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<h1 class="title toc-ignore">TractoR: Magnetic Resonance Imaging and Tractography with R</h1>
<h4 class="author"><em>Jonathan D. Clayden, Susana Muñoz Maniega, Amos J. Storkey, Martin D. King, Mark E. Bastin & Chris A. Clark</em></h4>
<h4 class="date"><em>2016-05-18</em></h4>
<p><em>Please note that this is an updated version of the <a href="http://www.jstatsoft.org/v44/i08/">original TractoR paper</a>, which was published in the</em> Journal of Statistical Software <em>in 2011. Please cite the reference below if you use TractoR in your work.</em></p>
<p><em>J.D. Clayden, S. Muñoz Maniega, A.J. Storkey, M.D. King, M.E. Bastin & C.A. Clark (2011). TractoR: Magnetic resonance imaging and tractography with R.</em> Journal of Statistical Software <strong>44</strong><em>(8):1-18.</em></p>
<p><em>This version of the paper is based on v3.0.1 of TractoR, but does not cover the full scope of this more recent release. (The current version at the time of original publication was v1.8.2.) Please see <a href="http://www.tractor-mri.org.uk" class="uri">http://www.tractor-mri.org.uk</a> for more information about the package and its authors.</em></p>
<div id="abstract" class="section level2">
<h2>Abstract</h2>
<p>Statistical techniques play a major role in contemporary methods for analysing magnetic resonance imaging (MRI) data. In addition to the central role that classical statistical methods play in research using MRI, statistical modelling and machine learning techniques are key to many modern data analysis pipelines. Applications for these techniques cover a broad spectrum of research, including many preclinical and clinical studies, and in some cases these methods are working their way into widespread routine use.</p>
<p>In this manuscript we describe a software tool called TractoR (for “Tractography with R”), a collection of packages for the R language and environment, along with additional infrastructure for straightforwardly performing common image processing tasks. TractoR provides general purpose functions for reading, writing and manipulating MR images, as well as more specific code for fitting signal models to diffusion MRI data and performing tractography, a technique for visualising neural connectivity.</p>
</div>
<div id="introduction" class="section level2">
<h2>Introduction</h2>
<p>Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique which is used routinely in hospitals worldwide. By exploiting fundamentally quantum-mechanical characteristics of water molecules in the presence of a strong magnetic field, MRI can recover detailed images of body tissues at a resolution of around 1 mm. Unlike many other medical imaging methods, MRI uses no ionising radiation, and therefore permits repeated scanning.</p>
<p>MRI is also a very flexible technique. By manipulating the nuclear magnetic resonance signal from water molecules, a broad variety of tissue contrasts can be obtained. Of particular interest in this paper is diffusion MRI (dMRI), wherein image intensity at each pixel (or voxel for 3D images) depends on the local pattern of self-diffusion of water (<a href="#bibliography">Le Bihan, 2003</a>). Since elements of body tissue such as cell membranes present obstacles to diffusion, these images can provide information about microscopic tissue structure; and the orientation of highly linear structures such as the brain’s white matter can also be inferred.</p>
<p>The development of new MRI analysis techniques is a major area of research, and some of the ideas generated by this research are filtering through to clinical applications. Statistical techniques play a very important part in many of these new developments. Currently, MATLAB is probably the most widely-used programming language for the development of new MRI analysis tools, with the statistical parametric mapping (SPM) package being a notable example (<a href="#bibliography">Friston et al., 2007</a>); but C++, Java and Python are also common choices. Due to its statistical pedigree and vectorised programming model, R (<a href="#bibliography">R Core Team, 2016</a>) is a strong candidate for software development in this field.</p>
<p>In this paper we introduce a software package called <strong>TractoR</strong> (for “Tractography with R”), which consists of several R packages, along with a simple command line based front-end and some associated infrastructure for performing common MRI analysis tasks. TractoR is free software, available from <a href="http://www.tractor-mri.org.uk" class="uri">http://www.tractor-mri.org.uk</a> under the terms of the GNU Public Licence, version 2.</p>
</div>
<div id="package-overview-and-conventions" class="section level2">
<h2>Package overview and conventions</h2>
<p>The core functionality of TractoR is provided in seven R packages. The <strong>tractor.base</strong> package provides general functions for reading, writing and manipulating magnetic resonance images; <strong>tractor.reg</strong> handles image registration or co-alignment; <strong>tractor.session</strong> maintains the various image files associated with a particular scan session or subject, and provides interfaces to third-party software; <strong>tractor.track</strong> provides core functions for performing diffusion <a href="#fibre-tracking">fibre tracking</a>; <strong>tractor.nt</strong> implements <a href="#neighbourhood-tractography">“neighbourhood tractography”</a>; <strong>tractor.graph</strong> provides data structures and algorithms to support graph-based “connectome” analyses; and <strong>tractor.utils</strong> provides various utility functions, primarily used by the TractoR <a href="#the-tractor-shell-interface">shell interface</a>.</p>
<p>The session abstraction obviates the need for the user to keep track of the locations of large numbers of files, instead following a convention for simplicity. The general layout in the current version of TractoR is shown in the table below, and the <strong>tractor.session</strong> package provides accessor functions for obtaining the true path to a particular image, such as the fractional anisotropy map (located at <code>tractor/diffusion/dti_FA</code> by default). The user may specify alternative paths for particular files if necessary, through the use of so-called “session maps”.</p>
<table>
<colgroup>
<col width="33%"></col>
<col width="66%"></col>
</colgroup>
<thead>
<tr class="header">
<th>Directory</th>
<th>Purpose</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>tractor</code></td>
<td>Top-level managed directory</td>
</tr>
<tr class="even">
<td><code>tractor/transforms</code></td>
<td>Stored transformations between different spaces</td>
</tr>
<tr class="odd">
<td><code>tractor/diffusion</code></td>
<td>Diffusion-weighted images and their derivatives (e.g. diffusion tensor components)</td>
</tr>
<tr class="even">
<td><code>tractor/fdt</code></td>
<td>Images and other files used by FSL’s diffusion toolbox</td>
</tr>
<tr class="odd">
<td><code>tractor/fdt.bedpostX</code></td>
<td>Images and other files produced by FSL BEDPOSTX</td>
</tr>
<tr class="even">
<td><code>tractor/structural</code></td>
<td>Structural (e.g. <span class="math inline">\(T_1\)</span>-weighted) images</td>
</tr>
<tr class="odd">
<td><code>tractor/freesurfer</code></td>
<td>Output from the Freesurfer pipeline</td>
</tr>
<tr class="even">
<td><code>tractor/functional</code></td>
<td>Functional (generally <span class="math inline">\(T_2^*\)</span>-weighted BOLD) images</td>
</tr>
</tbody>
</table>
<p>Classes in TractoR are implemented primarily as R reference classes, with a number of methods for accessing and modifying encapsulated data, which are called directly on the object. This will be demonstrated explicitly in the next section.</p>
