Detecting and characterizing restricted diffusion with accessible magnetic field gradients

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ID: 327861
2026
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Abstract
Abstract For over 50 years, diffusion magnetic resonance (dMR) has been employed in numerous studies on characterizing chemical compounds, porous media, and biological tissues. These studies have almost exclusively employed the Stejskal-Tanner (ST) pulse sequence, which is sensitive to the ensemble average propagator (EAP). EAP is a compromised version of the true diffusion propagator---a quantity that fully describes the diffusion process in any system and holds exquisite information that can be used to characterize complex samples. Recently, a new diffusion sensitization method was proposed, here referred to as PROP, and was shown to be capable of acquiring the true diffusion propagator in closed pores. However, its adequate use requires a large number of measurements and (consequently) a long acquisition time. Thus, its translation to, e.g., the clinical field, is challenging. Here, we propose a reduced PROP pulse sequence (rPROP) which offers various advantages over the ST sequence. rPROP excels at isolating restricted diffusion thanks to its inherently high diffusion-weighting, yielding an efficient free diffusion filter. We employ rPROP to study different samples, and compare its performance to ST measurements in terms of estimation accuracy and gradient amplitude requirements. Furthermore, we study the capability of rPROP to detect anisotropic diffusion in samples which predominantly feature isotropic diffusion. Finally, we present a method capable of acquiring the mean position distribution (analogous to the EAP attainable via ST data) of particles via MR measurements. We highlight that all signals and images presented in this work were acquired using a benchtop MRI scanner.
Reference Key
openalex_W7208817796 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Alfredo Ordinola, Magnus Herberthson, Walker S. Jackson, Evren Özarslan
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Journal PNAS nexus
Year 2026
DOI
10.1093/pnasnexus/pgag293
URL
https://doi.org/10.1093/pnasnexus/pgag293
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