Listed below are the data quality metrics that Gannet computes during
data processing, signal fitting, and tissue segmentation.
Linewidth
Linewidth is calculated as the full-width half-maximum (FWHM) (in Hz)
of fitted model signals. When reporting linewidths of datasets, you may
choose to use the FWHM of Cr, NAA, or the water reference (if a water
reference is provided).
Signal-to-noise ratio (SNR)
The SNR of fitted model signals is calculated as the amplitude of the
given modeled signal divided by twice the standard deviation of the
noise signal. To estimate the noise signal, Gannet takes two independent
segments of the OFF or DIFF spectrum (as appropriate to the modeled
signal of interest) between 8–9 ppm and 9–10 ppm, and detrends them
using a second-order polynomial function. The standard deviation of each
detrended noise segment is then calculated. Detrending is performed to
remove baseline artifacts (often related to the residual water signal).
The smaller of the two standard deviations is then used as the estimate
of noise, which is then multiplied by 2.
Detrended noise signal between either 8–9 or 9–10 ppm
(whichever produces a lower standard deviation) in the appropriate
spectrum (i.e., either the OFF or DIFF spectrum)
Frequency offsets (frequency drift/motion)
To estimate the degree of frequency offsets that result from
scanner-related frequency drift1 and participant motion2,
Gannet calculates the average frequency offset \(\overline{\Delta\delta_{0}}\)3.
This is calculated as the mean (over the course of the acquisition)
difference between the observed frequency of the residual water signal
in the pre-frequency-corrected subspectra and the nominal water
frequency \(\delta_{0}\) at 4.68 ppm
(4.8 ppm for room-temperature phantoms), or the nominal Cr frequency at
3.02 ppm for HERMES acquisitions. It should be noted that using the mean
of offset differences does not fully characterize frequency offsets but
is a useful heuristic.
Observed water or Cr frequency in each individual
subspectrum
\(\delta_{0}\)
Nominal water or Cr frequency
Fit error
When fitting signal functions to metabolite peaks, Gannet will also
estimate the error of the fit. This is defined as the standard deviation
of the residuals of the signal model fit divided by the signal model
amplitude and multiplied by 100 to give a percentage. For metabolite
peak fits, this is:
Since all metabolites are normalized to a reference signal (either Cr
or unsuppressed water), the fit error that really should be considered
(and reported) is the combined error of the metabolite and reference
signal model fits, which add up in quadrature. Formulaically, this is
defined as:
Hui
SCN, Mikkelsen M, Zöllner HJ, et al. Frequency
drift in MR spectroscopy at 3T. NeuroImage.
2021;241(21):118430. doi:10.1016/j.neuroimage.2021.118430
2.
Evans CJ, Puts NAJ, Robson SE, et al. Subtraction artifacts and frequency (Mis-)alignment in
J-difference GABA editing. Journal of Magnetic Resonance
Imaging. 2013;38(4):970-975. doi:10.1002/jmri.23923
3.
Mikkelsen M, Barker PB, Bhattacharyya PK, et
al. Big GABA: Edited MR spectroscopy at 24 research
sites. NeuroImage. 2017;159:32-45. doi:10.1016/j.neuroimage.2017.07.021