phcp-reco

Reconstruction scripts for post-mortem multi-quantitative fetal brain MRI at 11.7 teslas

Installation

Dependencies:

• Python 3.10
• FSL (tested with version 6.0.7.13)
• ANTS (tested with version 2.5.4)
• Gkg (locally distributed Singularity containers)

```shell git clone https://github.com/neurospin/phcp-reco.git

Installation in a virtual environment

cd phcp-reco python3 -m venv venv/ . venv/bin/activate pip install -e . ```

You may need to adapt the contents of phcp/config.py to suit your local set-up.

Data layout

Naming of input data

Each block should be assigned a short BlockName to identify it, based on its anatomical location, e.g. Inf, Mid, Sup, InfLat, SupMed, etc.

Each FOV within a block should be assigned a FovName that starts with BlockName and further specifies the position of the FOV within the block, e.g. InfAnt, InfPos, etc.

Each imaging session corresponds to a single FOV and should be named with a concatenation of the acquisition date in YYYYMMDD format, and FovName, e.g. 20250101InfPos.

Source data

The images of each field of view are first exported from the console in raw Bruker format and DICOM. The DICOM data is used for mostly everything, except that some acquisition parameters cannot be found in the DICOM metadata, namely the diffusion b-values and b-vectors, as well as the receiver gain. In those cases, we resort to reading them from the Bruker files acqp, method and visu_pars.

fov/sourcedata └── sub-{subjectID} └── ses-{sessionID} └── {ScanNumber} ├── acqp ├── method └── pdata └── 1 └── dicom └── MRIm*.dcm

The DICOM files are converted to BIDS format. TODO: document the conversion process

• dcm2niix
• Parse Bruker raw data and write bvec, bval, ReceiverGain

Raw data

The raw data is organized using the following BIDS layout, where each BIDS “session” corresponds to a given field of view:

fov/rawdata └── sub-{subjectID} └── ses-{sessionID} ├── anat │ ├── sub-{subjectID}_ses-{sessionID}_echo-{echo}_MEGRE.json │ ├── sub-{subjectID}_ses-{sessionID}_echo-{echo}_MEGRE.nii.gz │ ├── sub-{subjectID}_ses-{sessionID}_echo-{echo}_MESE.json │ ├── sub-{subjectID}_ses-{sessionID}_echo-{echo}_MESE.nii.gz │ ├── sub-{subjectID}_ses-{sessionID}_flip-{fa}_VFA.json │ ├── sub-{subjectID}_ses-{sessionID}_flip-{fa}_VFA.nii.gz │ ├── sub-{subjectID}_ses-{sessionID}_T2w.json │ └── sub-{subjectID}_ses-{sessionID}_T2w.nii.gz ├── dwi │ ├── sub-{subjectID}_ses-{sessionID}_acq-{bval}_run-{run}_dwi.json │ ├── sub-{subjectID}_ses-{sessionID}_acq-{bval}_run-{run}_dwi.nii.gz └── fmap ├── sub-{subjectID}_ses-{sessionID}_TB1AFI.json └── sub-{subjectID}_ses-{sessionID}_TB1AFI.nii.gz

Header-flipped raw data

The spatial transformation contained in the DICOM metadata, and thus Nifti rawdata, does not match the anatomical axes. The reason is that the ParaVision software does not allow us to input the real orientation of the tissue block in the scanner, so we default to choosing "head first supine" (HFS). Therefore, prior to any further processing we flip the the Nifti header transforms, so that the axes of the new "scanner-based" referential correspond to sensible anatomical orientations. This is done so that visualizing the images the usual viewers will display the brain in a sensible orientation.

The phcp-header-flip script will mirror the BIDS structure of the provided rawdata in a new headerfliprawdata directory.

