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preprocessing.html
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<h1 class="title toc-ignore">Preprocessing</h1>
<h4 class="date">Last updated: May 07, 2024</h4>
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<p><br></p>
<p>Listed below are the preprocessing routines that are run in
<code>GannetLoad.m</code>. Whether a particular routine or subroutine is
run will depend on the format of the inputted data and the options set
in <code>GannetPreInitialise.m</code>.</p>
<div id="rf-coil-combination" class="section level2 hasAnchor">
<h2 class="hasAnchor">RF coil combination<a href="#rf-coil-combination"
class="anchor-section" aria-label="Anchor link to header"></a></h2>
<p>Certain raw MRS data formats store data without coil combination;
specifically, GE P-file (.7), NIfTI-MRS (if the source data were raw),
Philips .raw, and Siemens TWIX (.dat) data. Gannet uses generalized
least squares<span class="citation"><sup><a
href="#ref-An2013">1</a></sup></span> to optimally combine the signal
from the multiple RF channels. If water files are provided, these data
will be used as references for signal weighting and phasing of the coil
data.</p>
</div>
<div id="eddy-current-correction" class="section level2 hasAnchor">
<h2 class="hasAnchor">Eddy-current correction<a
href="#eddy-current-correction" class="anchor-section"
aria-label="Anchor link to header"></a></h2>
<div class="info">
<p><i class="fa fa-info-circle" style="color: white"></i> Eddy-current
correction can only be applied if water reference data are provided.</p>
</div>
<p>In <code>GannetPreInitialise.m</code>, users have the option to apply
eddy-current correction (ECC) to metabolite and water data. If applied,
Gannet uses the method described by Klose (1990)<span
class="citation"><sup><a href="#ref-Klose1990">2</a></sup></span>. The
code for the ECC routine can be found in
<code>EddyCurrentCorrection.m</code>.</p>
</div>
<div id="phase-correction" class="section level2 hasAnchor">
<h2 class="hasAnchor">Phase correction<a href="#phase-correction"
class="anchor-section" aria-label="Anchor link to header"></a></h2>
<p>It is common for unprocessed spectra to be out of phase. Gannet
applies a global zero-order phase correction to all transients by
fitting the real-valued 3 ppm Cr and 3.2 ppm Cho signals in the
frequency domain and correcting the phase to be 0° phase (i.e., to make
all the Cr and Cho peaks have positive phase). This is performed in
<code>PhaseCorrection.m</code>.</p>
</div>
<div id="line-broadending-apodization" class="section level2 hasAnchor">
<h2 class="hasAnchor">Line-broadending (apodization)<a
href="#line-broadending-apodization" class="anchor-section"
aria-label="Anchor link to header"></a></h2>
<p>FID data are multiplied by a time-varying exponential weighting
function where the weighting constant is set in
<code>GannetPreInitialise.m</code> (3 Hz is the default).</p>
</div>
<div id="zero-filling" class="section level2 hasAnchor">
<h2 class="hasAnchor">Zero-filling<a href="#zero-filling"
class="anchor-section" aria-label="Anchor link to header"></a></h2>
<p>Gannet zero-pads all raw FIDs (i.e., adds zeros to the end of each
FID) to obtain a nominal spectral resolution (the resolution between
each frequency-domain data point) of 0.061 Hz/point. Differences in
spectral width and number of complex data points of the raw FIDs are
accounted for to obtain this nominal spectral resolution.</p>
</div>
<div id="frequency-and-phase-alignment"
class="section level2 hasAnchor">
<h2 class="hasAnchor">Frequency and phase alignment<a
href="#frequency-and-phase-alignment" class="anchor-section"
aria-label="Anchor link to header"></a></h2>
<p>During acquisition, spectral data are affected by frequency and phase
offsets as a result of biophysical, electronic, and participant factors.