</div>
<div id="the-mriimage-class" class="section level2">
<h2>The MriImage class</h2>
<p>The core data type used in TractoR is the <code>MriImage</code>. This class is defined in the <strong>tractor.base</strong> R package, along with methods for reading and writing objects from and to standard medical image file types, visualisation and flexible image manipulation. This data type consists of a dense or sparse array of voxel intensity values, along with a set of metadata describing the voxel dimensions, coordinate origin and storage format of the image.</p>
<p>The standard file format used by TractoR to store <code>MriImage</code> objects on disk is the <a href="http://nifti.nimh.nih.gov/nifti-1">NIfTI-1 format</a>, a widely-supported standard in medical imaging. Reading an image from such a file is simple.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"tractor.base"</span>)
i <-<span class="st"> </span><span class="kw">readImageFile</span>(<span class="kw">file.path</span>(<span class="kw">Sys.getenv</span>(<span class="st">"TRACTOR_HOME"</span>), <span class="st">"share"</span>, <span class="st">"mni"</span>, <span class="st">"brain.nii.gz"</span>))</code></pre></div>
<p>(Note that this example requires that the full TractoR distribution has been installed, and that the <code>TRACTOR_HOME</code> environment variable has been correctly set to point to its location on disk.) This particular image is a standard representation of the brain in Montréal Neurological Institute space (MNI space; see <a href="#bibliography">Evans et al., 1994</a>), provided by McGill University. Information about the image can be obtained through its <code>print()</code> method.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">print</span>(i)
<span class="co">#> Image source : /usr/local/tractor/share/mni/brain</span>
<span class="co">#> Image dimensions : 197 x 233 x 189 voxels</span>
<span class="co">#> Voxel dimensions : 1 x 1 x 1 mm</span>
<span class="co">#> Coordinate origin : (99,135,73)</span>
<span class="co">#> Additional tags : (none)</span>
<span class="co">#> Sparseness : 78.25% (dense storage)</span></code></pre></div>
<p>Access to the array of voxel values stored with the image, or specific elements of metadata, is through a series of accessor functions.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">d <-<span class="st"> </span>i$<span class="kw">getData</span>()
<span class="kw">class</span>(d)
<span class="co">#> [1] "array"</span>
<span class="kw">dim</span>(d)
<span class="co">#> [1] 197 233 189</span>
i$<span class="kw">getOrigin</span>()
<span class="co">#> [1] 99 135 73</span></code></pre></div>
<p>A full list of methods available for the <code>MriImage</code> class—or, analogously, any other reference class defined by TractoR—may be obtained by calling the <code>methods()</code> method on an object of the relevant class.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">i$<span class="kw">methods</span>()
<span class="co">#> [1] ".objectPackage" ".objectParent" "apply" </span>
<span class="co">#> [4] "binarise" "callSuper" "copy" </span>
<span class="co">#> [7] "export" "field" "fields" </span>
<span class="co">#> [10] "fill" "getClass" "getData" </span>
<span class="co">#> [13] "getDataAtPoint" "getDimensionality" "getDimensions" </span>
<span class="co">#> [16] "getFieldOfView" "getMetadata" "getNonzeroIndices" </span>
<span class="co">#> [19] "getOrigin" "getRefClass" "getSlice" </span>
<span class="co">#> [22] "getSource" "getSparseness" "getTags" </span>
<span class="co">#> [25] "getVoxelDimensions" "getVoxelUnits" "getXform" </span>
<span class="co">#> [28] "import" "initFields" "initialize" </span>
<span class="co">#> [31] "isEmpty" "isInternal" "isReordered" </span>
<span class="co">#> [34] "isSparse" "map" "mask" </span>
<span class="co">#> [37] "methods" "nTags" "serialise" </span>
<span class="co">#> [40] "setOrigin" "setSource" "setXform" </span>
<span class="co">#> [43] "show" "summarise" "threshold" </span>
<span class="co">#> [46] "trace" "untrace" "usingMethods" </span>
<span class="co">#> [49] "writeToFile"</span></code></pre></div>
<p>Slices of an image, maximum intensity projections and “contact sheet” style visualisations of all image slices can be easily created, with the aspect ratio of the image set according to the voxel dimensions. Simple image processing operations such as applying a lower intensity threshold can also be applied, and more sophisticated image processing is available through the <strong>mmand</strong> package (<a href="#bibliography">Clayden, 2016a</a>), which is provided as part of the full TractoR distribution, or available separately from the Comprehensive R Archive Network.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">createSliceGraphic</span>(i, <span class="dt">z=</span><span class="dv">80</span>)</code></pre></div>
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" /><!-- --></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">j <-<span class="st"> </span>i$<span class="kw">copy</span>()$<span class="kw">threshold</span>(<span class="dv">50</span>)
<span class="kw">createSliceGraphic</span>(j, <span class="dt">z=</span><span class="dv">80</span>)</code></pre></div>
<p><img 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" /><!-- --></p>
<p>The plane of the graphic is set by the option <code>z=80</code>, indicating the slice perpendicular to the <em>z</em>-axis (bottom-to-top), 80 voxels from the bottom of the brain volume. Notice that the image is first copied before thresholding it; otherwise, the threshold would be applied to the original.</p>
<p>Arbitrary functions of one or more images may also be applied easily, using the <code>map()</code> method, as in the examples below.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">k <-<span class="st"> </span>i$<span class="kw">copy</span>()$<span class="kw">map</span>(function(x) x^<span class="dv">2</span>)
l <-<span class="st"> </span>i$<span class="kw">copy</span>()$<span class="kw">map</span>(<span class="st">"*"</span>, j)</code></pre></div>
<p>The first of these produces a new image whose voxel values are the squares of the original, while the second multiplies two images together. The latter may be achieved more succinctly using the standard multiplication operator, as in <code>i*j</code>.</p>
<p>Finally, an <code>MriImage</code> may be created from a standard R array object, by using another <code>MriImage</code> as a template, or by specifying the metadata explicitly. In the following example we create a mask image whose value is 1 wherever the original image is positive, and 0 everywhere else. This could be more simply achieved using the <code>binarise()</code> method, but the longer version is instructive.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">d <-<span class="st"> </span><span class="kw">array</span>(<span class="kw">as.integer</span>(i$<span class="kw">getData</span>() ><span class="st"> </span><span class="dv">0</span>), <span class="dt">dim=</span>i$<span class="kw">getDimensions</span>())
j <-<span class="st"> </span><span class="kw">asMriImage</span>(d, i)
<span class="kw">print</span>(j)
<span class="co">#> Image source : internal</span>
<span class="co">#> Image dimensions : 197 x 233 x 189 voxels</span>
<span class="co">#> Voxel dimensions : 1 x 1 x 1 mm</span>
<span class="co">#> Coordinate origin : (99,135,73)</span>
<span class="co">#> Additional tags : (none)</span>
<span class="co">#> Sparseness : 78.25% (dense storage)</span></code></pre></div>
