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توضیحات

Diffusion weighted models fitting

ویژگی	مقدار
سیستم عامل	-
نام فایل	difit-1.0
نام	difit
نسخه کتابخانه	1.0
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Chandana Kodiweera
ایمیل نویسنده	kodiweera@hotmail.com
آدرس صفحه اصلی	https://github.com/kodiweera/difit
آدرس اینترنتی	https://pypi.org/project/difit/
مجوز	MIT

.. image:: ../logo.png :width: 400 :align: center :alt: ReadTheDocs :target: https://difit.readthedocs.io/en/stable/ *difit*: **A diffusion MRI models fitting software** ==================================================== *difit* is being developed as an attempt to bring in major diffusion models into one place (*v1.0.0* can do DTI and DKI models). *difit* allows to choose b-values and b0-images from multi-shell data as the user desires. This enable the user to test different combinations easily without splitting data prior to the model fitting. Major software packages in the background ----------------------------------------- *difit's* versions of DTI and DKI models come from `dipy <https://dipy.org/>`_. Nodes were built using `nipype <https://nipype.readthedocs.io/en/latest/>`_. Other dependancies are listed in the setup.cfg file. Installation ------------ ``pip install difit`` Models fitting command ------------------------- DTI *** ``python -m difit 'path/to/input/dir' 'path/to/output/dir' 'path/to/work/dir' --models dti --dti_b_values 1000 --dti_b0_images 3 --mem 6 --nprocs 2`` DKI *** ``python -m difit 'path/to/input/dir' 'path/to/output/dir' 'path/to/work/dir' --models dki --dki_b_values 500 1000 3000 --dki_b0_images 4 --mem 9 --nprocs 2`` Both DTI and DKI **************** It is possible to fit more than one model in one run. DTI followed by DKI will be fitted with the below command. ``python -m difit 'path/to/input/dir' 'path/to/output/dir' 'path/to/work/dir' --models dti dki --dki_b_values 500 1000 2000 --dki_b0_images 3 --dti_b_values 1000 --dti_b0_images 3 --mem 12 --nprocs 2`` Parallel Processing --------------------- difit can fit multiple subjects in parallel. If you have more than one subject, you can issue the command similar to below with the wildcard (*). If you want to fit few of the available subjects, you can use the curly bracket notaions to specify the subjects. ``python -m difit 'path/to/input/sub*/data' 'path/to/output/sub*/out' 'path/to/work/dir' --models dti dki --dki_b_values 500 1000 2000 --dki_b0_images 3 --dti_b_values 1000 --dti_b0_images 3 --mem 32 --nprocs 8`` Input dwi data files --------------------- *difit* searches for files ending with `*_dwi.nii.gz`, `*_dwi.bval`, `*_dwi.bvec` and `*_mask.nii.gz` in the input directory. Make sure to name your files with the same endings. If you use `qsiprep <https://qsiprep.readthedocs.io/en/latest/installation.html>`_ to preprocess data, you will end up with the above format which uses `BIDS <https://bids.neuroimaging.io/>`_ convention. If you used a different software to preprocess your data, name your files to match the above convention. The `*` indicate any name/s before the underscore can take place. An example for multi models and multi subjects parallel processing ****************************************************************** Assume there are two subject directories namely **sub01** and **sub02** and in each directory there are *data* and *out* directories available. In this example, diffusion data have four shells (500,1000,2000,3000) and 6 b0 images. But we are going to use only one shell for DTI and three shells for DKI model. The both models will use 3 b0 images (consecutive). :: projectdifit ├── sub01 │ ├── data │ │ ├── example_dwi.bval │ │ ├── example_dwi.bvec │ │ ├── example_dwi.nii.gz │ │ └── example_mask.nii.gz │ └── out ├── sub02 │ ├── data │ │ ├── example_dwi.bval │ │ ├── example_dwi.bvec │ │ ├── example_dwi.nii.gz │ │ └── example_mask.nii.gz │ └── out └── work ``python -m difit '/projectdifit/sub*/data' '/projectdifit/sub*/out' '/projectdifit/work' --models dti dki --dki_b_values 500 1000 2000 --dki_b0_images 3 --dti_b_values 1000 --dti_b0_images 3 --mem 32 --nprocs 8`` Output files ************ :: out ├── dki │ ├── AK.nii.gz │ ├── dki_AD_mosaic.png │ ├── dki_AD.nii.gz │ ├── dki_AK_mosaic.png │ ├── dki_FA_mosaic.png │ ├── dki_FA.nii.gz │ ├── dki_kFA_mosaic.png │ ├── dki_MD_mosaic.png │ ├── dki_MD.nii.gz │ ├── dki_MK_mosaic.png │ ├── dki_RD_mosaic.png │ ├── dki_RD.nii.gz │ ├── dki_RK_mosaic.png │ ├── dki_summary_plots.html │ ├── kFA.nii.gz │ ├── MK.nii.gz │ └── RK.nii.gz └── dti ├── dti_AD_mosaic.png ├── dti_AD.nii.gz ├── dti_FA_mosaic.png ├── dti_FA.nii.gz ├── dti_MD_mosaic.png ├── dti_MD.nii.gz ├── dti_RD_mosaic.png ├── dti_RD.nii.gz └── dti_summary_plots.html *difit* creates **dti** and **dki** directories in the out directory to store the above output files for each subject. HELP ***** ``python -m difit -h`` :: dmri models fitter work flow positional arguments: input_dir Input data directory. This directory must contain *_dwi.nii.gz, *_mask.nii.gz, *_dwi.bval, *_dwi.bvec. Multiple subjects can be list with wild cards e.g. ~/data/sub_*/data ; each subject directory contain its own set of diffusion files. output_dir The output directory for models metrices. In this directory seperate subdirectories will be created for each model; For multiple subjects, output can be given with a wildcard e.g. ~/data/sub_*/out work_dir directory for intermediate results optional arguments: -h, --help show this help message and exit --models MODELS [MODELS ...] Choose the model or models you want to fit to your data. Choose one or a combination from dti, dki (default: None) Options for choosing shell numbers for DTI processing: --dti_b_values DTI_B_VALUES [DTI_B_VALUES ...] Choose a b-value/s of multishell data to use for DTI model fitting (default: None) --dti_b0_images DTI_B0_IMAGES If dwi data contain more than one b0 images, choose how many you want to use for DTI model fitting (default: 1) Options for choosing shell numbers and b0 images for DKI processing: --dki_b_values DKI_B_VALUES [DKI_B_VALUES ...] Choose a b-values of multishell data to use for DKI model fitting (default: None) --dki_b0_images DKI_B0_IMAGES If dwi data contain more than one b0 images, choose how many you want to use for DKI model fitting (default: 1) Options to specify computer resources: --nprocs NPROCS maximum number of cpus across all processes (default: None) --omp-nthreads OMP_NTHREADS maximum number of threads per-process (default: None) --mem MEMORY_GB upper bound memory limit (GB) for difit models fitting (default: None) --use-plugin FILE nipype plugin configuration file (default: None) Future Additions **************** *MSMT-CSD particle filtering tractography*, *NODDI* and *FSL PROBTRACKX*. Note **** This project has been set up using PyScaffold 4.1. For details and usage information on PyScaffold see https://pyscaffold.

نیازمندی

مقدار	نام
>=3.2.1	sphinx
-	nibabel
-	dipy
-	setuptools
-	pytest
-	tox
-	pytest-cov
-	numpy
-	twine
-	pydeps
-	fury
-	multiprocess
-	nipype
-	weasyprint
-	nilearn
-	importlib-metadata
-	setuptools
-	pytest
-	pytest-cov

نحوه نصب

نصب پکیج whl difit-1.0:

pip install difit-1.0.whl

نصب پکیج tar.gz difit-1.0:

pip install difit-1.0.tar.gz