Usage

shell phcp-header-flip \ --sessionDirectory fov/rawdata/sub-${sub}/ses-${ses} \ --outputDirectory fov/headerfliprawdata

Outputs

fov/headerfliprawdata └── sub-{subjectID} └── ses-{sessionID} ├── anat │ ├── sub-{subjectID}_ses-{sessionID}_echo-{echo}_MEGRE.json │ ├── sub-{subjectID}_ses-{sessionID}_echo-{echo}_MEGRE.nii.gz │ ├── sub-{subjectID}_ses-{sessionID}_echo-{echo}_MESE.json │ ├── sub-{subjectID}_ses-{sessionID}_echo-{echo}_MESE.nii.gz │ ├── sub-{subjectID}_ses-{sessionID}_flip-{fa}_VFA.json │ ├── sub-{subjectID}_ses-{sessionID}_flip-{fa}_VFA.nii.gz │ ├── sub-{subjectID}_ses-{sessionID}_T2w.json │ └── sub-{subjectID}_ses-{sessionID}_T2w.nii.gz ├── dwi │ ├── sub-{subjectID}_ses-{sessionID}_acq-{bval}_run-{run}_dwi.json │ ├── sub-{subjectID}_ses-{sessionID}_acq-{bval}_run-{run}_dwi.nii.gz └── fmap ├── sub-{subjectID}_ses-{sessionID}_TB1AFI.json └── sub-{subjectID}_ses-{sessionID}_TB1AFI.nii.gz

The field-of-view processing pipeline

Early pre-processing

Early pre-processing of the raw data is performed with the phcp-bruker-preprocessing script, which includes:

• extraction of the b-vectors and b-values from the Bruker source data (acqp and method, visu_pars files) into standard BIDS-format .bvec and .bval files;
• extraction of receiver gain values from the Bruker source data (acqp file) for the diffusion and VFA images, and homogenization of the values to correct for potential receiver gain differences;
• concatenation of the segmented diffusion scans and of the individual VFA scans.

Inputs

The phcp-bruker-preprocessing script uses a few JSON files as configuration. You have to create a description directory, which can be stored under derivatives/gkg-Pipeline/PreprocessingDescriptions:

derivatives/gkg-Pipeline/PreprocessingDescriptions ├── sub-{subjectID} │ ├── RG_description.json │ └── sub-{subjectID}_ses-{sessionID}_description.json └── sub-{subjectID}.json

• RG_description.json contains a list of sessions with the list of modalities to be processed for each: json { "sub-{subjectID}_ses-{sessionID}": [ "b1500", "b4500", "b8000", "VFA" ] }
• sub-{subjectID}_ses-{sessionID}_description.json contains the list of Bruker scan numbers associated with each run of the segmented diffusion, for example::

json { "b1500": [97, 98], "b4500": [91, 92, 93, 94, 95, 96], "b8000": [82, 83, 84, 85, 86, 87, 88, 89, 90] }

Additionally, the “subject file”, whose name is free but suggested to be in the form sub-{subjectID}.json, contains the list of subjects/sessions to be processed in the following form:

json { "sub-{subjectID}": ["ses-{sessionID}"] }

Usage

shell phcp-brucker-preprocessing \ --sourcedata fov/sourcedata \ --rawdata fov/headerfliprawdata \ --derivatives fov/derivatives \ --subject derivatives/gkg-Pipeline/PreprocessingDescriptions/sub-${sub}.json \ --descriptions derivatives/gkg-Pipeline/PreprocessingDescriptions/sub-${sub}

Outputs