<p>The first line here binarises the original image, and the second uses the original image as a template to create a new <code>MriImage</code>.</p>
<div id="handling-dicom-files" class="section level3">
<h3>Handling DICOM files</h3>
<p>DICOM (for Digital Imaging and Communications in Medicine; see <a href="http://dicom.nema.org" class="uri">http://dicom.nema.org</a>) is an extremely complex standard for storing and transferring medical imaging information, and is the format in which image data is usually obtained from an MRI scanner. An <code>MriImage</code> object may be created from a directory of related DICOM files, where each file represents a slice of a larger image, and subsequently converted to NIfTI-1 format.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># First copy DICOM files into a temporary directory</span>
<span class="kw">dir.create</span>(path <-<span class="st"> </span><span class="kw">tempfile</span>())
<span class="kw">file.copy</span>(<span class="kw">file.path</span>(<span class="kw">Sys.getenv</span>(<span class="st">"TRACTOR_HOME"</span>), <span class="st">"tests"</span>, <span class="st">"data"</span>, <span class="st">"dicom"</span>, <span class="kw">c</span>(<span class="st">"01.dcm"</span>,<span class="st">"02.dcm"</span>)), path)
<span class="co">#> [1] TRUE TRUE</span>
<span class="co"># Now read and convert the files</span>
info <-<span class="st"> </span><span class="kw">readDicomDirectory</span>(path)
<span class="co">#> Looking for DICOM files in directory /var/folders/k0/s94b87h541b00dbrlpt29_7h0000gn/T//RtmpNMAc22/file181958c78b0a</span>
<span class="co">#> Reading image information from 2 files</span>
<span class="co">#> INFO: [x2] Data matrix is transposed relative to acquisition matrix</span>
<span class="co">#> Image orientation is PIR</span>
<span class="co">#> Data set contains 1 volume(s); 2 slice(s) per volume</span>
<span class="kw">print</span>(info$image)
<span class="co">#> Image source : internal</span>
<span class="co">#> Image dimensions : 2 x 224 x 256 voxels</span>
<span class="co">#> Voxel dimensions : 1 x 1 x 1 mm</span>
<span class="co">#> Coordinate origin : (19.25,95.05,134.78)</span>
<span class="co">#> Additional tags : (none)</span>
<span class="co">#> Sparseness : 0.84% (dense storage)</span>
<span class="kw">writeImageFile</span>(info$image, <span class="st">"image"</span>, <span class="st">"NIFTI"</span>)</code></pre></div>
<p>The output file will be called “image.nii”. (A gzipped NIfTI file, or image/header pair, may be obtained by substituting <code>"NIFTI_GZ"</code> or <code>"NIFTI_PAIR"</code> as the last argument to <code>writeImageFile</code>.)</p>
<p>Alternatively, a DICOM file may be read into a <code>DicomMetadata</code> object, which retains all of the information stored in the file. Individual DICOM “tags”, containing information about the scan acquisition, may then be extracted.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m <-<span class="st"> </span><span class="kw">readDicomFile</span>(<span class="kw">file.path</span>(<span class="kw">Sys.getenv</span>(<span class="st">"TRACTOR_HOME"</span>), <span class="st">"tests"</span>, <span class="st">"data"</span>, <span class="st">"dicom"</span>, <span class="st">"01.dcm"</span>))
m$<span class="kw">getTagValue</span>(<span class="dv">0x0018</span>, <span class="dv">0x0087</span>)
<span class="co">#> [1] 1.494</span>
tractor.base:::<span class="kw">getDescriptionForDicomTag</span>(<span class="dv">0x0018</span>, <span class="dv">0x0087</span>)
<span class="co">#> [1] "Magnetic Field Strength"</span></code></pre></div>
</div>
</div>
<div id="modelling-the-diffusion-weighted-signal" class="section level2">
<h2>Modelling the diffusion-weighted signal</h2>
<p>The <strong>tractor.session</strong> package for R defines the <code>MriSession</code> class, which encapsulates a single scanning session with a single subject, and manages a hierarchy of image files and other information which relate to that scan. Since several of these files are involved in performing most data processing tasks, the abstraction obviates the necessity to specify each of these files individually—rather, only the top-level directory containing the session data need be given explicitly.</p>
<p>Some initial preprocessing is required to move from a set of raw data files acquired from an MRI scanner to a data set ready for fibre tracking as laid out below, and the <strong>tractor.session</strong> package provides tools to perform that preprocessing, but those steps are omitted here for brevity. The <code>dpreproc</code> script, inside the full TractoR distribution’s <code>share/experiments</code> subdirectory, demonstrates how each of the standard preprocessing steps can be achieved. They are conversion of DICOM files to NIfTI-1 format if required (as described <a href="#handling-dicom-files">above</a>), brain extraction and coregistration of diffusion-weighted volumes.</p>
<div id="the-diffusion-tensor" class="section level3">
<h3>The diffusion tensor</h3>
<p>Diffusion MRI data are usually acquired as a series of 3D volumes, each of which has an associated diffusion sensitising magnetic gradient applied along a particular direction relative to the scanner’s native coordinate system, represented as a normalised column vector, <span class="math inline">\(\mathsf{G}\)</span>. Under the assumption that the spatial distribution of diffusing water molecules after a particular time, <span class="math inline">\(t\)</span>, is 3D Gaussian, the relationship between the signal amplitude in the presence of diffusion sensitisation, <span class="math inline">\(S\)</span>, and the signal without it, <span class="math inline">\(S_0\)</span>, is</p>
<p><span class="math display">\[\frac{S(b,\mathsf{G})}{S_0} = \exp \left(-b \mathsf{G}^{\mathsf{T}} \mathsf{D} \mathsf{G} \right) \; .\]</span></p>
<p>Here, <span class="math inline">\(b\)</span> is the diffusion “weighting factor”, which depends on the diffusion time and the strength of the magnetic gradient applied. <span class="math inline">\(\mathsf{D}\)</span> is known as the “effective diffusion tensor”, represented relative to the scanner’s coordinate system, and is related to the covariance matrix of the 3D Gaussian molecular displacement distribution by <span class="math inline">\(\Sigma=2\mathsf{D}t\)</span>. Using the equation above, the elements of the diffusion tensor matrix may be estimated in each voxel of the brain from the log-transformed signal intensities with and without diffusion weighting, using ordinary or weighted least-squares estimation, or more sophisticated methods where required. The combination of dMRI acquisition with diffusion tensor estimation is referred to as diffusion tensor imaging (<a href="#bibliography">Basser et al., 1994</a>).</p>
<p>With the diffusion tensor estimated at each voxel in the brain, it is common for many applications to calculate the eigenvalues, <span class="math inline">\(\lambda_1\)</span>, <span class="math inline">\(\lambda_2\)</span> and <span class="math inline">\(\lambda_3\)</span>, of each tensor, along with various derived quantities. The eigenvalues represent effective diffusivities, generally specified in mm<sup>2</sup> s<sup>-1</sup>, along the principal axes of the displacement distribution. From these are typically calculated measures such as mean diffusivity (MD), the mean of the eigenvalues, <span class="math inline">\(\bar{\lambda}\)</span>, and fractional anisotropy (FA), given by</p>
<p><span class="math display">\[\textrm{FA} = \sqrt{\frac{3}{2}} \sqrt{\frac{(\lambda_1-\bar{\lambda})^2 + (\lambda_2-\bar{\lambda})^2 + (\lambda_3-\bar{\lambda})^2}{\lambda_1^{\,2} + \lambda_2^{\,2} + \lambda_3^{\,2}}} \; .\]</span></p>
