What if each voxel were measured with a different diffusion protocol?

TL;DR

This paper introduces PIPE, a rapid, protocol-independent method for estimating diffusion MRI parameters voxel-wise, accommodating gradient nonlinearities and arbitrary protocols, thus improving flexibility and efficiency in fiber imaging.

Contribution

PIPE is a novel, fast, and versatile parameter estimation method that works across various protocols and tissue types, handling gradient nonlinearities without retraining or shell arrangements.

Findings

Maps fiber response and fODF parameters in under 3 minutes

Effective in the presence of significant gradient nonlinearities

Applicable to diverse tissues and multiple diffusion models

Abstract

Expansion of diffusion MRI (dMRI) both into the realm of strong gradients, and into accessible imaging with portable low-field devices, brings about the challenge of gradient nonlinearities. Spatial variations of the diffusion gradients make diffusion weightings and directions non-uniform across the field of view, and deform perfect shells in the q-space designed for isotropic directional coverage. Such imperfections hinder parameter estimation: Anisotropic shells hamper the deconvolution of fiber orientation distribution function (fODF), while brute-force retraining of a nonlinear regressor for each unique set of directions and diffusion weightings is computationally inefficient. Here we propose a protocol-independent parameter estimation (PIPE) method that enables fast parameter estimation for the most general case where the scan in each voxel is acquired with a different protocol in q-space. PIPE applies for any spherical convolution-based dMRI model, irrespective of its complexity, which makes it suitable both for white and gray matter in the brain or spinal cord, and for other tissues where fiber bundles have the same properties within a voxel (fiber response), but are distributed with an arbitrary fODF. Applied to in vivo human MRI with linear tensor encoding on a high-performance system, PIPE maps fiber response and fODF parameters for the whole brain in the presence of significant gradient nonlinearities in under 3 minutes. PIPE enables fast parameter estimation in the presence of arbitrary gradient nonlinearities, eliminating the need to arrange dMRI in shells or to retrain the estimator for different protocols in each voxel. PIPE applies for any model based on a convolution of a voxel-wise fiber response and fODF, and data from varying b-values, diffusion/echo times, and other scan parameters.