fov/headerfliprawdata └── sub-{subjectID} └── ses-{sessionID} └── dwi ├── sub-{subjectID}_ses-{sessionID}_acq-{bval}_run-{run}_dwi.bvec └── sub-{subjectID}_ses-{sessionID}_acq-{bval}_run-{run}_dwi.bval fov/derivatives/RGCorrection └── sub-{subjectID} └── ses-{sessionID} ├── RG_values.json └── sub-{subjectID}_ses-{sessionID}_VFA.nii.gz fov/derivatives/gkg-Pipeline/ └── sub-{subjectID} └── ses-{sessionID} ├── b1500 │ └── 01-Materials │ ├── sub-{subjectID}_ses-{sessionID}_acq-b1500_dwi.bval │ ├── sub-{subjectID}_ses-{sessionID}_acq-b1500_dwi.bvec │ └── sub-{subjectID}_ses-{sessionID}_acq-b1500_dwi.nii.gz ├── b4500 │ └── 01-Materials │ ├── sub-{subjectID}_ses-{sessionID}_acq-b4500_dwi.bval │ ├── sub-{subjectID}_ses-{sessionID}_acq-b4500_dwi.bvec │ └── sub-{subjectID}_ses-{sessionID}_acq-b4500_dwi.nii.gz └── b8000 └── 01-Materials ├── sub-{subjectID}_ses-{sessionID}_acq-b8000_dwi.bval ├── sub-{subjectID}_ses-{sessionID}_acq-b8000_dwi.bvec └── sub-{subjectID}_ses-{sessionID}_acq-b8000_dwi.nii.gz

B1 mapping

The transmit B1 field inhomogeneities are mapped using an Actual Flip Angle sequence. The following script reconstructs the B1 map from the AFI data. Parameters (TR1 and TR2) are not in the JSON metadata, they have to be passed manually but default values match the protocol.

Inputs

fov/headerfliprawdata └── sub-{subjectID} └── ses-{sessionID} └── fmap ├── sub-{subjectID}_ses-{sessionID}_TB1AFI.json └── sub-{subjectID}_ses-{sessionID}_TB1AFI.nii.gz

Usage

shell mkdir -p fov/derivatives/fov-reconstructed/sub-${sub}/ses-${ses}/fmap phcp-fov-afi-b1mapping \ fov/headerfliprawdata/sub-${sub}/ses-${ses}/fmap/sub-${sub}_ses-${ses}_TB1AFI.nii.gz \ fov/derivatives/fov-reconstructed/sub-${sub}/ses-${ses}/fmap/sub-${sub}_ses-${ses}_TB1map.nii.gz

Outputs

fov/derivatives/fov-reconstructed └── sub-{subjectID} └── ses-{sessionID} └── fmap └── sub-{subjectID}_ses-{sessionID}_TB1map.nii.gz

T2* mapping

Inputs

fov/headerfliprawdata └── sub-{subjectID} └── ses-{sessionID} └── anat ├── sub-{subjectID}_ses-{sessionID}_echo-{echo}_MEGRE.json └── sub-{subjectID}_ses-{sessionID}_echo-{echo}_MEGRE.nii.gz

Usage

shell mkdir -p fov/derivatives/T2starmapping/sub-${sub}/ses-${ses}/01-Materials fslmerge -t fov/derivatives/T2starmapping/sub-${sub}/ses-${ses}/01-Materials/t2star-mge.nii.gz fov/headerfliprawdata/sub-${sub}/ses-${ses}/anat/*_MEGRE.nii.gz mkdir -p fov/derivatives/T2starmapping/sub-${sub}/ses-${ses}/02-Results phcp-t2star-relaxometry \ --input fov/derivatives/T2starmapping/sub-${sub}/ses-${ses}/01-Materials \ --mge fov/headerfliprawdata/sub-${sub}/ses-${ses}/anat/'*_MEGRE.json' \ --outputDirectory fov/derivatives/T2starmapping/sub-${sub}/ses-${ses}/02-Results

Outputs

fov/derivatives/T2starmapping └── sub-{subjectID} └── ses-{sessionID} └── 02-Results ├── proton-density.nii.gz ├── fitted-mge.nii.gz ├── T2star.nii.gz └── T2starConfidenceMap.nii.gz

T2 mapping

Inputs

fov/headerfliprawdata └── sub-{subjectID} └── ses-{sessionID} └── anat ├── sub-{subjectID}_ses-{sessionID}_echo-{echo}_MESE.json └── sub-{subjectID}_ses-{sessionID}_echo-{echo}_MESE.nii.gz

Usage

shell mkdir -p fov/derivatives/T2mapping/sub-${sub}/ses-${ses}/01-Materials fslmerge -t fov/derivatives/T2mapping/sub-${sub}/ses-${ses}/01-Materials/t2-msme.nii.gz fov/headerfliprawdata/sub-${sub}/ses-${ses}/anat/*_MESE.nii.gz mkdir -p fov/derivatives/T2mapping/sub-${sub}/ses-${ses}/02-Results phcp-t2-relaxometry \ --input fov/derivatives/T2mapping/sub-${sub}/ses-${ses}/01-Materials \ --msme fov/headerfliprawdata/sub-${sub}/ses-${ses}/anat/'*_MESE.json' \ --outputDirectory fov/derivatives/T2mapping/sub-${sub}/ses-${ses}/02-Results

Outputs

fov/derivatives/T2mapping └── sub-{subjectID} └── ses-{sessionID} └── 02-Results ├── proton-density.nii.gz ├── fitted-msme.nii.gz ├── T2.nii.gz └── T2ConfidenceMap.nii.gz

T1 mapping

Inputs