<p>Considering the isosurface of probability density after a given diffusion time as an ellipsoid, MD describes the volume of the ellipsoid while FA describes its shape. FA is zero for a spherical ellipsoid, representing isotropic diffusion, while it is close to unity when one eigenvalue is substantially larger than the others, corresponding to a prolate ellipsoid (subfigures a–c, below). The white matter of the brain is highly linearised, and FA is consequently higher in these regions (subfigure d).</p>
<div class="figure">
<img src="data:image/png;base64,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" alt="Ellipsoids representing isotropic (a), oblate (b) and prolate (c) diffusion profiles, along with a map of fractional anisotropy in a slice of the human brain (d)." />
<p class="caption"><em>Ellipsoids representing isotropic (a), oblate (b) and prolate (c) diffusion profiles, along with a map of fractional anisotropy in a slice of the human brain (d).</em></p>
</div>
<p>Diffusion tensor fitting may be performed with TractoR using either ordinary least-squares or iterative weighted least-squares approaches. The latter is recommended due to heteroskedasticity in the log-transformed signal intensities (<a href="#bibliography">Salvador et al., 2005</a>). It may be performed as follows.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"tractor.session"</span>)
s <-<span class="st"> </span><span class="kw">attachMriSession</span>(<span class="kw">file.path</span>(<span class="kw">Sys.getenv</span>(<span class="st">"TRACTOR_HOME"</span>), <span class="st">"tests"</span>, <span class="st">"data"</span>, <span class="st">"session"</span>))
## createDiffusionTensorImagesForSession(s, method="iwls")</code></pre></div>
<p>This command will use preprocessed data files already stored in the specified session directory to estimate diffusion tensors at each image voxel, and write out a series of images representing various quantities derived from it or its diagonalisation, including MD and FA. (Be aware that running this command on the test data supplied with TractoR exactly as given will overwrite some standard test files, and may lead to some of TractoR’s self-tests failing afterwards. It is therefore preferable to use a copy of the test data, or a different data set, when experimenting.) Ordinary least-squares tensor fitting may be performed by substituting <code>method="ls"</code> into the last command. The full path to any particular image created within the session directory in this way may be obtained as follows.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">s$<span class="kw">getImageFileNameByType</span>(<span class="st">"FA"</span>)
<span class="co">#> [1] "/usr/local/tractor/tests/data/session/tractor/diffusion/dti_FA"</span></code></pre></div>
<p>An interface to the tensor estimation tool in the FMRIB Software Library (FSL; see <a href="#bibliography">Smith et al., 2004</a>; <a href="http://fsl.fmrib.ox.ac.uk" class="uri">http://fsl.fmrib.ox.ac.uk</a>) may be used as an alternative, for its somewhat faster speed, as follows.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">runDtifitWithSession</span>(s)</code></pre></div>
<p>In this case only the ordinary least-squares method is used, and FSL must be installed on the user’s system as well as TractoR.</p>
<p>We note that other diffusion signal models, such as a Gaussian mixture model or the so-called “q-ball” model, can be fitted to dMRI data using the <strong>dti</strong> R package (<a href="#bibliography">Polzehl & Tabelow, 2009</a>).</p>
</div>
<div id="a-sampling-based-approach" class="section level3">
<h3>A sampling-based approach</h3>
<p>The assumption of a 3D Gaussian molecular displacement distribution for self-diffusion of water in living tissue is oversimplistic. Even in highly linear white matter, more complex patterns of diffusion occur regularly at the crossings of multiple pathways (<a href="#bibliography">Jones, 2008</a>). For the purpose of fibre tracking, discussed in the next section, it is therefore usually desirable to allow for contributions from multiple fibre populations with different orientations. One such generalisation, described by <a href="#bibliography">Behrens et al. (2007)</a> and often referred to as the “ball and sticks” model, treats the displacement distribution as a mixture of pure isotropic and anisotropic components. Assuming that the orientation of the <span class="math inline">\(i\)</span>th fibre population is given by the column vector <span class="math inline">\(\mathsf{N}_i\)</span>, the signal model then becomes</p>
<p><span class="math display">\[\frac{S(b,\mathsf{G})}{S_0} = \left(1-\sum_i f_i \right) \exp(-bD) + \sum_i f_i \exp \left(-bD \left( \mathsf{G}^{\mathsf{T}} \mathsf{N}_i \right)^2 \right) \; ,\]</span></p>
<p>where <span class="math inline">\(f_i \in [0,1]\)</span> is the volume fraction of the <span class="math inline">\(i\)</span>th anisotropic component, and <span class="math inline">\(D\)</span> is the effective diffusivity.</p>
<p>In a standard dMRI acquisition, the known values in the equation above are <span class="math inline">\(b\)</span> and <span class="math inline">\(\mathsf{G}\)</span>, while <span class="math inline">\(S\)</span> and <span class="math inline">\(S_0\)</span> are observed, and <span class="math inline">\(D\)</span>, <span class="math inline">\((f_i)\)</span> and <span class="math inline">\((\mathsf{N}_i)\)</span> are to be estimated. Rather than calculate point estimates of these parameters, (<a href="#bibliography">Behrens et al., 2007</a>) put forward a Markov chain Monte Carlo (MCMC) approach to estimating their posterior distributions. (The priors used by the algorithm as published are uninformative. Noise in each voxel is modelled to be independent and identically distributed Gaussian, with a Gamma distribution prior on the precision parameter.) They refer to their algorithm as BEDPOST (Bayesian Estimation of Diffusion Parameters Obtained using Sampling Techniques). The algorithm is provided as part of FSL, and TractoR provides a simple interface to it for R, viz.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">runBedpostWithSession</span>(s, <span class="dt">nFibres=</span><span class="dv">3</span>)</code></pre></div>
<p>Here, the <code>nFibres</code> argument determines the maximum number of anisotropic components allowed per voxel, i.e., the upper limit on <span class="math inline">\(i\)</span> in the model in the equation above. BEDPOST uses the automatic relevance determination framework on the volume fraction parameters, <span class="math inline">\((f_i)\)</span>, to set priors which allow the weights of components to be forced to zero where they are irrelevant for predicting the signal (see <a href="#bibliography">MacKay, 1995</a>). In this way, only as many components are maintained in each voxel as are supported by the data. BEDPOST typically takes several hours to run, at the end of which a number of new files are created within the session directory, representing the estimated distributions for each parameter of interest—notably the orientation vectors, <span class="math inline">\((\mathsf{N}_i)\)</span>.</p>
</div>
</div>
<div id="fibre-tracking" class="section level2">
<h2>Fibre tracking</h2>
<p>Diffusion fibre tracking, or “tractography”, is the process of reconstructing white matter tract trajectories by following the local orientation information estimated as <a href="#modelling-the-diffusion-weighted-signal">described above</a>. It has a wide spectrum of applications in clinical imaging and neuroscience.</p>
