Package: neuronorm 1.0.2
David Payares
neuronorm: Preprocessing of Structural MRI for Multiple Neurodegenerative Diseases
Preprocessing pipeline for normalizing and cleaning T1-weighted, T2-weighted and FLAIR MRI images coming from different sources, diseases, patients, scanners and sites.
Authors:
neuronorm_1.0.2.tar.gz
neuronorm_1.0.2.tar.gz(r-4.5-noble)neuronorm_1.0.2.tar.gz(r-4.4-noble)
neuronorm_1.0.2.tgz(r-4.4-emscripten)neuronorm_1.0.2.tgz(r-4.3-emscripten)
neuronorm.pdf |neuronorm.html✨
neuronorm/json (API)
| # Install 'neuronorm' in R: |
| install.packages('neuronorm', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:212e8cd8e9. Checks:OK: 1 NOTE: 1. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 05 2024 |
| R-4.5-linux | NOTE | Dec 05 2024 |
Exports:coregistration_imagescreate_bias_listget_modalitiesimage_normalization_ravelload_mri_groupload_mri_patientpreprocess_modalitiespreprocess_modality_t1preprocess_patients
Dependencies:abindbitopsevaluatefslrhighrknitrmatrixStatsneurobaseoro.niftiR.methodsS3R.ooR.utilsRcppRNiftixfunyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Create a vector of the corregistered images based on the available MRI images modalities. | coregistration_images |
| Create a vector of inhomogeneity corrected images based on the available MRI images modalities. | create_bias_list |
| Create a vector of strings with the MRI modalities available in a patient's folder. | get_modalities |
| Wrapper function for RAVEL normalization of T1-weighted images | image_normalization_ravel |
| Load MRI per group or disease | load_mri_group |
| Load MRI scans per patient | load_mri_patient |
| Preprocess group of MRI scan for one patient | preprocess_modalities |
| Preprocess T1-weighted MRI scan for one patient | preprocess_modality_t1 |
| Preprocess MRI scans for multiple patients | preprocess_patients |