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[FIX] Revise preprocessed DWI data example #37

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[FIX] Revise preprocessed DWI data example #37

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51 changes: 20 additions & 31 deletions src/05-derivatives/05-diffusion-derivatives.md
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## Preprocessed diffusion-weighted images

Multiple different versions of preprocessing can be stored for the same source
data. To distinguish them from each other, the `desc` filename keyword can be
used. Details of preprocessing performed for each variation of the processing
should be included in the pipeline documentation.
- As with [raw diffusion imaging data](../04-modality-specific-files/01-magnetic-resonance-imaging-data.md#required-gradient-orientation-information),
inclusion of gradient orientation information is REQUIRED. While
[the inheritance principle](../02-common-principles.md#the-inheritance-principle)
applies, it is common for DWI preprocessing to include rotation of
gradient orientations according to subject motion, so series-specific
gradient information is typically expected.

- As per [file naming conventions for BIDS Derivatives](01-introduction.md#file-naming-conventions),
preprocessed DWI data must not possess the same name as that of the raw
DWI data. It is RECOMMENDED to disambiguate through use of the key-value
"`_desc-preproc`".

- As per [common data types](02-common-data-types.md) for derivative data, a
JSON sidecar file is REQUIRED due to the REQUIRED `SkullStripped` field.

Template:

```Text
<pipeline_name>/
sub-<participant_label>/
dwi/
<source_keywords>[_space-<space>][_desc-<label>]_dwi.nii[.gz]
<source_keywords>[_space-<space>][_desc-<label>]_dwi.bvals
<source_keywords>[_space-<space>][_desc-<label>]_dwi.bvecs
<source_keywords>[_space-<space>][_desc-<label>]_dwi.json
<source_keywords>[_space-<space>]_desc-preproc_dwi.nii[.gz]
<source_keywords>[_space-<space>]_desc-preproc_dwi.bvals
<source_keywords>[_space-<space>]_desc-preproc_dwi.bvecs
<source_keywords>[_space-<space>]_desc-preproc_dwi.json
```

The JSON sidecar file is REQUIRED (due to the REQUIRED `SkullStripped` field -
see [Common Data Types](02-common-data-types.md)), and MAY additionally be used to
store information about what preprocessing options were used (for example
whether denoising was performed, corrections applied for field inhomogeneity /
gradient non-linearity / subject motion / eddy currents, etc.).

Additional reserved JSON metadata fields:

| **Key name** | **Description** |
| -------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| Denoising | OPTIONAL. String. Denoising method |
| GibbsRingingCorrection | OPTIONAL. Boolean. Removal of Gibbs ringing artifacts |
| MotionCorrection | OPTIONAL. String. Motion correction; allowed values: `none`, `volume`, `slice` |
| EddyCurrentCorrection | OPTIONAL. String. Eddy current distortion correction; reserved values: `none`, `linear`, `quadratic`, `cubic` |
| IntensityNormalizationMethod | OPTIONAL. String. Method (if any) used for intensity normalization |
| FieldInhomogeneityEstimation | OPTIONAL. String. Method (if any) used for estimation of the B0 inhomogeneity field; reserved values: `multiecho`, `phaseencode`, `registration` |
| FieldInhomogeneityCorrection | OPTIONAL. String. Correction for geometric distortions arising from B0 magnetic field inhomogeneity; reserved values: `none`, `static`, `dynamic` |
| GradientNonLinearityGeometryCorrection | OPTIONAL. Boolean. Correction for geometric distortions arising from non-linearity of gradients |
| GradientNonLinearityQSpaceCorrection | OPTIONAL. Boolean. Correction for spatial inhomogeneity of diffusion sensitisation gradient strength |
| SliceDropoutDetection | OPTIONAL. Boolean. Detection of signal dropout in acquired slices during pre-processing |
| SliceDropoutReplacement | OPTIONAL. Boolean. Replacement of image data within slices containing signal dropout with predicted values |
| BiasFieldCorrectionMethod | OPTIONAL. String. Method (if any) used for correction of B1 RF field inhomogeneity |

## Diffusion models

Diffusion MRI can be modeled using various paradigms to extract more
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