear transforms.
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# Spatial transforms
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The problem has been traditionally confused by the need of _transforming_ or mapping one image (generally referred to as _moving_) into another that serves as reference, with the goal of _fusing_ the information from both.
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An example of image fusion application would be the alignment of functional data from one individual's brain to the same individual's corresponding anatomical MRI scan for visualization.
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Therefore, "applying a transform" entails two operations: first, transforming the coordinates of the samples in the reference image $R$ to find their mapping $\vec{x}'$ on $M$ via $T\{\cdot\}$, and second an interpolation step as $\vec{x}'$ will likely fall off-the-grid of the moving image $M$.
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These two operations are confusing because, while the spatial transformation projects from $R$ to $M$, the data flows in reversed way after the interpolation of the values of $M$ at the mapped coordinates $\vec{x}'$.
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Let $\vec{x}$ represent the coordinates of a point in the reference coordinate
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system $R$, and $\vec{x}'$ its projection on to another coordinate system $M$:
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# Software Architecture
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$T\colon R \subset \mathbb{R}^n \to M \subset \mathbb{R}^n$
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There are four main components within the tool: an `io` submodule to handle the structure of the various file formats, a `base` submodule where abstract classes are defined, a `linear` submodule implementing $n$-dimensional linear transforms, and a `nonlinear` submodule for both parametric and non-parametric nonlinear transforms.
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Furthermore, _NiTranforms_ provides a straightforward _Application Programming Interface_ (API) that allows researchers to map point sets via transforms, as well as apply transforms (i.e., mapping the coordinates and interpolating the data) to data structures with ease.
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$\vec{x} \mapsto \vec{x}' = f(\vec{x}).$
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To ensure the consistency and uniformity of internal operations, all transforms are defined using a left-handed coordinate system of physical coordinates.
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In words from the neuroimaging domain, the coordinate system of transforms is _RAS+_ (or positive directions point to the Righthand for the first axis, Anterior for the second, and Superior for the third axis).
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The internal representation of transform coordinates is the most relevant design decision, and implies that a conversion of coordinate system is necessary to correctly interpret transforms generated by other software.
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When a transform that is defined in another coordinate system is loaded, it is automatically converted into _RAS+_ space.
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In an image registration problem, $M$ is a moving image from which we want to
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sample data in order to bring the image into spatial alignment with the
100-
reference image $R$. Hence, $f$ here is the spatial transformation function
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that maps from coordinates in $R$ to coordinates in $M$. There are a
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multiplicity of image registration algorithms and corresponding image
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transformation models to estimate linear and nonlinear transforms.
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_NiTransforms_ was developed using a test-driven development paradigm, with the
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battery of tests being written prior to the software implementations.
77+
Two categories of tests were used: unit tests and cross-tool comparison tests.
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Unit tests evaluate the formal correctness of the implementation, while cross-tool
79+
comparison tests assess the correct implementation of third-party software.
80+
The testing suite is incorporated into a continuous integration framework, which assesses the continuity of the implementation along the development life and ensures that code changes and additions do not break existing functionalities.
10481

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The problem has been traditionally confused by the need of _transforming_ or
106-
mapping one image (generally referred to as _moving_) into another that serves
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as reference, with the goal of _fusing_ the information from both. An example of
108-
image fusion application would be the alignment of functional data from one
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individual's brain to the same individual's corresponding anatomical MRI scan
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for visualization. Therefore, "applying a transform" entails two operations:
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first, transforming the coordinates of the samples in the reference image $R$ to
112-
find their mapping $\vec{x}'$ on $M$ via $T\{\cdot\}$, and second, an
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interpolation step as $\vec{x}'$ will likely fall off-the-grid of the moving
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image $M$. These two operations are confusing because, while the spatial
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transformation projects from $R$ to $M$, the data flows in reversed way after the
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interpolation of the values of $M$ at the mapped coordinates $\vec{x}'$.
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# Examples
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# References

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