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[DOC] ADNI-to-BIDS : add Field Mapping (FMAP) specificities #1199

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Jun 6, 2024
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[DOC] ADNI-to-BIDS : add Field Mapping (FMAP) specificities #1199

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985ef4d
Add FMAP specificities to doc
AliceJoubert Jun 3, 2024
01c7db7
Update conversion errors list
AliceJoubert Jun 3, 2024
8242f7f
Modify doc
AliceJoubert Jun 6, 2024
7e990fa
Small fix
AliceJoubert Jun 6, 2024
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Small fix 2
AliceJoubert Jun 6, 2024
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37 changes: 37 additions & 0 deletions docs/Converters/ADNI2BIDS.md
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Original file line number Diff line number Diff line change
Expand Up @@ -69,6 +69,7 @@ Currently, the modalities supported by our converter are:
| Florbetapir (AV45) PET (amyloid) | - | ✓ | ✓ |
| Florbetaben (FBB) PET (amyloid) | - | - | ✓ |
| Flortaucipir (AV1451) PET (tau) | - | - | ✓ |
| Field mapping (FMAP) | ✓ | ✓ | ✓ |
| Clinical data | ✓ | ✓ | ✓ |

To convert the imaging data to BIDS, a list of subjects with their sessions is first obtained from the `ADNIMERGE` spreadsheet.
Expand All @@ -85,6 +86,7 @@ Gradwarp, B1-inhomogeneity corrected and N3 bias field corrected images are sele
- **FLAIR** We select images containing 'FLAIR' in the sequence name, without multiplanar reconstruction (MPR).
- **fMRI** We select images containing 'MRI' in the sequence name, that are not multiband.
- **FDG, Amyloid and Tau PET** The images co-registered and averaged across time frames are selected.
- **FMAP** We select images referring to Field Mapping in their sequence name.

For all imaging modalities, the scans failing quality control (if it was performed) are discarded.

Expand All @@ -100,6 +102,11 @@ The user can easily modify this file if they want to convert additional clinical
For further details regarding clinica data, we recommend to look at the [ADNI Data Package
](https://adni.bitbucket.io/index.html) developed by the Alzheimer's Disease Neuroimaging Initiative.

??? failure "Warning : Field Mapping modality"
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I don't see this block in the rendered documentation: https://aramislab.paris.inria.fr/clinica/docs/public/PR-1199/Converters/ADNI2BIDS/

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I don't see it either, as well as all the changes I made in the last commit (ex : removing the "except for FMAP" part as suggested). Did I miss something I should have done for it to appear ?

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Indeed, there is a bug in the automatic documentation deployment pipeline... 😱 I'll open a PR to fix it.
I've updated the docs for this PR manually and the changes you made seem to be there.
Could you just double check that everything looks good in the rendered page ?

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I am not sure if it is still relevant now that you merged the PR but no I can't see the new changes in the rendered page. I will rebase a bit later :-)

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That's weird, I can see them.

The conversion of the Field Mapping modality was recently added to Clinica (v O.9.O) and is still experimental.
Bugs are expected to occur. In particular, it could not be tested how the process handles having more than one full run
(magnitude + phase images) acquired in the same session. Feel free to report bugs if you encounter any.

## Using the converter

The converter can be run with the following command line:
Expand Down Expand Up @@ -165,6 +172,7 @@ Due to the high computational time required for converting all the modalities of
- `PET_FDG` for Fluorodeoxyglucose (FDG) PET
- `PET_AMYLOID` for Pittsburgh compound B (PIB), Florbetapir (AV45) and Florbetaben (FBB) PET
- `PET_TAU` for Flortaucipir (AV1451) PET
- `FMAP` for Field Mapping

It is also possible to provide the path to a .txt file with the list of subjects to convert using the optional parameter `--subjects_list`.
This file must contain one subject identifier per line.
Expand Down Expand Up @@ -306,6 +314,23 @@ clinica convert adni-to-bids -h
- _Interslice distance varies in the volume (incompatible with NIfTI format):_
- Subject sub-ADNI128S2220 for session ses-M048

- **FMAP**
- _Image conversion generates an invalid output file (real/imaginary suffix):_
- Subject sub-ADNI002S1261 for session ses-M060 ; ses-M072 ; ses-M084 ; ses-M096
- Subject sub-ADNI006S4485 for session ses-M000 ; ses-M003 ; ses-M006 ; ses-M012 ; ses-M024 ; ses-M048
- _Unrecognized BIDS case for Field Mappings :_
- Subject sub-ADNI006S4485 for session ses-M078
- Subject sub-ADNI009S4388 for session ses-M003 ; ses-M006 ; ses-M012 ; ses-M024 ; ses-M048
- Subject sub-ADNI023S4115 for session ses-M000 ; ses-M003 ; ses-M006 ; ses-M012 ; ses-M024 ; ses-M048
- Subject sub-ADNI123S4127 for session ses-M000 ; ses-M012 ; ses-M024 ; ses-M036
- _Missing keys in .json files :_
- Subject sub-ADNI006S4485 for session ses-M090
- Subject sub-ADNI036S6088 for session ses-M012
- Subject sub-ADNI123S4127 for session ses-M084
- _Missing DICOMs slices :_
- Subject sub-ADNI023S4115 for session ses-M126
- Subject sub-ADNI177S6448 for session ses-M024

## Citing this converter in your paper

!!! cite "Example of paragraph:"
Expand Down Expand Up @@ -479,6 +504,18 @@ Known conversion exceptions are removed from the list.
- Functioning is the same as described above for FDG PET but we look for a different sequence ("AV1451 Co-registered, Averaged").
- The list of images for each subject is added to the list of images to convert.

??? abstract "Field Mapping (FMAP)""
The quality selection process for fMRI data described above is also used there :
- QC files are filtered to keep only entries corresponding to fMRI scans. We keep:
- `MAYOADIRL_MRI_IMAGEQC_12_08_15` rows containing `fMRI` as `series_type`,
- `MAYOADIRL_MRI_QUALITY_ADNI3` rows containing `EPB` as `SERIES_TYPE`.
- Resulting entries from both QC files are concatenated in one dataframe.
- The image sequence names in the `MRILIST.csv` file are filtered to keep only Field Mapping sequences.
- For each subject, since the `ADNIMERGE` and `MRILIST` files have different notations for the visits, a correspondence must be established.
For each subject, we pair the closest dates from the two files as the same visit (`visits_to_timepoints`).
- For each visit, the images are filtered to keep only images for the current visit.
- Since Field Mapping produces several images, all of them are considered and renamed according to BIDS specifications.

### Step 2: Path extraction

In this step the input is a pandas dataframe of images containing metadata.
Expand Down
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