fov/headerfliprawdata └── sub-{subjectID} └── ses-{sessionID} └── anat └── sub-{subjectID}_ses-{sessionID}_flip-{fa}_VFA.json fov/derivatives/RGCorrection └── sub-{subjectID} └── ses-{sessionID} └── sub-{subjectID}_ses-{sessionID}_VFA.nii.gz fov/derivatives/fov-reconstructed └── sub-{subjectID} └── ses-{sessionID} └── fmap └── sub-{subjectID}_ses-{sessionID}_TB1map.nii.gz

Usage

shell mkdir -p fov/derivatives/T1mapping/sub-${sub}/ses-${ses}/01-Materials cp fov/derivatives/RGCorrection/sub-${sub}/ses-${ses}/sub-${sub}_ses-${ses}_VFA.nii.gz \ fov/derivatives/T1mapping/sub-${sub}/ses-${ses}/01-Materials/t1map.nii.gz cp fov/derivatives/fov-reconstructed/sub-${sub}/ses-${ses}/fmap/sub-${sub}_ses-${ses}_TB1map.nii.gz \ fov/derivatives/T1mapping/sub-${sub}/ses-${ses}/01-Materials/b1.nii.gz mkdir -p fov/derivatives/T1mapping/sub-${sub}/ses-${ses}/02-Results phcp-t1-relaxometry \ --input fov/derivatives/T1mapping/sub-${sub}/ses-${ses}/01-Materials \ --vfa fov/headerfliprawdata/sub-${sub}/ses-${ses}/anat/'*_VFA.json' \ --outputDirectory fov/derivatives/T1mapping/sub-${sub}/ses-${ses}/02-Results

Outputs

fov/derivatives/T1mapping └── sub-{subjectID} └── ses-{sessionID} └── 02-Results ├── proton-density.nii.gz ├── fitted-vfa.nii.gz ├── T1.nii.gz ├── T1_rec-unbiased.nii.gz └── T1ConfidenceMap.nii.gz

Diffusion imaging pipeline

The diffusion imaging pipeline takes care of the whole chain, from the pre-processing (artefact correction) to the diffusion models (DTI, NODDI, and tractography). JSON files are used to configure which steps to run and pass parameters.

Inputs

The diffusion data itself, corrected for receiver gain, is read from this hierarchy (see phcp-bruker-preprocessing above):

fov/derivatives/gkg-Pipeline/ └── sub-{subjectID} └── ses-{sessionID} ├── b1500 │ └── 01-Materials │ ├── sub-{subjectID}_ses-{sessionID}_acq-b1500_dwi.bval │ ├── sub-{subjectID}_ses-{sessionID}_acq-b1500_dwi.bvec │ └── sub-{subjectID}_ses-{sessionID}_acq-b1500_dwi.nii.gz ├── b4500 │ └── 01-Materials │ ├── sub-{subjectID}_ses-{sessionID}_acq-b4500_dwi.bval │ ├── sub-{subjectID}_ses-{sessionID}_acq-b4500_dwi.bvec │ └── sub-{subjectID}_ses-{sessionID}_acq-b4500_dwi.nii.gz └── b8000 └── 01-Materials ├── sub-{subjectID}_ses-{sessionID}_acq-b8000_dwi.bval ├── sub-{subjectID}_ses-{sessionID}_acq-b8000_dwi.bvec └── sub-{subjectID}_ses-{sessionID}_acq-b8000_dwi.nii.gz

The pipeline is configured using three JSON files:

derivatives/gkg-Pipeline/ ├── PreprocessingDescriptions │ └── sub-{subjectID}.json └── GkgPipelineDescriptions ├── GkgPipeline_description_{subjectID}.json └── tasks.json

• sub-{subjectID}.json is the “subject file” described above in the preprocessing section.

• GkgPipeline_description_${subjectID}.json contains a list of sessions with the list of modalities to be processed for each: json { "sub-{subjectID}_ses-{sessionID}": [ "b1500", "b4500", "b8000" ] }

• tasks.json contains the list of steps that should be run:

json { "OrientationAndBValueFileDecoding": 1, "NonLocalMeansFiltering": 1, "QSpaceSamplingAddition": 1, "Morphologist": 1, "OutlierCorrection": 1, "EddyCurrentCorrection": 1, "LocalModelingDTI": 1, "LocalModelingQBI": 0 }

Usage

shell phcp-dffusion-pipeline \ --subjectJsonFileName fov/derivatives/gkg-Pipeline/PreprocessingDescriptions/sub-${sub}.json \ --taskJsonFileName fov/derivatives/gkg-Pipeline/GkgPipelineDescriptions/tasks.json \ --gkgpipelineJsonFilename fov/derivatives/GkgPipelineDescriptions/GkgPipeline_description_${sub}.json\ --outputDirectory fov/derivatives/gkg-Pipeline/