<div id="tracking-from-single-seed-points" class="section level3">
<h3>Tracking from single seed points</h3>
<p>The standard algorithm for performing streamline tractography from a single seed point may be described as follows.</p>
<ol style="list-style-type: decimal">
<li>Start with the current “front” of the streamline set to the seed point.</li>
<li>Sample a local fibre orientation at the streamline front.</li>
<li>Move the front some small distance in the direction of the sampled direction.</li>
<li>Return to step 2, and repeat until a stopping criterion is met.</li>
<li>Return to step 1 and repeat once, travelling in the opposite direction away from the seed point.</li>
</ol>
<p>Step 2 in the process described above involves some subtlety. Firstly, if a point estimate of the local fibre orientation is used—such as the principal eigenvector of a single diffusion tensor—then the sampling is deterministic. If MCMC is used to estimate a distribution over orientations, on the other hand, then the sampling may be repeated multiple times with different results in general. Thus, multiple streamlines may be generated from a single seed point. Local uncertainty will tend to accumulate in the streamlines’ trajectories as one moves away from the seed point, and a set of streamlines generated in this way give an indication of the precision available for tracking pathways from the seed. Secondly, in models of diffusion allowing for multiple anisotropic components, multiple fibre directions may be present within each voxel, and one must therefore decide which component to sample from. The convention is usually to sample from all components, and then choose the sample which is most similar to the orientation of the previous step; but alternative strategies are possible, and may be more robust (e.g., <a href="#bibliography">Clayden & Clark, 2010</a>).</p>
<p>White matter pathways terminate in grey matter, which does not have the orientational coherence of white matter, and orientation uncertainty is therefore very high in these areas. Stopping criteria usually ensure that the streamline does not leave the brain or turn back on itself, but entering grey matter may also be a reason to stop tracking.</p>
<div class="figure">
<img src="data:image/png;base64,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" alt="Example of fibre tracking in the brain. A set of 1000 sampled streamlines from a single seed point (the red dot) are shown individually (a), and summarised in a visitation map (b)." />
<p class="caption"><em>Example of fibre tracking in the brain. A set of 1000 sampled streamlines from a single seed point (the red dot) are shown individually (a), and summarised in a visitation map (b).</em></p>
</div>
<p>Assuming BEDPOST has already been run on the session directory, single seed tractography can be run as follows.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"tractor.track"</span>)
s <-<span class="st"> </span><span class="kw">attachMriSession</span>(<span class="kw">file.path</span>(<span class="kw">Sys.getenv</span>(<span class="st">"TRACTOR_HOME"</span>), <span class="st">"tests"</span>, <span class="st">"data"</span>, <span class="st">"session"</span>))
p <-<span class="st"> </span>s$<span class="kw">getTracker</span>()$<span class="kw">run</span>(<span class="kw">c</span>(<span class="dv">50</span>,<span class="dv">59</span>,<span class="dv">33</span>), <span class="dt">count=</span><span class="dv">1000</span>, <span class="dt">requireStreamlines=</span><span class="ot">TRUE</span>)</code></pre></div>
<p>The first argument to the <code>run()</code> method of the tracker provides the 3D coordinates of the seed point—using the R convention of indexing from one—and <code>count</code> is the number of streamlines to generate. (If <code>count</code> is set to 1, then only a single streamline is generated, and the result is analogous to “deterministic” tractography.) The resulting set of streamlines is written to a file in TrackVis “.trk” format (<a href="#bibliography">Wang & Wedeen, 2015</a>), which may be visualised in the TrackVis program, or else plotted within R as shown in the figure above.</p>
<p>A common method of presentation for tractography results is as a visitation map or spatial histogram, an image with the same resolution as the original dMRI images wherein each voxel’s value is the number of streamlines which pass through it. An example is shown in subfigure b, above, as a maximum intensity projection overlaid on a slice of the FA image. This type of visualisation may be created using the following TractoR code.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fa <-<span class="st"> </span>s$<span class="kw">getImageByType</span>(<span class="st">"FA"</span>)
i <-<span class="st"> </span>StreamlineSource$<span class="kw">new</span>(p)$<span class="kw">getVisitationMap</span>(fa)
<span class="kw">createSliceGraphic</span>(fa, <span class="dt">z=</span><span class="dv">33</span>)
<span class="kw">createProjectionGraphic</span>(i, <span class="dt">axis=</span><span class="dv">3</span>, <span class="dt">colourScale=</span><span class="dv">2</span>, <span class="dt">add=</span><span class="ot">TRUE</span>)</code></pre></div>
<p><img 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" /><!-- --></p>
<p>Note that an object of class <code>StreamlineSource</code> is created as a wrapper around the “.trk” file full of streamlines created above, and the visitation map is retrieved by calling this object’s <code>getVisitationMap()</code> method. The <code>axis</code> option in the last line controls the axis of the projection, while the <code>colourScale</code> option is used to select a heatmap, rather than greyscale, colour lookup table for the overlay. Since <code>add=TRUE</code> is given, the projection is overlaid on the FA slice graphic created by the previous command.</p>
<p>If only a visitation map is required, without the intermediate set of streamlines, then the same effect may be achieved using the command</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">i <-<span class="st"> </span><span class="kw">readImageFile</span>(s$<span class="kw">getTracker</span>()$<span class="kw">run</span>(<span class="kw">c</span>(<span class="dv">50</span>,<span class="dv">59</span>,<span class="dv">33</span>), <span class="dt">count=</span><span class="dv">1000</span>))</code></pre></div>
<p>This works because the <code>run()</code> method of the <code>Tracker</code> class returns the path to the file or files that it creates.</p>
</div>
<div id="tracking-between-regions" class="section level3">
<h3>Tracking between regions</h3>
<p>An alternative to single seed tractography, which is relevant to many applications, is tracking between regions of the brain. Under this arrangement, a seed region is used to generate streamlines, and one or more “waypoint” regions are used to constrain their paths. In most implementations, streamlines which do not pass through all of the waypoint regions are simply discarded.</p>
<p>TractoR provides facilities for identifying target regions and filtering out streamlines that do not reach them. For example,</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Create a 3 x 3 x 3 voxel neighbourhood</span>
n <-<span class="st"> </span><span class="kw">createNeighbourhoodInfo</span>(<span class="dv">3</span>)
<span class="co"># Duplicate the FA map twice, fill each with 0s, and then add 1s</span>
<span class="co"># within the neighbourhood of a point in each case</span>
seed <-<span class="st"> </span>fa$<span class="kw">copy</span>()$<span class="kw">fill</span>(0L)
