CBrainMRIPrePro

This repository provides easy to use preprocessing for conventional Brain MRI images (ie : T1, T1ce, T2, Flair, …). For the time being the supported format are Nifti files.

The provided pipeline offer the possibility to resample image spacing, bias field correct, co-register (in SRI24 template or in a reference modality), skull-stripped, and z-score normalize.

The package is based on ANTsPy and HD-BET.

Installation

1. Create a conda environment (recommended)

ENVNAME="CBrainMRIPrePro"
conda create -n $ENVNAME python==3.7.7 -y
conda activate $ENVNAME

2. Install repository

Method 1: Github Master Branch

pip install git+https://github.com/Alxaline/cBrainMRIPrePro.git

Method 2: Development Installation

git clone https://github.com/Alxaline/cBrainMRIPrePro.git
cd cBrainMRIPrePro
pip install -e .

Documentation

https://cbrainmriprepro.readthedocs.io/en/latest/

How to use it

The pipe Class is describe in the documentation

Using CBrainMRIPrePro is straightforward. The following examples show how to perform: - Bias field correction - Registration on the SRI24 atlas space - Skull-stripping - Z-Score normalization

4 modalities are used (t1, t1ce, t2, flair). For the 4 modalities n4 correction is applied. T1 modality is considered as the reference and Template is set to true. T1 will be first register on the SRI24 atlas. This register T1 modality is now considered as the reference for others modalities for co-registration. Then images are skull-stripped using HD-BET and Z-Score normalize inside the brain

preprocess = DataPreprocessing(dict_image={"t1": "/path/t1.nii.gz",
                                           "t1ce": "/path/t1ce.nii.gz",
                                           "t2": "/path/t2.nii.gz",
                                           "flair": "/path/flair.nii.gz"},
                               reference={"t1": "/path/t1.nii.gz"},
                               output_folder="/output_test",
                               resample_spacing=None,
                               n4_correction=["t1", "t1ce", "flair", "t2"],
                               inter_type_resample=4,
                               inter_type_apply_transform_registration=4,
                               template=True,
                               do_coregistration=True,
                               do_ss=True,
                               normalize_z_score=["t1", "t1ce", "t2", "flair"],
                               save_step=("resample", "n4_correction", "coregistration", "affine_transform",
                                          "skullstripping", "normalize"),
                               device="0", overwrite=True,
                               )

Then:

preprocess.run_pipeline()

The output folder will contain five folders nammed affine_transform, coregistration, n4_correction, normalize and affine_transform respectively containing version of files with the current preprocess step.

Tree is as follows:

output_folder
├── affine_transform
│   ├── filename_n4_fwdtransforms_flair.mat
│   ├── filename_n4_fwdtransforms_t1ce.mat
│   ├── filename_n4_fwdtransforms_t1.mat
│   ├── filename_n4_fwdtransforms_t2.mat
│   ├── filename_n4_invtransforms_flair.mat
│   ├── filename_n4_invtransforms_t1ce.mat
│   ├── filename_n4_invtransforms_t1.mat
│   ├── filename_n4_invtransforms_t2.mat
├── coregistration
│   ├── filename_n4_reference_t1.nii.gz
│   ├── filename_n4_register_flair.nii.gz
│   ├── filename_n4_register_t1ce.nii.gz
│   ├──  filename_n4_register_t1.nii.gz
│   ├── filename_n4_register_t2.nii.gz
├── n4_correction
│   ├── filename_n4_flair.nii.gz
│   ├── filename_n4_t1ce.nii.gz
│   ├── filename_n4_t1.nii.gz
│   ├── filename_n4_t2.nii.gz
├── normalize
│   ├── filename_n4_register_ss_normalize_flair.nii.gz
│   ├── filename_n4_register_ss_normalize_t1ce.nii.gz
│   ├── filename_n4_register_ss_normalize_t1.nii.gz
│   ├── filename_n4_register_ss_normalize_t2.nii.gz
├── skullstripping
│   ├── filename_n4_register_brain_mask_t1.nii.gz
│   ├── filename_n4_register_ss_flair.nii.gz
│   ├── filename_n4_register_ss_t1ce.nii.gz
│   ├── filename_n4_register_ss_t1.nii.gz
│   ├── ilename_n4_register_ss_t2.nii.gz

How to cite ?

If you find this repository useful for your research, please cite our work:

Carré, A., Battistella, E., Niyoteka, S. et al. AutoComBat: a generic method for harmonizing MRI-based radiomic features. Sci Rep 12, 12762 (2022). https://doi.org/10.1038/s41598-022-16609-1

BibTeX:

@article{carreAutoComBatGenericMethod2022,
    title = {AutoComBat: a generic method for harmonizing MRI-based radiomic features},
    volume = {12},
    issn = {2045-2322},
    url = {https://www.nature.com/articles/s41598-022-16609-1},
    doi = {10.1038/s41598-022-16609-1},
    language = {en},
    number = {1},
    urldate = {2022-07-27},
    journal = {Scientific Reports},
    author = {Carré, Alexandre and Battistella, Enzo and Niyoteka, Stephane and Sun, Roger and Deutsch, Eric and Robert, Charlotte},
    year = {2022},
    keywords = {Cancer imaging, Computational science, Tumour biomarkers},
    pages = {12762},
}