Outputs

fov/derivatives/gkg-Pipeline/ └── sub-{subjectID} └── ses-{sessionID} └── b1500 / b4500 / b8000 / multishell ├── 02-OrientationAndBValueFileDecoding ├── 03-NonLocalMeansFiltering ├── 04-QSpaceSamplingAddition ├── 05-MorphologistPipeline ├── 06-OutlierCorrection ├── 07-EddyCurrentAndMotionCorrection ├── 08-LocalModeling-DTI ├── 09-LocalModeling-QBI └── ...

Outputs of the field-of-view processing pipeline

fov/derivatives/fov-reconstructed └── sub-{subjectID} └── ses-{sessionID} └── fmap └── sub-{subjectID}_ses-{sessionID}_TB1map.nii.gz

The reconstruction pipeline

The reconstruction pipeline consists of: 1. Obtaining the deformation fields that bring all fields of view into spatial correspondence. The registration strategy consists of three steps described below: intra-FOV registration, inter-FOV registration, and inter-block registration. 2. Applying these deformation fields to each field of view, and merging the data into a single image.

Intra-FOV registration

Intra-FOV registration aims to align each modality to the modality of reference (T2-weighted). The main steps are:

1. Placing the modality of reference (T2-weighted) in 'fixed' folder and all other modalities inside the 'moving' folder;
2. Running the phcp-intrafov-registration script, preforming format conversion when needed (GIS to NIfTI), bias correction and intra-FOV registration.

Inputs of intra-FOV registration

The inputs are the two main folders ('fixed' and 'moving') containing modalities to register. derivatives/Registration/sub-{subjectID}/IntraFOV_Registration/{FovName}/ ├── fixed │ └── sub-{subjectID}_ses-{sessionIDFOV1}_T2w.nii.gz └── moving ├── {modality-prefix}_{FovName1}.nii.gz or .ima ├── {modality-prefix}_{FovName2}.nii.gz or .ima └── ...

Rename your moving using the name of the modality as the first prefix (ex: VFA_flip-90.nii.gz, MSME_echo-1.nii.gz, ...).

Usage

shell phcp-intrafov-registration --verbose \ -i derivatives/Registration/sub-${sub}/IntraFOV_Registration/${FovName}

Outputs of intra-FOV registration

Intra-FOV creates transformation files in the 'transformfiles' folder, which is created automatically. Transformation files will have the following name architecture : `{modality-prefix}ToT2w{object}.{extension}.objectrefers to the type of file created and can take the following values:0GenericAffine,1Warp,1InverseWarp,Regis,INV_Regis. Andextensioncan bematif it is a linear transformation file andnii.gz` if it is an NIfTI image such as the warped image or the deformation field.

Inter-FOV registration

Inter-FOV registration aims to reconstruct a whole-block image from its constituent FOVs. The main steps are:

1. Placing the input data and creating a JSON file describing the input FOVs and the approximate translation between them;
2. Running the phcp-interfov-registration script, which performs bias correction, denoising, gradient non-linearity correction, the inter-FOV translation, runs the actual registration, and initializes the geometry of the whole-block space;
3. Creating a transformation file for placing the reference (most posterior) FOV into the whole-block space, and optionally fine-tuning the fusion parameters;
4. Running phcp-interfov-fusion to apply the registration fuse all FOVs, yielding a whole-block T2-weighted image.

