The fields listed on this page are applicable to releases 3.3.0+ and are not necessarily correct for other versions of Gannet.

All output from Gannet analyses are saved in a structure (e.g., MRS_struct, or whatever name the user has chosen) that contains many fields and subfields. Listed below are lists of these fields and their descriptions. Note that not possible field that Gannet can generate during an analysis pipeline is shown for the sake of brevity.

MRS_struct

Field Description
version Structure containing release numbers of Gannet and the Gannet modules
ii During analysis, the current file being loaded; after analysis, the number of files that were loaded
metabfile Cell array containing the inputted metabolite data filenames
waterfile Cell array containing the inputted water reference data filenames

MRS_struct.p

Field Description
target The metabolite(s) targeted by the editing pulses
ON_OFF_order Order of the editing subexperiments
seqorig Origin of the Philips MEGA-PRESS or GE HERMES sequences
LB Exponential line-broadening applied (Hz)
water_ECC Whether eddy-current correction was applied to the water data
metab_ECC Whether eddy-current correction was applied to the metabolite data
water_removal Whether the residual water signal in DIFF spectrum was removed using HSVD
alignment Choice of shot-to-shot frequency-and-phase correction
use_prealign_ref Whether RobustSpecReg used the averaged pre-aligned subspectra as references to align the averaged post-aligned subspectra
fit_resid_water Whether the residual water signal in the OFF spectrum was fitted to calculate a water suppression factor
weighted_averaging Whether weighted signal averaging was implemented
HERMES Whether the data are HERMES data
HERCULES Whether the data are HERCULES data
PRIAM Whether the data are PRIAM data
phantom Whether the data are phantom data
join Whether the input files were joined
mat Whether the output structure was saved as MAT-file
csv Whether a CSV file of useful data was created
append Whether the PDF outputs were appended into single files
hide Whether the output figures were hidden
vendor Scanner vendor
reference Concentrations will be calculated relative to this; if a water reference is provided, concentrations will be calculated relative to both water and Cr, otherwise they will be calculated relative to Cr
numScans Total number of scans in the batch
numFilesPerScan When input files are joined, the number of files joined per scan
npoints Number of data points of each input file
nrows Number of data frames stored in each input file
nrows_water Number of data frames stored in each input water reference file
Navg Total number of averages collected at acquisition
Nwateravg Total number of averages collected at acquisition
TR Repetition time (ms)
TE Echo time (ms)
TR_water Repetition time (ms)
TE_water Echo time (ms)
LarmorFreq Larmor frequency (MHz)
sw Spectral width (Hz)
voxdim Array of voxel dimensions (mm)
voxoff Array of voxel position offset (mm)
voxang Array of voxel angulation (deg)
ZeroFillTo Processed spectra are zero-filled to this many data points
zf Zero-fill factor
dt Dwell time (s)
SpecRes Spectral resolution of the raw data (Hz/point)
SpecResNominal Nominal spectral resolution of the processed data (Hz/point)
Tacq Acquisition time (s)
weighted_averaging_method The algorithm used for weighted signal averaging

MRS_struct.fids

Field Description
data Raw time-domain metabolite data (dimensions: npoints \(\times\) nrows) of the last loaded dataset
data_water Raw time-domain water reference data (dimensions: npoints \(\times\) nrows_water) of the last loaded dataset
ON_OFF Array of the editing order of the last loaded dataset (1 = ON; 0 = OFF)
data_align Raw time-domain metabolite data of the last loaded dataset with frequency-and-phase correction applied

MRS_struct.spec

Field Description
vox1 (or whatever was set in MRS_struct.p.vox in GannetPreInitialise.m) This structure contains the complex frequency-domain spectral data for all input datasets
freq Frequency axis (ppm), calculated from the last loaded dataset
F0freq Cell array of the observed frequency of either residual water signal or the 3 ppm Cr signal for each average
F0freq2 Cell array of the observed frequency of the 3 ppm Cr signal for each average; this is used in RobustSpectralRegistration.m as the starting values for frequency during optimization
AllFramesFT Spectra of the last loaded dataset without frequency-and-phase correction applied
AllFramesFTrealign Spectra of the last loaded dataset with frequency-and-phase correction applied

