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| Home > Publications Database > Software: PyMyelinPSO: Particle Swarm Optimization of in-vivo MRI data for unbiased quantitative Myelin Mapping v1.0 |
| Home > Publications Database > Software: PyMyelinPSO: Particle Swarm Optimization of in-vivo MRI data for unbiased quantitative Myelin Mapping v1.0 |
| Software | DZNE-2026-00478 |
; ; ;
2026
Zenodo
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Please use a persistent id in citations: doi:10.5281/ZENODO.18709789
Abstract: PyMyelinPSO version 1.0 is a Python framework for Particle Swarm Optimization (PSO)–based inversion of in-vivo and atlas MRI data (published by Dvorak et al., (2021). An atlas for human brain myelin content throughout the adult life span. Scientific reports, 11(1), pp.1-13.). The software enables voxel-wise single and joint inversion of quantitative MRI relaxation signals, currently focusing on T2 and T2* (including complex-valued data), with planned extension toward T1 inversion in future releases. The framework implements a regularization-free global search strategy for multi-echo spin-echo and gradient-echo data, enabling bias-free myelin water quantification. By avoiding explicit regularization constraints and performing multi-cycle stochastic optimization, PyMyelinPSO allows voxel-wise uncertainty assessment derived from the intrinsic ill-posedness of the inversion problem. This supports clinically interpretable quantitative metrics and systematic evaluation of preprocessing effects, such as denoising or Gibbs correction, on derived myelin-related parameters. PyMyelinPSO provides a configurable and reproducible environment for large-scale MRI parameter estimation. It supports slice-parallel full-volume processing, pixel-wise iterative Pareto analysis, and diagnostic iteration testing for convergence assessment and performance optimization. Multi-core parallelization is implemented via Python’s ProcessPoolExecutor combined with memory-mapped arrays for computational efficiency and scalability. The framework automatically generates synthetic decay curves, model parameter vectors, full-volume parameter maps (e.g., MWF, misfit), Pareto analyses, and convergence diagnostics within a standardized directory structure. PyMyelinPSO is intended for quantitative MRI research, relaxation modeling, uncertainty-aware parameter estimation, and advanced myelin water imaging applications.
Keyword(s): Particle Swarm Optimization ; Myelin Water Imaging ; Quantitative Diagnostics ; Uncertainty Quantification ; Myelin
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