Inputs of inter-FOV registration

The inputs are the pre-calculated deformation field for correction of gradient non-linearity (WarpHeaderFile_FWHM1.nii.gz), a Description.json file described below, and the T2-weighted images of all FOVs. They should be prepared according to this layout:

derivatives/Registration/sub-{subjectID}/InterFOV_Registration/{BlockName}/ ├── 01-Materials │ ├── sub-{subjectID}_ses-{sessionIDFOV1}_T2w.nii.gz │ ├── sub-{subjectID}_ses-{sessionIDFOV2}_T2w.nii.gz │ ├── sub-{subjectID}_ses-{sessionIDFOV3}_T2w.nii.gz │ ├── ... │ └── WarpHeaderFile_FWHM1.nii.gz └── Description.json

A Description.json file should be prepared, listing the FOVs of the block and their relative translation values. In the example below, a block was acquired in 3 FOVs with 30 mm translation between each FOV. Note that the reference FOV (the most posterior FOV) needs to be identified with a 0.0 translation, and other FOVs must have non-zero translation.

json { "sub-{subjectID}_ses-{sessionIDFOV1}_T2w": 0.0, "sub-{subjectID}_ses-{sessionIDFOV2}_T2w": 30.0, "sub-{subjectID}_ses-{sessionIDFOV3}_T2w": 60.0, }

Usage

shell phcp-interfov-registration --verbose \ -i derivatives/Registration/sub-{subjectID}/InterFOV_Registration/${BlockName} \ -j derivatives/Registration/sub-{subjectID}/InterFOV_Registration/${BlockName}/Description.json

Outputs of inter-FOV registration

Each FOV is denoised with NLMF (non-local means filtering) and debiased with N4, corrected for gradient non-linearities, and the translation is applied to the header, yielding a preprocessed output with _NLMF_N4_rec-unwarp suffix. Note that the deformation fields are estimated and saved to the 03-TransformFiles directory, but they are not applied to the data at this stage: therefore, quality control takes place after the next step of inter-FOV fusion.

derivatives/Registration/sub-{subjectID}/InterFOV_Registration/{BlockName}/ ├── 02-Preparation │ ├── sub-{subjectID}_ses-{sessionIDFOV1}_T2w_NLMF_N4_rec-unwarp.nii.gz │ ├── sub-{subjectID}_ses-{sessionIDFOV2}_T2w_NLMF_N4_rec-unwarp.nii.gz │ ├── sub-{subjectID}_ses-{sessionIDFOV3}_T2w_NLMF_N4_rec-unwarp.nii.gz │ ├── sub-{subjectID}_ses-{sessionIDFOV1}_T2w_sub-{subjectID}_ses-{sessionIDFOV1}_T2w.json │ ├── sub-{subjectID}_ses-{sessionIDFOV2}_T2w_sub-{subjectID}_ses-{sessionIDFOV3}_T2w.json │ └── ... ├── 03-TransformFiles │ └── ... └── IntermediateSpace.nii.gz

Inputs of inter-FOV fusion

The previous process creates a reference geometry for the whole-block image, IntermediateSpace.nii.gz, but that reference is not derived from the FOV geometry. Therefore, the image of the reference FOV (normally the most posterior FOV) must be manually registered into that intermediate space. This can be done by: 1. Loading IntermediateSpace.nii.gz in ITK-SNAP as a main image, using the Jet colormap so that the background of that image appears blue on the black background (beware that this uniform image causes a crash of ITK-SNAP if Contrast inspector or Auto adjust constrast are used). 2. Loading 02-Preparation/sub-{subjectID}_ses-{sessionIDRefFOV}_T2w_NLMF_N4_rec-unwarp.nii.gz as an additional image in overlay mode. 3. Using the Registration module of ITK-SNAP in Rigid mode, to perform manual alignment of the reference FOV at the extremity of the intermediate space. Make sure to hit Zoom to fit so that you can visualize the full whole-block space. 4. Saving the resulting transformation matrix into a file named refFOV_to_intermediateSpace.txt.

