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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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What If Each Voxel Were Measured With a Different Diffusion Protocol?

Santiago Coelho¹, Gregory Lemberskiy¹, Ante Zhu²

¹Center for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA.

Magnetic Resonance in Medicine

|December 12, 2025

Summary

This summary is machine-generated.

Gradient nonlinearities in diffusion MRI (dMRI) challenge parameter estimation. The new Protocol-Independent Parameter Estimation (PIPE) method enables fast, accurate analysis of complex fiber structures, even with varying scan protocols.

Keywords:

diffusion MRI gradient nonlinearities machine learning microstructure spherical convolution

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Area of Science:

Medical Imaging
Neuroimaging
Biophysics

Background:

Diffusion MRI (dMRI) is expanding to strong gradients and low-field devices, introducing gradient nonlinearities.
These nonlinearities cause spatial variations in diffusion gradients, distorting q-space and hindering accurate parameter estimation.
Current methods struggle with anisotropic shells and inefficient retraining for varying protocols.

Purpose of the Study:

To develop a protocol-independent parameter estimation (PIPE) method for dMRI.
To address the challenge of gradient nonlinearities in dMRI analysis.
To enable fast and accurate estimation of fiber orientation distribution functions (fODFs) despite protocol variations.

Main Methods:

Proposed a protocol-independent parameter estimation (PIPE) method applicable to any spherical convolution-based dMRI model.
Derived a parsimonious representation to isolate isotropic and anisotropic effects of gradient nonlinearities.
Applied PIPE to in vivo human MRI data with linear tensor encoding.

Main Results:

PIPE enables fast parameter estimation for the whole brain in under 3 minutes, even with significant gradient nonlinearities.
The method successfully evaluates fiber response and fODF parameters.
Demonstrated robustness across varying b-values, diffusion/echo times, and other scan parameters.

Conclusions:

PIPE facilitates rapid parameter estimation in the presence of arbitrary gradient nonlinearities.
Eliminates the need for dMRI shell arrangements or retraining estimators for different protocols.
Applicable to various dMRI models and data acquired with diverse scan parameters.