seed[<span class="kw">t</span>(n$vectors +<span class="st"> </span><span class="kw">c</span>(<span class="dv">42</span>,<span class="dv">66</span>,<span class="dv">32</span>))] <-<span class="st"> </span>1L
target <-<span class="st"> </span>fa$<span class="kw">copy</span>()$<span class="kw">fill</span>(0L)
target[<span class="kw">t</span>(n$vectors +<span class="st"> </span><span class="kw">c</span>(<span class="dv">58</span>,<span class="dv">66</span>,<span class="dv">33</span>))] <-<span class="st"> </span>1L
<span class="co"># Retrieve the session's tracker and set options, then track</span>
tracker <-<span class="st"> </span>s$<span class="kw">getTracker</span>()$<span class="kw">setTargets</span>(target)$<span class="kw">setFilters</span>(<span class="dt">minTargetHits=</span><span class="dv">1</span>)
i <-<span class="st"> </span><span class="kw">readImageFile</span>(tracker$<span class="kw">run</span>(seed$<span class="kw">getNonzeroIndices</span>(), <span class="dt">count=</span><span class="dv">500</span>))
<span class="co">#> 4450 streamlines (33%) were retained after filtering</span>
<span class="co"># Visualise the result, and the original regions of interest</span>
<span class="kw">createSliceGraphic</span>(fa, <span class="dt">z=</span><span class="dv">32</span>)
<span class="kw">createProjectionGraphic</span>(i, <span class="dt">axis=</span><span class="dv">3</span>, <span class="dt">colourScale=</span><span class="dv">2</span>, <span class="dt">add=</span><span class="ot">TRUE</span>)
<span class="kw">createSliceGraphic</span>(seed, <span class="dt">z=</span><span class="dv">32</span>, <span class="dt">colourScale=</span><span class="st">"green"</span>, <span class="dt">add=</span><span class="ot">TRUE</span>)
<span class="kw">createSliceGraphic</span>(target, <span class="dt">z=</span><span class="dv">32</span>, <span class="dt">colourScale=</span><span class="st">"blue"</span>, <span class="dt">add=</span><span class="ot">TRUE</span>)</code></pre></div>
<p><img 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" /><!-- --></p>
<p>In this case, the <code>count</code> option controls the number of streamlines generated <em>per seed point</em> within the mask. These commands create a seed mask and waypoint mask (each as a 3 x 3 x 3 voxel block), run tractography using them, and visualise the results. The visualisation is built up with an FA slice as the base layer, then a projection of the tract, and finally the two regions of interest.</p>
</div>
<div id="neighbourhood-tractography" class="section level3">
<h3>Neighbourhood tractography</h3>
<p>“Neighbourhood tractography” is an alternative to waypoint methods, which uses a reference tract and machine learning methods to find consistent representations of a particular tract of interest in a group of dMRI data sets (<a href="#bibliography">Clayden et al., 2007, 2009b</a>). The principle is to model the variation in shape of the tract across individuals, and then evaluate the plausibility of a given “candidate tract” as a match to the reference tract. The <strong>tractor.nt</strong> package provides functions and supporting data structures for performing neighbourhood tractography, and a standard set of reference tracts are provided with the main TractoR distribution (<a href="#bibliography">Muñoz Maniega et al., 2008</a>). New reference tracts may also be created by the user.</p>
<p>Reference and candidate tracts are represented for these purposes as B-splines with a fixed distance between knot points. Where multiple streamlines are generated from a single seed point, the spline is fitted to the spatial median of the set. One knot is arranged to coincide with the seed point, and for the <span class="math inline">\(i\)</span>th candidate tract in a data set there are then <span class="math inline">\(L_1^i\)</span> knots to one side of the seed, and <span class="math inline">\(L_2^i\)</span> to the other. Working away from the seed point, we denote the angle between the straight line connecting knot <span class="math inline">\(u-1\)</span> to knot <span class="math inline">\(u\)</span>, and its equivalent in the reference tract, with <span class="math inline">\(\phi^i_u\)</span>. The index, <span class="math inline">\(u\)</span>, is taken as being negative to one side of the seed, and positive to the other side.</p>
<p>A set of indicator variables, <span class="math inline">\((z^i)\)</span>, describe which candidate tract is the best match to the reference tract within the data set, such that <span class="math inline">\(z^i=1\)</span> if tract <span class="math inline">\(i\)</span> is the best match and <span class="math inline">\(z^i=0\)</span> otherwise. The special case <span class="math inline">\(z^0=1\)</span> is also allowed, to indicate no match. The likelihood of the model given the observed data then depends on the value of the relevant indicator variable. Our aim is to establish the posterior distribution <span class="math inline">\(P(z^i\,|\,D)\)</span>, where <span class="math inline">\(D\)</span> represents all data, for all candidate tracts, subject to the constraint that</p>
<p><span class="math display">\[\sum_{i=0}^N P(z^i=1) = 1 \, ;\]</span></p>
<p>i.e., there is exactly one best match, or none.</p>
<p>Given the shape and length data for the best matching tract and an appropriate reference tract, the likelihood is given by</p>
<p><span class="math display">\[P(\mathbf{d}^i\,|\,\mathbf{A},\mathbf{p},z^i=1) = P(L^i_1\,|\,L^*_1,\mathbf{p}_1,z^i=1) \, P(L^i_2\,|\,L^*_2,\mathbf{p}_2,z^i=1) \prod_{u=1}^{\check{L}^i_1} P(\phi^i_{-u}\,|\,\alpha_u,z^i=1) \prod_{u=1}^{\check{L}^i_2} P(\phi^i_{u}\,|\,\alpha_u,z^i=1) \; ,\]</span></p>
<p>where <span class="math inline">\(\mathbf{d}^i = (L_1^i,L_2^i,(\phi_u^i))\)</span>, <span class="math inline">\(\mathbf{A}=(\alpha_u)\)</span> and <span class="math inline">\(\mathbf{p}=(\mathbf{p}_1,\mathbf{p}_2)\)</span>—the latter two being parameters of the model. <span class="math inline">\(L^*_1\)</span> and <span class="math inline">\(L^*_2\)</span> are the lengths of the reference tract corresponding to <span class="math inline">\(L^i_1\)</span> and <span class="math inline">\(L^i_2\)</span> respectively; <span class="math inline">\(\check{L}^i_1 = \min \{ L^i_1, L^*_1 \}\)</span>, and equivalently for <span class="math inline">\(\check{L}^i_2\)</span>. The corresponding forward model for tracts which do not match the reference (with <span class="math inline">\(z^i=0\)</span>) is uninformative, since the reference tract is not a good predictor of their topologies.</p>
<div class="figure">
<img 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" alt="Graphical representation of the tract shape model for tracts matched to a reference tract, including priors on model parameters. The shaded nodes represent observed variables." />
<p class="caption"><em>Graphical representation of the tract shape model for tracts matched to a reference tract, including priors on model parameters. The shaded nodes represent observed variables.</em></p>
</div>
<p>A graphical representation of this model is shown above, and the distributional details are as follows.</p>
\begin{align}
L^i_1 \, | \, L^*_1 \sim \mathrm{Multinomial}(n_1,\mathbf{p}_1) \nonumber \\
L^i_2 \, | \, L^*_2 \sim \mathrm{Multinomial}(n_2,\mathbf{p}_2) \\
\frac{\cos \phi^i_u + 1}{2} \sim \mathrm{Beta}(\alpha_u,1) \nonumber
\end{align}
<p>where <span class="math inline">\(n_1 = |\mathbf{p}_1|\)</span>, and equivalently for <span class="math inline">\(n_2\)</span>. Multinomial distributions are appropriate for the tract length variables because they reflect the number of knots either side of the seed point, which must be integral. The true length of the tract is therefore approximated by the sum of the fixed-length gaps between knots. We apply the prior</p>
