Welcome to the official PyDesigner project!¶
PyDesigner was inspired by NYU’s DESIGNER dMRI preprocessing pipeline to bring pre- and post- processing to every MRI imaging scientist. With PyDesigner, users are no longer confined to specific file types, operating systems, or complicated scripts just to extract DTI or DKI parameters – PyDesigner makes this easy, and you will love it!
100% Python-based scripts
Minimized package dependencies for small package footprint
Preprocessing designed to boost SNR
Accurate and fast DTI and DKI metrics via cutting-edge algorithms
One-shot preprocessing to parameter extraction
Cross-platform compatibility between Windows, Mac and Linux using Docker
Highly flexible and easy to use
Parallel processing for quicker preprocessing and parameterization
Easy install with pip
Input file-format agnostic – works with .nii, .nii.gz, .mif and dicoms
Quality control metrics to evaluate data integrity – SNR graphs, outlier voxels, and head motion
Uses the latest techniques from DTI/DKI/FBI literature
Works with DTI, DKI, WMTI, FBI, or FBWM datasets
Supports multi-TE dataset processing
Tractography ready: Computes ODF spherical harmonic expansion for MRtrix3, and .fib files for DSI Studio
Installable modules for Python or Jupyter Notebook scripting of custom workflows
We welcome all DTI/DKI researchers to evaluate this software and pass on their feedback or issues through the Issues and Discussion page of this project’s GitHub repository.
- System Requirements
Parallel processing in PyDesigner scales almost linearly with the nummber of CPU cores present. The application is also memory-intensive due to the number of parameter maps being computed.
Based on this evaluation, for processing a single DWI using PyDesigner, we recommend the following minimum system specifications:
Intel i7-9700 or AMD Ryzen 1800X [8 cores]
16 GB RAM
12 GB free storage
Nvidia CUDA-enabled GPU
PyDesigner in Container¶
PyDesigner can run on all major platforms (x86, x64, and ARM) via Docker container technology. The containerized form of PyDesigner is called NeuroDock and comes preloaded with PyDesigner, FSL, and MRtrix3.
Obtaining the FSL software typically requires registration. Therefore, we requeust users of NeuroDock who have not previously registered as a user of the FSL software complete the registration process. This allows developers of FSL to be recognized for the utilization of their software.
Please include the following citation if you used PyDesigner in your work or publication:
Siddhartha Dhiman, Joshua B Teves, Kathryn E Thorn, Emilie T McKinnon, Hunter G Moss, Vitria Adisetiyo, Benjamin Ades-Aron, Jelle Veraart, Jenny Chen, Els Fieremans, Andreana Benitez, Joseph A Helpern, Jens H Jensen. PyDesigner: A Pythonic Implementation of the DESIGNER Pipeline for Diffusion Tensor and Diffusional Kurtosis Imaging. bioRxiv 2021.10.20.465189. doi: 10.1101/2021.10.20.465189
The PyDesigner software packages is based upon the the references listed below. Please be sure to cite them if PyDesigner was used in any publications.
Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of MRI. Magn Reson Med 2005;53:1432-1440. doi: 10.1002/mrm.20508
Jensen JH, Helpern JA. MRI Quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed 2010;23:698-710. doi: 10.1002/nbm.1518
Fieremans E, Jensen JH, Helpern JA. White matter characterization with diffusional kurtosis imaging. Neuroimage 2011;58:177-188. doi: 10.1016/j.neuroimage.2011.06.006
Tabesh A, Jensen JH, Ardekani BA, Helpern JA. Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magn Reson Med 2011;65:823-836. doi: 10.1002/mrm.22655
Glenn GR, Helpern JA, Tabesh A, Jensen JH. Quantitative assessment of diffusional kurtosis anisotropy. NMR Biomed 2015;28:448-459. doi: 10.1002/nbm.3271
Jensen JH, Glenn GR, Helpern JA. Fiber ball imaging. Neuroimage 2016; 124:824-833. doi: 10.1016/j.neuroimage.2015.09.049
McKinnon ET, Helpern JA, Jensen JH. Modeling white matter microstructure with fiber ball imaging. Neuroimage 2018;176:11-21. doi: 10.1016/j.neuroimage.2018.04.025
Ades-Aron B, Veraart J, Kochunov P, McGuire S, Sherman P, Kellner E, Novikov DS, Fieremans E. Evaluation of the accuracy and precision of the diffusion parameter EStImation with Gibbs and NoisE removal pipeline. Neuroimage. 2018;183:532-543. doi: 10.1016/j.neuroimage.2018.07.066
Moss H, McKinnon ET, Glenn GR, Helpern JA, Jensen JH. Optimization of data acquisition and analysis for fiber ball imaging. Neuroimage 2019;200;690-703. doi: 10.1016/j.neuroimage.2019.07.005
Moss HG, Jensen JH. Optimized rectification of fiber orientation density function. Magn Reson Med. 2020 Jul 25. doi: 10.1002/mrm.28406. Online ahead of print.