Revisiting classical diffusion magnetic resonance methods as a means to measure time-dependent diffusion

The field of diffusion microstructural magnetic resonance (MR) aims to probe time-dependent diffusion, i.e., an ensemble-averaged mean-squared displacement ⟨<i>r</i> <sup>2</sup>(<i>t</i>)⟩ that is not linear in time. This time-dependence contains rich information about the surrounding microenvironment. MR methods to measure time-dependent diffusion quantitatively, however, require either non-standard pulse sequences, such as oscillating gradients, or make non-physical assumptions, such as infinitely narrow gradient pulses. Here, we argue that standard spin echo and stimulated echo MR sequences can be used to probe ⟨<i>r</i> <sup>2</sup>(<i>t</i>)⟩ directly. In particular, we propose a framework in which the log-signal ratio obtained from a pair of measurements with different inter-pulse spacing <i>Δ</i> is proportional to the MSD between these two <i>Δ</i> values along the gradient direction <i>x</i>: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>⟨</mml:mo> <mml:mrow> <mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow> <mml:mrow><mml:mi>x</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msubsup> <mml:mrow><mml:mo>(</mml:mo> <mml:mrow> <mml:msub><mml:mrow><mml:mi>Δ</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:mo>)</mml:mo></mml:mrow> </mml:mrow> <mml:mo>⟩</mml:mo></mml:mrow> <mml:mo>-</mml:mo> <mml:mrow><mml:mo>⟨</mml:mo> <mml:mrow> <mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow> <mml:mrow><mml:mi>x</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msubsup> <mml:mrow><mml:mo>(</mml:mo> <mml:mrow> <mml:msub><mml:mrow><mml:mi>Δ</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:mo>)</mml:mo></mml:mrow> </mml:mrow> <mml:mo>⟩</mml:mo></mml:mrow> </mml:math> . The framework is quantitative for short, finite-duration gradient pulses and under the Gaussian phase approximation (GPA). To validate the framework, we consider one-dimensional diffusion between impermeable, parallel planes, as well as periodically-spaced, permeable planes. Excellent agreement is obtained between the estimation and the ground truth in the regime where the GPA is expected to hold. Importantly, the GPA can be made to hold for any underlying microstructure, making the proposed framework widely applicable.

Authors

Institutions

Publication Details

Journal
Magnetic Resonance Letters
Published
2025-04-22
DOI
https://doi.org/10.1016/j.mrl.2025.200197
Citations
2
Primary Topic
Advanced Neuroimaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
2.72

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Revisiting classical diffusion magnetic resonance methods as a means to measure time-dependent diffusion

Peter J. Basser, Nathan H. Williamson, Teddy X. Cai
2 citations
Magnetic Resonance Letters
Advanced Neuroimaging Techniques and Applications
2.72
article

Revisiting classical diffusion magnetic resonance methods as a means to measure time-dependent diffusion

Peter J. Basser, Nathan H. Williamson, Teddy X. Cai
article en
2 citations

Abstract

The field of diffusion microstructural magnetic resonance (MR) aims to probe time-dependent diffusion, i.e., an ensemble-averaged mean-squared displacement ⟨<i>r</i> <sup>2</sup>(<i>t</i>)⟩ that is not linear in time. This time-dependence contains rich information about the surrounding microenvironment. MR methods to measure time-dependent diffusion quantitatively, however, require either non-standard pulse sequences, such as oscillating gradients, or make non-physical assumptions, such as infinitely narrow gradient pulses. Here, we argue that standard spin echo and stimulated echo MR sequences can be used to probe ⟨<i>r</i> <sup>2</sup>(<i>t</i>)⟩ directly. In particular, we propose a framework in which the log-signal ratio obtained from a pair of measurements with different inter-pulse spacing <i>Δ</i> is proportional to the MSD between these two <i>Δ</i> values along the gradient direction <i>x</i>: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>⟨</mml:mo> <mml:mrow> <mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow> <mml:mrow><mml:mi>x</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msubsup> <mml:mrow><mml:mo>(</mml:mo> <mml:mrow> <mml:msub><mml:mrow><mml:mi>Δ</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:mo>)</mml:mo></mml:mrow> </mml:mrow> <mml:mo>⟩</mml:mo></mml:mrow> <mml:mo>-</mml:mo> <mml:mrow><mml:mo>⟨</mml:mo> <mml:mrow> <mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow> <mml:mrow><mml:mi>x</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msubsup> <mml:mrow><mml:mo>(</mml:mo> <mml:mrow> <mml:msub><mml:mrow><mml:mi>Δ</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:mo>)</mml:mo></mml:mrow> </mml:mrow> <mml:mo>⟩</mml:mo></mml:mrow> </mml:math> . The framework is quantitative for short, finite-duration gradient pulses and under the Gaussian phase approximation (GPA). To validate the framework, we consider one-dimensional diffusion between impermeable, parallel planes, as well as periodically-spaced, permeable planes. Excellent agreement is obtained between the estimation and the ground truth in the regime where the GPA is expected to hold. Importantly, the GPA can be made to hold for any underlying microstructure, making the proposed framework widely applicable.

Magnetic Resonance LettersVol. 5(4)
Henry M. Jackson Foundation (US), Uniformed Services University of the Health Sciences (US), Army Medical University (CN), Eunice Kennedy Shriver National Institute of Child Health and Human Development (US)
Eunice Kennedy Shriver National Institute of Child Health and Human Development
Openalex Percentile: Top 11%
Advanced Neuroimaging Techniques and Applications
2.72
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.