Gannet has several algorithms to correct for these errors during
preprocessing. Users can choose which method to use in
<code>GannetPreInitialise.m</code>:</p>
<table>
<colgroup>
<col width="20%" />
<col width="20%" />
<col width="40%" />
<col width="20%" />
</colgroup>
<thead>
<tr class="header">
<th align="left"><u>Method</u></th>
<th align="left"><u>Option</u></th>
<th align="left"><u>Description</u></th>
<th align="left"><u>Function</u></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Spectral registration<span class="citation"><sup><a
href="#ref-Near2015">3</a></sup></span></td>
<td align="left"><code>SpecReg</code></td>
<td align="left">A time-domain-based alignment method that uses
nonlinear least-squares optimization to align each individual transient
to a reference transient.</td>
<td align="left"><code>SpectralRegistration.m</code></td>
</tr>
<tr class="even">
<td align="left">Multi-step frequency and phase correction<span
class="citation"><sup><a
href="#ref-Mikkelsen2018">4</a></sup></span></td>
<td align="left"><code>SpecRegHERMES</code></td>
<td align="left">A method originally developed to align multiplexed
edited HERMES data. This approach is based on spectral
registration.</td>
<td align="left"><code>SpectralRegistrationHERMES.m</code></td>
</tr>
<tr class="odd">
<td align="left">Robust spectral registration<span
class="citation"><sup><a href="#ref-Mikkelsen2020">5</a></sup></span>
(the default and recommended)</td>
<td align="left"><code>RobustSpecReg</code></td>
<td align="left">A method based on spectral registration that is robust
against spectral distortions caused by unstable residual water peaks and
lipid contamination.</td>
<td align="left"><code>RobustSpectralRegistration.m</code></td>
</tr>
<tr class="even">
<td align="left">Peak alignment</td>
<td align="left"><code>Cr</code>, <code>Cho</code>, <code>NAA</code>,
<code>H2O</code></td>
<td align="left">Frequency-domain-based alignment using one of the
following peaks in each transient as the target signal: Cr, Cho, NAA, or
residual H2O.</td>
<td align="left"><code>AlignUsingPeak.m</code>,
<code>AlignUsingH2O.m</code></td>
</tr>
<tr class="odd">
<td align="left">No alignment</td>
<td align="left"><code>none</code></td>
<td align="left">Do not perform any frequency or phase alignment between
transients.</td>
<td align="left">n/a</td>
</tr>
</tbody>
</table>
</div>
<div id="signal-averaging" class="section level2 hasAnchor">
<h2 class="hasAnchor">Signal averaging<a href="#signal-averaging"
class="anchor-section" aria-label="Anchor link to header"></a></h2>
<p>Gannet provides two methods for averaging individual transients
(selected in <code>GannetPreInitialise.m</code>): arithmetic averaging
(with outlier rejection) and weighted averaging (the default). The code
for the signal averaging routines can be found in
<code>SignalAveraging.m</code>.</p>
<div id="arithmetic-averaging" class="section level3 hasAnchor">
<h3 class="hasAnchor">Arithmetic averaging<a
href="#arithmetic-averaging" class="anchor-section"
aria-label="Anchor link to header"></a></h3>
<p>Arithmetic averaging is straightforward. All sequentially acquired
<span class="math inline">\(n\)</span> pairs of subspectra <span
class="math inline">\(x_i\)</span> (e.g., all edit-ON and edit-OFF
subspectra) are averaged using the arithmetic mean: <span
class="math inline">\(\bar{x} = \frac{1}{n}\sum_{i=1}^nx_i\)</span>.</p>
<p>Note that before the arithmetic averaging of subspectra, individual
transients are excluded based on the outlier rejection algorithm used
during frequency and phase alignment.</p>
</div>
<div id="weighted-averaging" class="section level3 hasAnchor">
<h3 class="hasAnchor">Weighted averaging<a href="#weighted-averaging"
class="anchor-section" aria-label="Anchor link to header"></a></h3>
<p>Weighted averaging down-weights individual difference subspectra that
are corrupted by signal artifacts — this is an important distinction
from traditional signal averaging. First, the difference between
sequentially acquired pairs (e.g., all edit-ON and edit-OFF subspectra)
is calculated. A similarity matrix <span
class="math inline">\(\mathbf{D}\in\mathbb{R}^{P{\times}P}\)</span> is
obtained by calculating the mean squared error between each real-valued
difference subspectrum <span class="math inline">\(p\)</span> and every
other real-valued difference subspectrum (in the range 1.8 to 3.4 ppm).
