PyDesigner v1.0: A Pythonic Implementation of the DESIGNER Pipeline for Diffusion Magnetic Resonance Imaging

Area of Science:

• Neuroimaging
• Medical Image Processing
• Computational Neuroscience

Background:

• Diffusion MRI (dMRI) is crucial for studying white matter structure.
• Existing dMRI preprocessing pipelines can be complex and lack flexibility.
• Accurate estimation of diffusion metrics requires robust preprocessing steps.

Purpose of the Study:

• To introduce PyDesigner, a novel Python-based software for dMRI preprocessing and tensor estimation.
• To provide a user-friendly, cross-platform solution for optimizing diffusion measure derivation.
• To enhance data integrity through integrated quality control metrics.

Main Methods:

• PyDesigner integrates tools from FSL and MRtrix3 for denoising, Gibbs ringing correction, motion correction, and Rician bias correction.
• The software supports various dMRI datasets (DKI, DTI, WMTI, FBI, FBWM) and file formats (.nii, .nii.gz, .mif, DICOM).
• It offers pipeline customization and outputs quality control metrics like SNR, outlier voxels, and head motion.

Main Results:

• PyDesigner accurately derives commonly used diffusion metrics and tractography ODFs/fib files.
• The software is file-format agnostic and compatible with Windows, macOS, and Linux.
• Quality control metrics aid in evaluating the integrity of processed dMRI data.

Conclusions:

• PyDesigner offers a flexible, efficient, and user-friendly solution for dMRI preprocessing in non-commercial research.
• It optimizes the estimation of diffusion metrics and tractography, facilitating robust neuroimaging studies.
• The integrated quality control features enhance the reliability and interpretability of dMRI results.