<p><span class="math display">\[\alpha_u - 1 \sim \mathrm{Exponential}(\lambda) \; ,\]</span></p>
<p>which constrains each <span class="math inline">\(\alpha_u\)</span> to ensure that smaller deviations from the reference tract are always more likely (<span class="math inline">\(\alpha_u \geq 1\)</span>), and also regularises their distributions to avoid model overfitting for small data sets.</p>
<p>The model may be fitted in a supervised fashion by choosing a set of training tracts representing good matches to the reference , or taking an unsupervised approach using an Expectation–Maximisation (EM) algorithm (<a href="#bibliography">Clayden et al., 2009b</a>). The unsupervised approach is generally preferable in applications, unless test data are very scarce, in which case it may be helpful to train from another population. The same framework may also be used to remove individual streamlines which are inconsistent with the trajectory of the reference tract (<a href="#bibliography">Clayden et al., 2009a</a>).</p>
<p>A set of B-spline tract representations suitable for training or evaluating against a model may be created using the following commands.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"tractor.nt"</span>)
s <-<span class="st"> </span><span class="kw">attachMriSession</span>(<span class="kw">file.path</span>(<span class="kw">Sys.getenv</span>(<span class="st">"TRACTOR_HOME"</span>), <span class="st">"tests"</span>, <span class="st">"data"</span>, <span class="st">"session"</span>))
r <-<span class="st"> </span><span class="kw">getNTResource</span>(<span class="st">"reference"</span>, <span class="st">"pnt"</span>, <span class="kw">list</span>(<span class="dt">tractName=</span><span class="st">"genu"</span>))
n <-<span class="st"> </span><span class="kw">createNeighbourhoodInfo</span>(<span class="dt">centre=</span><span class="kw">c</span>(<span class="dv">50</span>,<span class="dv">59</span>,<span class="dv">33</span>), <span class="dt">width=</span><span class="dv">3</span>)
l <-<span class="st"> </span><span class="kw">calculateSplinesForNeighbourhood</span>(s, n, r, <span class="dt">nStreamlines=</span><span class="dv">100</span>)</code></pre></div>
<p>Here, we are using the standard reference for the “genu” tract as the reference, and creating B-spline candidate tracts for every point within a <span class="math inline">\(3 \times 3 \times 3\)</span> voxel neighbourhood centred at (50,59,33). (The <code>getNTResource()</code> function is used to obtain various standard resources provided with TractoR for performing neighbourhood tractography. In this case, we are requesting a reference tract, <code>"reference"</code>, associated with tract name <code>"genu"</code>, appropriate for probabilistic neighbourhood tractography, <code>"pnt"</code>.) Each B-spline is fitted to the median of 100 sampled streamlines. We must then calculate the components of <span class="math inline">\(\mathbf{d}^i\)</span> for each seed point, and this is achieved with</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">d <-<span class="st"> </span><span class="kw">createDataTableForSplines</span>(l, r$<span class="kw">getTract</span>(), <span class="st">"knot"</span>, <span class="dt">sessionPath=</span>s$<span class="kw">getDirectory</span>(), <span class="dt">neighbourhood=</span>n)</code></pre></div>
<p>The reference tract needs to be provided here to calculate the angles, <span class="math inline">\((\phi^i_u)\)</span>, between segments of the reference and candidate tracts.</p>
<p>We can now fit a model using these B-spline tracts as training data. (Of course, this is not wise in practice without manually removing aberrant tracts, and in any case tracts from several data sets should be used for a generalisable model, but this serves to illustrate the method.)</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m <-<span class="st"> </span><span class="kw">newMatchingTractModelFromDataTable</span>(d, r$<span class="kw">getTract</span>(), <span class="dt">lambda=</span><span class="dv">1</span>, <span class="dt">alphaOffset=</span><span class="dv">1</span>, <span class="dt">weights=</span><span class="ot">NULL</span>, <span class="dt">asymmetric=</span><span class="ot">TRUE</span>)
<span class="kw">print</span>(m)
<span class="co">#> INFO: [x27] Fitting B-spline model with fixed knot spacing of 8.38</span>
<span class="co">#> Asymmetric model : TRUE</span>
<span class="co">#> Alphas (left) : 1.98, 1.96, 1.99, 1.96, 1.99</span>
<span class="co">#> Alphas (right) : 1.99, 1.97, 1.99, 1.97, 1.95</span>
<span class="co">#> Ref tract lengths : 6 (left), 6 (right)</span>
<span class="co">#> Length cutoff : 11</span>
<span class="co">#> Point type : knot</span></code></pre></div>
<p>The model object has class <code>MatchingTractModel</code>. The values of <code>lambda</code> (<span class="math inline">\(\lambda\)</span>), <code>alphaOffset</code> and <code>weights</code> specified here will have a significant effect on the outcome—indeed, with only one subject in the data set, the prior dominates in this example, limiting the values of <span class="math inline">\(\alpha_u\)</span> severely. The larger the value of <code>lambda</code>, the greater the extent to which the prior will favour small values of <span class="math inline">\(\alpha_u\)</span>, since the exponential prior has mean <span class="math inline">\(1/\lambda\)</span>. A value of <code>alphaOffset=1</code> corresponds to the prior given above. If the specified weights are <code>NULL</code>, then each tract is given the same weight. In the EM context, this corresponds to an assumption that each tract is <em>a priori</em> equiprobable. The <code>asymmetric</code> parameter determines whether a constraint that <span class="math inline">\(\alpha_u = \alpha_{-u}\)</span> should be applied (the symmetric case) or not (the asymmetric case). It is generally wise to set this to <code>TRUE</code> unless very few data are available.</p>
<p>The trained model generated above may be used directly to calculate the posterior matching probabilities of a new set of candidate tracts. However, in applications it is often helpful to work in an unsupervised fashion, learning the model and calculating posteriors in one go, since this removes the need for separate training data. This can be achieved as follows.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m <-<span class="st"> </span><span class="kw">runMatchingEMForDataTable</span>(d, r$<span class="kw">getTract</span>(), <span class="dt">lambda=</span><span class="dv">1</span>, <span class="dt">alphaOffset=</span><span class="dv">1</span>, <span class="dt">asymmetricModel=</span><span class="ot">TRUE</span>)
<span class="co">#> Starting EM algorithm</span>
<span class="kw">print</span>(m$mm)
<span class="co">#> Asymmetric model : TRUE</span>
<span class="co">#> Alphas (left) : 1.98, 1.97, 1.99, 1.96, 1.98</span>
<span class="co">#> Alphas (right) : 1.99, 1.97, 1.99, 1.97, 1.96</span>
<span class="co">#> Ref tract lengths : 6 (left), 6 (right)</span>
<span class="co">#> Length cutoff : 13</span>
<span class="co">#> Point type : knot</span>
<span class="kw">which.max</span>(m$tp[[<span class="dv">1</span>]])
<span class="co">#> [1] 2</span></code></pre></div>
<p>The list <code>m$tp</code> gives the tract matching posteriors for each subject in the data set. Here, under the final model, the 2nd (of 27) candidate tracts has the highest posterior. The mean FA within this tract may finally be calculated as follows.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Read the visitation map, threshold and binarise it</span>
