-[4. Fitting of the resultant spectrum](#4.-Fit-the-data)
-[5. Display the fitting reports](#5.-Display-the-result)
Will Clarke
June 2020
University of Oxford
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## 1. File conversion using spec2nii
Run spec2nii twix -v to establish the file contents, then using this information run spec2nii twix -e with the appropriate flags to extract the required scans.
The -j option causes a JSON side-car file to be generated. The -q flag suppresses text output.
This dataset uses a modified versions of the cmrr spectro package sequences on a siemens scanner. It has three sets of water reference scans. The first is taged as a phase correction, iscollected at the start of the main suppressed water scan and will be used for eddy current correction.
The second is collected in a separate scan with only the RF portion of the water suppression disabled. This is used for coil combination (and could be used for eddy current correction). The third is collected with the OVS and all aspects of the water suppression disabled. It therefore experiences eddy currents unlike all the other scans. It will be used for final concentration scaling.
Contents of file: meas_MID310_STEAM_metab_FID115673.dat
The file contains these evalinfo flags with dimensions and sizes as follows:
image : Col, Cha, Set [4096 32 64]
phasecor : Col, Cha [4096 32]
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From the final lines of the output of the first cell we can see that this "twix" file contains two groups of data.
The first is tagged as "image" and contains 64 repetitions (sets) of 4096 point FIDs collected on 32 channels.
The second has a single FID on each channel.
We now call spec2nii again specifying the group we want to extract each time. spec2nii will generate a NIfTI file for each repetition (set) with a size of 1x1x1x4096x32.
We repeat this for the water reference scans, extracting just the image data.
After conversion we have 64 averages from the suppressed water scan. The "Set" descriptor at the end of the file name arises from the indexing loop used at acquisition time to store the data. This is likely to vary from sequence to sequence, and is the choice of the pulse sequence programmer. Multiple looping indicies might be used for more complex data acquisition strategies.
We also have one ecc scan, and two "Reps" (another looping index) of each of the water reference scans. In this case both Set and Rep are used to indicate transients that should be averaged toghether.
Notice that each NIfTI file is paired witht a JSON sidecar file which contains useful information for further processing.
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## 2. Preprocessing of the data
### 2.1 Pre-processing using fsl_mrs_proc
In this section we will preprocess the data using individual subcommands of the fsl_mrs_proc script to preprocess step-by-step. This example could be used as a template to construct your own preprocessing script in either python or another batch processing script (e.g. bash).
#### Description of steps
1. Take averages of water references used for combination across files
2. Coil combine the metab data, the ecc data and the quantification data using the "comb" data as the reference.
3. Phase and frequency align the data where there are multiple transients.
4. Combine data across those transients (one in each file) by taking the mean.
5. Run eddy current correction using the appropriate reference.
6. In this data an additional FID point is collected before the echo centre. Remove this.
7. Run HLSVD on the data to remove the residual water in the water suppressed data.
6. Phase the data by a single peak as a crude zero-order phase correction.
Note that the "%%capture" and "%sx" commands are only required in this example notebook and are unecessary on the command line.
Note that the "%sx" command is only required in this example notebook and are unecessary on the command line.
In this section we will preprocess the data using a single preprocessing script which encapsulates all of the commands run above. Some of the commands are optional.
Run tissue segmentation on the packaged T1 data and mask using the SVS voxel. Here we provide a (partial) .anat file produced by [fsl_anat](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/fsl_anat) to speed up execution.
This step requires an installation of FSL compatible with fslpy. If you don't want to run this step the fitting step will run using a pre-packaged tissue segmentation file.