Inter-FOV fusion uses the following files:

derivatives/Registration/sub-{subjectID}/InterFOV_Registration/{BlockName}/ ├── 01-Materials │ ├── sub-{subjectID}_ses-{sessionIDFOV1}_T2w_NLMF_N4_rec-unwarp.nii.gz │ ├── sub-{subjectID}_ses-{sessionIDFOV2}_T2w_NLMF_N4_rec-unwarp.nii.gz │ ├── sub-{subjectID}_ses-{sessionIDFOV3}_T2w_NLMF_N4_rec-unwarp.nii.gz │ ├── ... ├── 03-TransformFiles │ └── ... ├── Description.json ├── IntermediateSpace.nii.gz └── refFOV_to_intermediateSpace.txt

Usage

shell phcp-interfov-fusion \ -i derivatives/Registration/sub-{subjectID}/InterFOV_Registration/${BlockName} \ -j derivatives/Registration/sub-{subjectID}/InterFOV_Registration/${BlockName}/Description.json

Outputs of inter-FOV fusion

derivatives/Registration/sub-{subjectID}/InterFOV_Registration/{BlockName}/ ├── 04-FovsDistribution │ └── ... └── Reconstructed_block.nii.gz

You should check the quality of the reconstructed block at this stage, and make adjustments to the fusion parameters if necessary (currently this requires editing the scripts themselves).

Inter-block registration

The registration strategy is described in the data paper, but the details of registration can differ for different specimens. As a result, no registration scripts are distributed at the moment.

Concatenation of transformations (optional)

The phcp-concat-transforms script concatenates a series of linear and non-linear transformations applied to a single field of view (FOV), producing two key output files: - total_affine_transform.txt - total_deformation_field.nii.gz

Example

shell phcp-concat-transforms \ --input sub-${sub}_ses-${ses}_T2w.nii.gz \ --json transform_filenames_sorted.json \ --output /output_directory_path/

Required Inputs

• sub-{sub}_ses-{ses}_T2w.nii.gz defines the initial (native) space. The output total_deformation_field.nii.gz space will be the same.
• transform_filenames_sorted.json lists the successive transformation files, sorted in the order they should be applied (from first to last). The file should follow this structure: json { "tparams": [ "First_transform_filename(.txt or .nii.gz)", "Second_transform_filename(.txt or .nii.gz)", "...", "Last_transform_filename(.txt or .nii.gz)" ] }

Output Files and Usage

The script generates the following main outputs: 1. total_deformation_field.nii.gz – A non-linear deformation field in ANTs format, usable directly with antsApplyTransforms. 2. total_affine_transform.txt – A text file representing the combined affine transformation.

To apply the transformations correctly using antsApplyTransforms, use the following order: 1. total_deformation_field.nii.gz 2. total_affine_transform.txt

Additional Files

• Secondary files (not discussed in this repository but included for reference): jacobian_deformation_field.nii.gz, jacobianLog_deformation_field.nii.gz, total_deformation_field_smoothed.nii.gz
• Tertiary files: Intermediate files generated during processing; not required for downstream analysis.

Transformation into the final space and fusion

The phcp-transform-and-fuse script merges multiple fields of view (FOVs) from the initial space into the final space. It consists of two main stages, controlled via the --run flag.

Example Workflow