MRS_struct.out

Field Description
AvgDeltaF0 Vector containing a metric of the amount of frequency offset observed during the acquisition, calculated as the mean difference between the observed frequency of the residual water signal (or the 3 ppm Cr signal) in the pre-frequency-corrected subspectra and the nominal water frequency at 4.68 ppm (or the nominal 3 ppm Cr signal if HERMES or GSH editing)
SpecReg Structure containing output from spectral registration
reject Cell array of binary integers indicating whether an individual transient should be rejected during signal averaging
signal_averaging Structure containing a cell array of the weighting factors applied if weighted signal averaging is used
vox1 (or whatever was set in MRS_struct.p.vox in GannetPreInitialise.m) This structure contains the output from the signal modeling, quality analysis, and quantification of all the signals of interest
QA Structure containing metrics calculated to quantify the performance of the tissue segmentation

MRS_struct.mask

Field Description
outfile Cell array of the output filenames of the voxel masks
img Cell array containing three-dimensional arrays representing the three orthogonal slices of the T1-weighted structural images that are displayed in the figure outputs
T1image Cell array of the input T1-weighted structural image filenames
img_montage Cell array containing three-dimensional arrays representing a montage of axial slices of the T1-weighted structural images overlaid by the tissue-segmented voxel masks that are displayed in the GannetQuantify.m figure output
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title: "Output structure attributes"
date: "Last updated: `r format(Sys.time(), '%B %d, %Y')`"
output:
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---

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knitr::opts_chunk$set(echo = TRUE)
```

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```

```{css, echo = FALSE}
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<br>

::: info
<i class="fa fa-info-circle" style="color: white"></i>&nbsp; The fields listed on this page are applicable to releases 3.3.0+ and are not necessarily correct for other versions of Gannet.
:::

All output from Gannet analyses are saved in a structure (e.g., `MRS_struct`, or whatever name the user has chosen) that contains many fields and subfields. Listed below are lists of these fields and their descriptions. Note that not possible field that Gannet can generate during an analysis pipeline is shown for the sake of brevity.

## MRS_struct

| <u>Field</u> | <u>Description</u> |
|:------------------|:----------------------------------------------------|
| `version` | Structure containing release numbers of Gannet and the Gannet modules |
| `ii` | During analysis, the current file being loaded; after analysis, the number of files that were loaded |
| `metabfile` | Cell array containing the inputted metabolite data filenames |
| `waterfile` | Cell array containing the inputted water reference data filenames |

## MRS_struct.p

| <u>Field</u> | <u>Description</u> |
|:------------------|:----------------------------------------------------|
| `target` | The metabolite(s) targeted by the editing pulses |
| `ON_OFF_order` | Order of the editing subexperiments | 
| `seqorig` | Origin of the Philips MEGA-PRESS or GE HERMES sequences |
| `LB` | Exponential line-broadening applied (Hz) |
| `water_ECC` | Whether eddy-current correction was applied to the water data |
| `metab_ECC` | Whether eddy-current correction was applied to the metabolite data |
| `water_removal` | Whether the residual water signal in DIFF spectrum was removed using HSVD |
| `alignment` | Choice of shot-to-shot frequency-and-phase correction |
| `use_prealign_ref` | Whether `RobustSpecReg` used the averaged pre-aligned subspectra as references to align the averaged post-aligned subspectra |
| `fit_resid_water` | Whether the residual water signal in the OFF spectrum was fitted to calculate a water suppression factor |
| `weighted_averaging` | Whether weighted signal averaging was implemented |
| `HERMES` | Whether the data are HERMES data |
| `HERCULES` | Whether the data are HERCULES data | 
| `PRIAM` | Whether the data are PRIAM data |
| `phantom` | Whether the data are phantom data |
| `join` | Whether the input files were joined |
| `mat` | Whether the output structure was saved as MAT-file |
| `csv` | Whether a CSV file of useful data was created |
| `append` | Whether the PDF outputs were appended into single files |
| `hide` | Whether the output figures were hidden |
| `vendor` | Scanner vendor |
| `reference` | Concentrations will be calculated relative to this; if a water reference is provided, concentrations will be calculated relative to both water and Cr, otherwise they will be calculated relative to Cr |
| `numScans` | Total number of scans in the batch |
| `numFilesPerScan` | When input files are joined, the number of files joined per scan |
| `npoints` | Number of data points of each input file |
| `nrows` | Number of data frames stored in each input file |
| `nrows_water` | Number of data frames stored in each input water reference file |
| `Navg` | Total number of averages collected at acquisition |
| `Nwateravg` | Total number of averages collected at acquisition |
| `TR` | Repetition time (ms) |
| `TE` | Echo time (ms) |
| `TR_water` | Repetition time (ms) |
| `TE_water` | Echo time (ms) |
| `LarmorFreq` | Larmor frequency (MHz) |
| `sw` | Spectral width (Hz) |
| `voxdim` | Array of voxel dimensions (mm) |
| `voxoff` | Array of voxel position offset (mm) |
| `voxang` | Array of voxel angulation (deg) |
| `ZeroFillTo` | Processed spectra are zero-filled to this many data points |
| `zf` | Zero-fill factor |
| `dt` | Dwell time (s) |
| `SpecRes` | Spectral resolution of the raw data (Hz/point) |
| `SpecResNominal` | Nominal spectral resolution of the processed data (Hz/point) |
| `Tacq` | Acquisition time (s) |
| `weighted_averaging_method` | The algorithm used for weighted signal averaging |