A similarity metric <span class="math inline">\(d_{p}\)</span> is
calculated as the column-wise median of <span
class="math inline">\(\mathbf{D}\)</span>. Normalized weights <span
class="math inline">\(w_{p}\)</span> are then derived, <span
class="math inline">\(w_{p} = d^{-2}_p/\sum{d^{-2}_p}\)</span>, and
applied to the difference pairs before summation.</p>
<p>Other algorithms to calculate weights can be found in
<code>SignalAveraging.m</code>. (Note: These have not been tested
extensively and should be considered experimental.)</p>
</div>
</div>
<div id="residual-water-removal" class="section level2 hasAnchor">
<h2 class="hasAnchor">Residual water removal<a
href="#residual-water-removal" class="anchor-section"
aria-label="Anchor link to header"></a></h2>
<p>If <code>water_removal</code> is set to 1 in
<code>GannetPreInitialise.m</code> (the default), the residual water
peak is removed from all difference spectra using an HSVD filter<span
class="citation"><sup><a
href="#ref-Barkhuijsen1987">6</a></sup></span>.</p>
<p><br></p>
<div id="references" class="section level3 unnumbered hasAnchor">
<h3 class="unnumbered hasAnchor">References<a href="#references"
class="anchor-section" aria-label="Anchor link to header"></a></h3>
<div id="refs" class="references csl-bib-body">
<div id="ref-An2013" class="csl-entry">
<div class="csl-left-margin">1. </div><div class="csl-right-inline">An
L, Willem van der Veen J, Li S, Thomasson DM, Shen J. <span
class="nocase">Combination of multichannel single-voxel MRS signals
using generalized least squares</span>. <em>Journal of Magnetic
Resonance Imaging</em>. 2013;37(6):1445-1450. doi:<a
href="https://doi.org/10.1002/jmri.23941">10.1002/jmri.23941</a></div>
</div>
<div id="ref-Klose1990" class="csl-entry">
<div class="csl-left-margin">2. </div><div
class="csl-right-inline">Klose U. <span class="nocase">In vivo proton
spectroscopy in presence of eddy currents</span>. <em>Magnetic Resonance
in Medicine</em>. 1990;14(1):26-30. doi:<a
href="https://doi.org/10.1002/mrm.1910140104">10.1002/mrm.1910140104</a></div>
</div>
<div id="ref-Near2015" class="csl-entry">
<div class="csl-left-margin">3. </div><div class="csl-right-inline">Near
J, Edden R, Evans CJ, Paquin R, Harris A, Jezzard P. <span
class="nocase">Frequency and phase drift correction of magnetic
resonance spectroscopy data by spectral registration in the time
domain</span>. <em>Magnetic Resonance in Medicine</em>.
2015;73(1):44-50. doi:<a
href="https://doi.org/10.1002/mrm.25094">10.1002/mrm.25094</a></div>
</div>
<div id="ref-Mikkelsen2018" class="csl-entry">
<div class="csl-left-margin">4. </div><div
class="csl-right-inline">Mikkelsen M, Saleh MG, Near J, et al. <span
class="nocase">Frequency and phase correction for multiplexed edited MRS
of GABA and glutathione</span>. <em>Magnetic Resonance in Medicine</em>.
2018;80(1):21-28. doi:<a
href="https://doi.org/10.1002/mrm.27027">10.1002/mrm.27027</a></div>
</div>
<div id="ref-Mikkelsen2020" class="csl-entry">
<div class="csl-left-margin">5. </div><div
class="csl-right-inline">Mikkelsen M, Tapper S, Near J, Mostofsky SH,
Puts NAJ, Edden RAE. <span class="nocase">Correcting frequency and phase
offsets in MRS data using robust spectral registration</span>. <em>NMR
in Biomedicine</em>. 2020;33(10):e4368. doi:<a
href="https://doi.org/10.1002/nbm.4368">10.1002/nbm.4368</a></div>
</div>
<div id="ref-Barkhuijsen1987" class="csl-entry">
<div class="csl-left-margin">6. </div><div
class="csl-right-inline">Barkhuijsen H, Beer R de, Ormondt D van. <span
class="nocase">Improved algorithm for noniterative time-domain model
fitting to exponentially damped magnetic resonance signals</span>.
<em>Journal of Magnetic Resonance</em>. 1987;73(3):553-557. doi:<a
href="https://doi.org/10.1016/0022-2364(87)90023-0">10.1016/0022-2364(87)90023-0</a></div>
</div>
</div>
</div>
</div>
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