```shell

Step 1: Prepare final-space materials (default mode)

phcp-transform-and-fuse -p fov/derivatives/Registration/sub-${sub}/fusion/

Step 2: Merge using 3 blocks

phcp-transform-and-fuse -p fov/derivatives/Registration/sub-${sub}/fusion/ --run -n 3 ```

Stage 1: Prepare Materials (Default, without --run)

This stage generates all necessary materials for reconstruction in the final space from T2star modality in the initial space, including:

• Geometric penalty maps
• Each modality transformed into the final space

All output will be stored in a folder created automatically and named 02-RefSpace.

Required Inputs

Considering Fusion folder as the working directory : derivatives/Registration └── sub-{subjectID} └── Fusion ├── 01-InitSpace/ │ ├── {modality}_{FovName}.nii.gz │ └── ... ├── SendToRefSpace_{modality}.json └── SendToRefSpace_...

1. 01-InitSpace/ folder

Must contain your FOV data in the initial space files, for example:

T2star_InfPos.nii.gz T2star_InfMid.nii.gz ...

• File naming format: {modality}_{FovName}.nii.gz, where {modality} in [ QT1, QT2, QT2star, ADC1500, ADC4500, ADC8000, FA1500, FA4500, FA8000, TransDiff1500, TransDiff4500, TransDiff8000, ParaDiff1500, ParaDiff4500, ParaDiff8000, GFA1500, GFA4500, GFA8000, T2w] and {FovName} is the FOV name as described above.
• Supported formats: GIS or NIfTI
• Must contain at least T2star modality

2. SendToRefSpace_{modality}.json files

You must provide one JSON file per modality (QT1, QT2, QT2star, DWI-one key for all DWI modalities-, T2W), with the following structure:

json { "RefSpace": "refspace_filename_path", "InfPos": { "tparams": ["last_transformation_path", "...", "first_transformation_path"] }, "InfMid": { "tparams": ["..."] } } - RefSpace points to a Nifti file that specifies the geometry of the whole-brain space that was used for the registration process

• tparams is a list of filenames that describe each step of the transformation chain. Each filename can point to either a linear transformation (.txt or .mat or a non-linear deformation field (.nii.gz). The files must be sorted from the last transformation file to the first.

Stage 2: Merge Blocks (with --run and -n)

This stage must be performed after the stage 1. Merges the transformed data located in 02-RefSpace using:

• Geometric penalties for all modalities
• Geometric penalties & Goodness-of-fit weighting for relaxometric maps

Stores the reconstructed blocks in 03-Blocks and the final reconstruction in 04-Reconstruction.

Required Inputs

derivatives/Registration └── sub-{subjectID} └── Fusion ├── 02-RefSpace/ │ └── ... ├── block_{i}.json └── block...

1. block_{i}.json files

For n blocks, create n files named:

block_1.json block_2.json ... block_n.json

Each file should follow this structure:

json { "mask": ["mask_file_1", "...", "mask_file_n"], "modality_1": ["modality1_file_1", "...", "modality1_file_n"], "modality_2": ["..."] }

Important Notes:

• File paths must be ordered continuously from one anatomical end to the other (e.g., InfPos > InfMid > InfAnt).
• Reverse order (e.g., InfAnt > InfMid > InfPos) is also valid, as long as the sequence is consistent.
• Do not skip any FOVs.

Data publication

Prepare the dataset for publication (reidentification, Nifti encoding, adjustment of headers):

shell phcp-prepare-published-dataset \ input_directory \ output_bids_directory \ publication.yaml

Contributing

This repository uses pre-commit to ensure that all committed code follows basic quality standards. Please install it and configure it to run as part of git commit by running pre-commit install in your local repository:

shell pipx install pre-commit pre-commit install # set up the hook (to run pre-commit during 'git commit')