# Specialized scans

This tutorial will cover how HippUnfold can be applied to non-standard

data including ex-vivo scans, super-high resolution data (eg. \<0.3mm

isotropic), non-MRI 3D imaging data, or scans where a corresponding

whole-brain T1w image is not available.

We will show how the available flags can be adapted for these use-cases

with several worked examples.

## Case 1: super high resolution

In this example, we have only a limited field of view covering the

hippocampus, and the resolution and contrast do not closely match the

training data of HippUnfold (0.3-1.0mm isotropic T1w, T2w, or DWI data).

This could be ex-vivo MRI data, or it could even be 3D microscopy data

as in our recent [3D BigBrain

publication](https://www.sciencedirect.com/science/article/pii/S105381191930919X).

Thus we don\'t expect HippUnfold\'s inbuilt UNet to be successful in

segmenting hippocampal tissue before unfolding, and we do not want to

downsample our data to accommodate HippUnfold\'s usual UNet and

unfolding workflow in `space-corobl` (which consists of 0.3mm isotropic

resampling cropped coronally-oblique to the hippocampus).

This will require manual segmentation of hippocampal grey matter, SRLM,

and neighbouring structures, though in the future we hope to include

models trained with higher resolution data (and contrasts more common in

ex-vivo scanning). This should be done according to the protocol

outlined

[here](https://ars.els-cdn.com/content/image/1-s2.0-S1053811917309977-mmc1.pdf)

or, more recently, the video example

[here](https://www.youtube.com/watch?v=mUQJ2GUcnLU&t=1s). This manual

segmentation file should have the `_dseg` suffix.

Here is an example of what the input directory might look like:

    exvivo/

    └── sub-001/

        ├── sub-001_hemi-R_desc-hippo_T2w.nii.gz

        └── sub-001_hemi-R_desc-hippo_dseg.nii.gz

This can be unfolded with the command:

    hippunfold . PATH_TO_OUTPUT_DIR participant --modality cropseg \

    --path_cropseg exvivo/sub-{subject}/sub-{subject}_hemi-{hemi}_desc-hippo_dseg.nii.gz \

    --hemi R --skip_inject_template_labels

Explanation: `--modality cropseg` informs HippUnfold that the input

manual segmentation should not be resampled and UNet does not need to be

run. Because of a limitation in bids parsing for the `hemi`

entity, we need to use the generic path input,

`--path_cropseg` in this case, making sure we use the

`{subject}` and `{hemi}` wildcards in the

filename. Output files will be named with `space-corobl` because

HippUnfold is coded to effectively treat all files as already being in

this space. We need the `--hemi R` to prevent HippUnfold looking for

both hemispheres. Finally, because this segmentation was performed

manually on very high resolution data, we can optionally consider

skipping the template shape injection step with

`--skip_inject_template_labels`. Template shape injection can fix minor

errors in segmentation from UNet or from an imperfect manual rater, at

the cost of smoothing out some details of the hippocampus due to the

fact that it uses deformable registration with inherent smoothness

contraints.

Note that because we are not resampling to the CITI168 template or using

UNet, the T2w image in this example is effectively not being used at

all. Instead, the provided manual segmentation makes up the basis for

unfolding.

## Case 2: one ex-vivo hemisphere 

In this example, we have a single hemisphere that was scanned ex-vivo at a

nearly standard resolution and T2w contrast. Because the resolution and

contrast are similar to the HippUnfold training data, we expect UNet

will work and so we don\'t need to perform manual segemntation. However,

due to gross deformations and the missing hemisphere, we don\'t expect

this sample to register well to the standard CITI168 template. Thus, we

will need to manually resample the image to the CITI168 template prior

to running HippUnfold, focusing in particular on aligning the

hippocampus. Once done, we may have a directory like this:

    PATH_TO_EXVIVO_DIR/

    └── sub-001/

        ├── sub-001_hemi-R_desc-exvivo_T2w.nii.gz

        ├── sub-001_hemi-R_affine-exvivo-to-CITI168_xfm.txt

        └── sub-001_hemi-R_desc-exvivo_space-CITI168_T2w.nii.gz

Note that only the last file is needed for unfolding:

    hippunfold . PATH_TO_OUTPUT_DIR participant --output_spaces corobl --hemi R --no_reg_template \

    --path_T2w PATH_TO_EXVIVO_DIR/sub-001/sub-001_hemi-R_desc-exvivo_space-CITI168_T2w.nii.gz \

    --output_spaces corobl

Here we need to use `--path_T2w` to specify which input should be used,

and `--no_reg_template` to specify that it is already in

`space-CITI168`. In this case, we also specified

`--output_spaces corobl`. This is not needed, but is useful when we are

interested in only the hippocampus as `space-corobl` is higher

resolution and cropped more nicely around the hippocampus then the

original scan, making it a good space to perform subsequent analyses.

Alternatively, outputs can be transformed back to the original space

using the inverted transform

`sub-001_hemi-R_affine-exvivo-to-CITI168_xfm.txt`.

This same usage could also be applied in a standard MRI case where no

T1w image is available.