## MRS_struct.fids

| <u>Field</u> | <u>Description</u> |
|:------------------|:----------------------------------------------------|
| `data` | Raw time-domain metabolite data (dimensions: `npoints` $\times$ `nrows`) of the last loaded dataset |
| `data_water` | Raw time-domain water reference data (dimensions: `npoints` $\times$ `nrows_water`) of the last loaded dataset |
| `ON_OFF` | Array of the editing order of the last loaded dataset (`1` = ON; `0` = OFF) |
| `data_align` | Raw time-domain metabolite data of the last loaded dataset with frequency-and-phase correction applied |

## MRS_struct.spec

| <u>Field</u> | <u>Description</u> |
|:------------------|:----------------------------------------------------|
| `vox1` (or whatever was set in `MRS_struct.p.vox` in `GannetPreInitialise.m`) | This structure contains the complex frequency-domain spectral data for all input datasets |
| `freq` | Frequency axis (ppm), calculated from the last loaded dataset |
| `F0freq` | Cell array of the observed frequency of either residual water signal or the 3 ppm Cr signal for each average |
| `F0freq2` | Cell array of the observed frequency of the 3 ppm Cr signal for each average; this is used in `RobustSpectralRegistration.m` as the starting values for frequency during optimization |
| `AllFramesFT` | Spectra of the last loaded dataset without frequency-and-phase correction applied |
| `AllFramesFTrealign` | Spectra of the last loaded dataset with frequency-and-phase correction applied |

## MRS_struct.out

| <u>Field</u> | <u>Description</u> |
|:------------------|:----------------------------------------------------|
| `AvgDeltaF0` | Vector containing a metric of the amount of frequency offset observed during the acquisition, calculated as the mean difference between the observed frequency of the residual water signal (or the 3 ppm Cr signal) in the pre-frequency-corrected subspectra and the nominal water frequency at 4.68 ppm (or the nominal 3 ppm Cr signal if HERMES or GSH editing) |
| `SpecReg` | Structure containing output from spectral registration |
| `reject` | Cell array of binary integers indicating whether an individual transient should be rejected during signal averaging |
| `signal_averaging` | Structure containing a cell array of the weighting factors applied if weighted signal averaging is used |
| `vox1` (or whatever was set in `MRS_struct.p.vox` in `GannetPreInitialise.m`) | This structure contains the output from the signal modeling, quality analysis, and quantification of all the signals of interest |
| `QA` | Structure containing metrics calculated to quantify the performance of the tissue segmentation |

## MRS_struct.mask

| <u>Field</u> | <u>Description</u> |
|:------------------|:----------------------------------------------------|
| `outfile` | Cell array of the output filenames of the voxel masks |
| `img` | Cell array containing three-dimensional arrays representing the three orthogonal slices of the <i>T</i><sub>1</sub>-weighted structural images that are displayed in the figure outputs |
| `T1image` | Cell array of the input <i>T</i><sub>1</sub>-weighted structural image filenames |
| `img_montage` | Cell array containing three-dimensional arrays representing a montage of axial slices of the <i>T</i><sub>1</sub>-weighted structural images overlaid by the tissue-segmented voxel masks that are displayed in the `GannetQuantify.m` figure output |






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