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<section id="frequently-asked-questions">
<span id="faq"></span><h1>Frequently Asked Questions<a class="headerlink" href="#frequently-asked-questions" title="Link to this heading">#</a></h1>
<section id="theoretical">
<h2>Theoretical<a class="headerlink" href="#theoretical" title="Link to this heading">#</a></h2>
<ol class="arabic simple">
<li><p><strong>What is a b-value?</strong></p></li>
</ol>
<blockquote>
<div><p>The b-value <span class="math notranslate nohighlight">\(b\)</span> or <em>diffusion weighting</em> is a function of the
strength, duration and temporal spacing and timing parameters of the
specific paradigm. This function is derived from the Bloch-Torrey
equations. In the case of the classical Stejskal-Tanner
pulsed gradient spin-echo (PGSE) sequence, at the time of readout
<span class="math notranslate nohighlight">\(b=\gamma^{2}G^{2}\delta^{2}\left(\Delta-\frac{\delta}{3}\right)\)</span>
where <span class="math notranslate nohighlight">\(\gamma\)</span> is the gyromagnetic ratio, <span class="math notranslate nohighlight">\(\delta\)</span> denotes the pulse
width, <span class="math notranslate nohighlight">\(G\)</span> is the gradient amplitude and <span class="math notranslate nohighlight">\(\Delta\)</span> the center-to-center
spacing. <span class="math notranslate nohighlight">\(\gamma\)</span> is a constant, but we can change the other
three parameters and in that way control the b-value.</p>
</div></blockquote>
<ol class="arabic simple" start="2">
<li><p><strong>What is q-space?</strong></p></li>
</ol>
<blockquote>
<div><p>Q-space is the space of one or more 3D spin displacement wave vectors
<span class="math notranslate nohighlight">\(\mathbf{q}\)</span>. The vector <span class="math notranslate nohighlight">\(\mathbf{q}\)</span>
parametrizes the space of diffusion gradients. It is related to the
applied magnetic gradient <span class="math notranslate nohighlight">\(\mathbf{g}\)</span> by the formula
<span class="math notranslate nohighlight">\(\mathbf{q}=(2\pi)^{-1}\gamma\delta\mathbf{g}\)</span>.
Every single vector <span class="math notranslate nohighlight">\(\mathbf{q}\)</span> has the same orientation as the
direction of diffusion gradient <span class="math notranslate nohighlight">\(\mathbf{g}\)</span> and length proportional
to the strength <span class="math notranslate nohighlight">\(g\)</span> of the gradient field. Every single point in
q-space corresponds to a possible 3D volume of the MR signal for a specific
gradient direction and strength. Therefore if, for example, we have
programmed the scanner to apply 60 gradient directions, then our data
should have 60 diffusion volumes, with each volume obtained for a specific
gradient. A Diffusion Weighted Image (DWI) is the volume acquired
from only one direction gradient.</p>
</div></blockquote>
<ol class="arabic simple" start="3">
<li><p><strong>What does DWI stand for?</strong></p></li>
</ol>
<blockquote>
<div><p>Diffusion Weighted Imaging (DWI) is MRI imaging designed to be sensitive
to diffusion. A diffusion weighted image is a volume of voxel data gathered
by applying only one gradient direction
using a diffusion sequence. We expect that the signal in any voxel
should be low if there is greater mobility of water molecules along
the specified gradient direction and it should be high if there is
less movement in that direction. Yes, it is counterintuitive but correct!
However, greater mobility gives greater opportunity for the proton spins to
be dephased, producing a smaller RF signal.</p>
</div></blockquote>
<ol class="arabic simple" start="4">
<li><p><strong>Why dMRI and not DTI?</strong></p></li>
</ol>
<blockquote>
<div><p>Diffusion MRI (dMRI or dwMRI) are the preferred terms if you want to speak
about diffusion weighted MRI in general. DTI (diffusion tensor imaging) is
just one of the many ways you can reconstruct the voxel from your measured
signal. There are plenty of others, for example DSI, GQI, QBI, etc.</p>
</div></blockquote>
<ol class="arabic simple" start="5">
<li><p><strong>What is the difference between Image coordinates and World coordinates?</strong></p></li>
</ol>
<blockquote>
<div><p>Image coordinates have positive integer values and represent the centres
<span class="math notranslate nohighlight">\((i, j, k)\)</span> of the voxels. There is an affine transform (stored in the
nifti file) that takes the image coordinates and transforms them into
millimeter (mm) in real world space. World coordinates have floating point
precision and your dataset has 3 real dimensions e.g. <span class="math notranslate nohighlight">\((x, y, z)\)</span>.</p>
</div></blockquote>
<ol class="arabic simple" start="6">
<li><p><strong>We generated dMRI datasets with nonisotropic voxel sizes. What do we do?</strong></p></li>
</ol>
<blockquote>
<div><p>You need to resample your raw data to an isotropic size. Have a look at
the module <code class="docutils literal notranslate"><span class="pre">dipy.align.aniso2iso</span></code>. (We think it is a mistake to
acquire nonisotropic data because the directional resolution of the data
will depend on the orientation of the gradient with respect to the
voxels, being lower when aligned with a longer voxel dimension.)</p>
</div></blockquote>
<ol class="arabic simple" start="7">
<li><p><strong>Why are non-isotropic voxel sizes a bad idea in diffusion?</strong></p></li>
</ol>
<blockquote>
<div><p>If, for example, you have <span class="math notranslate nohighlight">\(2 \times 2 \times 4\ \textrm{mm}^3\)</span> voxels, the
last dimension will be averaged over the double distance and less detail
will be captured compared to the other two dimensions. Furthermore, with
very anisotropic voxels the uncertainty on orientation estimates will
depend on the position of the subject in the scanner.</p>
</div></blockquote>
</section>
<section id="practical">
<h2>Practical<a class="headerlink" href="#practical" title="Link to this heading">#</a></h2>
<ol class="arabic simple">
<li><p><strong>Why Python and not MATLAB or some other language?</strong></p></li>
</ol>
<blockquote>
<div><p>Python is free, batteries included, very well-designed, painless to read
and easy to use.
There is nothing else like it. Give it a go.
Once with Python, always with Python.</p>