i <-<span class="st"> </span><span class="kw">readImageFile</span>(s$<span class="kw">getTracker</span>()$<span class="kw">run</span>(n$vectors[,<span class="dv">2</span>], <span class="dt">count=</span><span class="dv">1000</span>))
i$<span class="kw">threshold</span>(<span class="fl">0.01</span>*<span class="kw">max</span>(i))$<span class="kw">binarise</span>()
fa <-<span class="st"> </span>s$<span class="kw">getImageByType</span>(<span class="st">"FA"</span>)
<span class="co"># Find the average FA within the nonzero voxels of the thresholded map</span>
<span class="kw">mean</span>(fa[i$<span class="kw">getNonzeroIndices</span>()], <span class="dt">na.rm=</span><span class="ot">TRUE</span>)
<span class="co">#> [1] 0.4718762</span></code></pre></div>
<p>This represents the mean FA within the region visited by at least 1% (<code>i$threshold(0.01*max(i))</code>) of the streamlines initiated from the final seed point. Mean FA values from a full data set may then be analysed using standard R hypothesis testing functions such as <code>t.test()</code> or <code>cor.test()</code>.</p>
<p>It should be noted that there is considerable scope in the code provided with the <strong>tractor.nt</strong> package for adjusting the exact processes applied, or for using similar models for other purposes, but we have focussed on a standard usage here for brevity.</p>
</div>
</div>
<div id="the-tractor-shell-interface" class="section level2">
<h2>The TractoR shell interface</h2>
<p>In addition to the three main R packages whose major functionality has been laid out above, TractoR provides a shell interface and set of related R “experiment scripts” for performing various common tasks quickly and directly. In addition, it allows those without experience of working with R to use some of TractoR’s core functionality immediately. An additional R package, <strong>tractor.utils</strong>, exists mainly to support this interface. It should be noted that the interface uses a Unix shell script and is therefore not directly usable in Windows.</p>
<p>The interface works through a single shell script program called <code>tractor</code>, which may be executed by typing just its name if the <code>bin</code> subdirectory of the TractoR distribution is on the user’s <code>PATH</code>. Specific tasks are chosen by giving the name of a particular experiment script: for example, the <code>dpreproc</code> script may be used for preprocessing dMRI data, the <code>track</code> script can be used for performing tractography, and so on. All available experiment scripts may be listed by using the <code>list</code> script, viz.</p>
<div class="sourceCode"><pre class="sourceCode bash"><code class="sourceCode bash"><span class="kw">tractor</span> list
<span class="co">#> Starting TractoR environment...</span>
<span class="co">#> Experiment scripts found in /usr/local/tractor/share/experiments:</span>
<span class="co">#> [1] age apply bedpost binarise </span>
<span class="co">#> [5] chfiletype clone components compress </span>
<span class="co">#> [9] console dicomread dicomsort dicomtags </span>
<span class="co">#> [13] dirviz dpreproc extract freesurf </span>
<span class="co">#> [17] gradcheck gradread gradrotate graph-build </span>
<span class="co">#> [21] graph-decompose graph-extract graph-props graph-reweight </span>
<span class="co">#> [25] graph-viz graph2csv hnt-eval hnt-interpret </span>
<span class="co">#> [29] hnt-ref hnt-viz imageinfo imagestats </span>
<span class="co">#> [33] import list mean mkroi </span>
<span class="co">#> [37] morph parcellate path peek </span>
<span class="co">#> [41] platform plotcorrections pnt-data pnt-em </span>
<span class="co">#> [45] pnt-eval pnt-interpret pnt-prune pnt-ref </span>
<span class="co">#> [49] pnt-train pnt-viz reg-apply reg-check </span>
<span class="co">#> [53] reg-info reg-linear reg-nonlinear reg-viz </span>
<span class="co">#> [57] reshape slice smooth split </span>
<span class="co">#> [61] status tensorfit track transform </span>
<span class="co">#> [65] trim update values view </span>
<span class="co">#> </span>
<span class="co">#> Experiment completed with 0 warning(s) and 0 error(s)</span></code></pre></div>
<p>In addition, a description and list of supported arguments and options for a particular script may be obtained by using the <code>-o</code> flag to the <code>tractor</code> program.</p>
<div class="sourceCode"><pre class="sourceCode bash"><code class="sourceCode bash"><span class="kw">tractor</span> -o mean
<span class="co">#> OPTIONS for script /usr/local/tractor/share/experiments/mean.R (* required)</span>
<span class="co">#> AveragingMode: binary [weighted]</span>
<span class="co">#> ThresholdLevel: 0.01</span>
<span class="co">#> ThresholdRelativeTo: nothing [maximum,minimum]</span>
<span class="co">#> ARGUMENTS: metric image, [mask image]</span>
<span class="co">#> </span>
<span class="co">#> DESCRIPTION:</span>
<span class="co">#> Calculate the mean or weighted mean value of a metric within the nonzero region </span>
<span class="co">#> of a brain volume. The specified mask image can be used as a binary mask (the </span>
<span class="co">#> default) or as a set of weights (with AveragingMode:weighted). In the latter </span>
<span class="co">#> case any weight threshold given is ignored. If the mask is missing then the </span>
<span class="co">#> metric image is itself the mask.</span></code></pre></div>
<p>The <code>tractor</code> program has a full Unix <code>man</code> page, and that may be consulted for further details on how to use and configure this interface. Finally, it is possible to call these scripts from R, using the <code>callExperiment()</code> function.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"tractor.utils"</span>)
<span class="kw">callExperiment</span>(<span class="st">"imageinfo"</span>, <span class="kw">file.path</span>(<span class="kw">Sys.getenv</span>(<span class="st">"TRACTOR_HOME"</span>),<span class="st">"share"</span>,<span class="st">"mni"</span>,<span class="st">"brain"</span>), <span class="dt">outputLevel=</span>OL$Info)
<span class="co">#> Running experiment script /usr/local/tractor/share/experiments/imageinfo.R</span>
<span class="co">#> Image source : /usr/local/tractor/share/mni/brain</span>
<span class="co">#> Image dimensions : 197 x 233 x 189 voxels</span>
<span class="co">#> Voxel dimensions : 1 x 1 x 1 mm</span>
<span class="co">#> Coordinate origin : (99,135,73)</span>
<span class="co">#> Additional tags : (none)</span></code></pre></div>
</div>
<div id="conclusion" class="section level2">
<h2>Conclusion</h2>
<p>In this paper we have described <strong>TractoR</strong>, a set of R packages along with a separate shell interface and scripting platform for processing magnetic resonance images in general, and dMRI data in particular. The package provides facilities for general-purpose reading, writing and manipulation of 2D, 3D and 4D images, along with signal modelling, tractography and tract shape modelling functions specific to dMRI. Relatively recent additions which have not been explicitly covered in this paper include image registration using the <strong>RNiftyReg</strong> package (<a href="#bibliography">Clayden, 2016b</a>), and graph-based “connectome” analysis. It is intended that this platform will continue to broaden in the future, making further use of the comprehensive statistical capabilities of the R language and package ecosystem.</p>
</div>
<div id="bibliography" class="section level2">
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