Diffusion MRI protocol design for reliable parameter recovery: a unified framework across biophysical models
Abstract
Reliable microstructural parameter estimation in diffusion MRI (dMRI) critically depends on the acquisition protocol. Yet, common protocol design criteria may not fully reflect estimator bias, parameter constraints, and nonlinear fitting behavior in complex inverse problems. Here, we introduce Protocol Optimization via Loss Assessing Parameter Recovery (POLAR), an end-to-end framework that searches the continuous acquisition space and directly minimizes multiparameter recovery error through noisy signal simulation and the chosen model fitting. We demonstrate POLAR spanning distinct microstructural environments: the Cylinder-Zeppelin model for white matter, Neurite Exchange Imaging (NEXI) for gray matter, and Cellular Exchange Imaging (CEXI) for permeable cells. Across independent held-out parameter sets, POLAR consistently outperformed model-specific reference designs, reducing RMSE by up to 50% for Cylinder-Zeppelin and 74% for CEXI, while reducing NEXI exchange time bias by 70%. These gains across distinct biophysical models, acquisition settings, and reference design strategies demonstrate the benefit of optimizing acquisition protocols through the downstream parameter-estimation problem. POLAR provides a flexible, model-specific and estimator-aware framework for designing dMRI protocols tailored to the microstructural parameters and experimental conditions of interest.
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BibTeX
@misc{taskin2026diffusion,
title = {{Diffusion MRI protocol design for reliable parameter recovery: a unified framework across biophysical models}},
author = {Ekin Taskin and Jonathan Rafael-Patiño and Erick Jorge Canales-Rodríguez and Jean-Philippe Thiran},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.28099},
eprint = {2610.28099},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.28099},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Diffusion MRI protocol design for reliable parameter recovery: a unified framework across biophysical models
AU - Ekin Taskin
AU - Jonathan Rafael-Patiño
AU - Erick Jorge Canales-Rodríguez
AU - Jean-Philippe Thiran
PY - 2026
DA - 2026/10/20
PB - arXiv
DO - 10.48550/arxiv.2610.28099
UR - https://arxiv.org/abs/2610.28099
AB - Reliable microstructural parameter estimation in diffusion MRI (dMRI) critically depends on the acquisition protocol. Yet, common protocol design criteria may not fully reflect estimator bias, parameter constraints, and nonlinear fitting behavior in complex inverse problems. Here, we introduce Protocol Optimization via Loss Assessing Parameter Recovery (POLAR), an end-to-end framework that searches the continuous acquisition space and directly minimizes multiparameter recovery error through noisy signal simulation and the chosen model fitting. We demonstrate POLAR spanning distinct microstructural environments: the Cylinder-Zeppelin model for white matter, Neurite Exchange Imaging (NEXI) for gray matter, and Cellular Exchange Imaging (CEXI) for permeable cells. Across independent held-out parameter sets, POLAR consistently outperformed model-specific reference designs, reducing RMSE by up to 50% for Cylinder-Zeppelin and 74% for CEXI, while reducing NEXI exchange time bias by 70%. These gains across distinct biophysical models, acquisition settings, and reference design strategies demonstrate the benefit of optimizing acquisition protocols through the downstream parameter-estimation problem. POLAR provides a flexible, model-specific and estimator-aware framework for designing dMRI protocols tailored to the microstructural parameters and experimental conditions of interest.
ER -