Comparison of water exchange measurements between filter‐exchange imaging and diffusion time‐dependent kurtosis imaging in the human brain

Magnetic Resonance in Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 29, 2025

Abstract Purpose Filter‐exchange imaging (FEXI) and diffusion time (t)‐dependent kurtosis (DKI(t)) are two diffusion‐based methods that have been proposed for in vivo measurements of water exchange rates. Few studies directly compared these methods. We aimed to investigate whether FEXI DKI(t) yield comparable the human brain vivo. Methods Eight healthy volunteers underwent multiple‐direction acquisitions on a 3T scanner. performed region interest (ROI) analysis determine correlations between FEXI‐derived apparent rate (AXR) DKI(t)‐derived reciprocal (). Results In both white matter (WM) gray (GM), revealed substantial diffusion‐time dependence diffusivity kurtosis. However, at t ≥ 100 ms, showed weak dependence. WM, this may be due myelin “free” with different T 1 values, although other factors, such as remaining restrictive effects from microstructural barriers, cannot excluded. found significant correlation AXR axial direction within WM. No was present GM, values similar ranges. Conclusion These results suggest could sensitive same process only when is sufficiently long, GM effect microstructure non‐negligible, especially short times (<100 ms).

Language: Английский

---

The Diffusion Exchange Ratio (DEXR): A minimal sampling of diffusion exchange spectroscopy to probe exchange, restriction, and time-dependence

Journal of Magnetic Resonance, Journal Year: 2024, Volume and Issue: 366, P. 107745 - 107745

Published: Aug. 6, 2024

Language: Английский

---

Age‐Trajectories of Higher‐Order Diffusion Properties of Major Brain Metabolites in Cerebral and Cerebellar Gray Matter Using In Vivo Diffusion‐Weighted MR Spectroscopy at 3T

Aging Cell, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 16, 2025

ABSTRACT Healthy brain aging involves changes in both structure and function, including alterations cellular composition microstructure across regions. Unlike diffusion‐weighted MRI (dMRI), MR spectroscopy (dMRS) can assess cell‐type specific microstructural changes, providing indirect information on cell through the quantification interpretation of metabolites' diffusion properties. This work investigates age‐related higher‐order properties total N‐Acetyl‐aspartate (neuronal biomarker), choline (glial creatine (both neuronal glial biomarker) beyond classical apparent coefficient cerebral cerebellar gray matter healthy human brain. Twenty‐five subjects were recruited scanned using a semi‐LASER sequence two regions‐of‐interest (ROI) at 3T: posterior‐cingulate (PCC) cortices. Metabolites' was characterized by quantifying metrics from Gaussian non‐Gaussian signal representations biophysical models. All studied metabolites exhibited lower diffusivities higher kurtosis values cerebellum compared to PCC, likely stemming complexity cerebellum. Multivariate regression analysis (accounting for ROI tissue as covariate) showed slight decrease (or no change) all increase with age, none which statistically significant ( p > 0.05). The proposed age‐trajectories provide benchmarks identifying anomalies major could be related pathological mechanisms altering composition.

Language: Английский

---

Magnetic Resonance Measurements of Transcytolemmal Water Exchange

Acta Physica Sinica, Journal Year: 2025, Volume and Issue: 74(11), P. 0 - 0

Published: Jan. 1, 2025

Transcytolemmal water exchange is a critical process for maintaining cellular homeostasis and function, serving as potential biological marker tumor proliferation, prognosis, states. The application of Magnetic Resonance Imaging (MRI) to measure transcytolemmal dates back the 1960s, when researchers first measured residence time intracellular molecules in erythrocyte suspensions. Concurrently, multi-exponential nature nuclear magnetic resonance signals tissues was discovered. Studies suggested that could be one factors explaining this characteristic, marking beginning research into measuring using techniques. After decades development, current MRI techniques can broadly classified two types: those relaxation contrast mechanism diffusion mechanism. This review introduces development these technologies, discussing principles, mathematical/biophysical models, results, validation representative methods. Regarding relaxation-based MR techniques, systematically organizes methodologies quantifying through chronological developments across three substrates: <i>ex vivo</i> cell suspensions, tissues, <i>in tissues. modeling section emphasizes frameworks, including two-site-exchange model three-site-two-exchange shutter-speed model. diffusion-based progress diffusion-encoding measurement. Diffusion-encoding methods are introduced according single encoding sequences double sequences. For modeling, it covers types, Kärger based on two-component Gaussian assumption, modified incorporating restricted effects, first-order reaction kinetic models. Additionally, comparative studies among different also discussed. Finally, evaluates their respective clinical applications, advantages, limitations. Future prospects technological field proposed at end.

Language: Английский

---

Introducing µGUIDE for quantitative imaging via generalized uncertainty-driven inference using deep learning

eLife, Journal Year: 2024, Volume and Issue: 13

Published: Nov. 26, 2024

This work proposes µGUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or signal representation, with exemplar demonstration in diffusion-weighted magnetic resonance imaging. Harnessing new deep learning architecture for automatic feature selection combined simulation-based inference and efficient sampling the distributions, µGUIDE bypasses high computational time cost conventional approaches does not rely on acquisition constraints define model-specific summary statistics. The obtained allow highlight degeneracies present definition quantify uncertainty ambiguity estimated parameters.

Language: Английский

---

Introducing µGUIDE for quantitative imaging via generalized uncertainty-driven inference using deep learning

eLife, Journal Year: 2024, Volume and Issue: 13

Published: Aug. 22, 2024

This work proposes µGUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or signal representation, with exemplar demonstration in diffusion-weighted magnetic resonance imaging. Harnessing new deep learning architecture for automatic feature selection combined simulation-based inference and efficient sampling the distributions, µGUIDE bypasses high computational time cost conventional approaches does not rely on acquisition constraints define model-specific summary statistics. The obtained allow highlight degeneracies present definition quantify uncertainty ambiguity estimated parameters.

Language: Английский

---

Mean Kärger Model Water Exchange Rate in Brain

Imaging Neuroscience, Journal Year: 2024, Volume and Issue: 2, P. 1 - 16

Published: Jan. 1, 2024

Abstract Intercellular water exchange in brain is analyzed terms of the multi-compartment Kärger model (KM), and mean KM rate used as a summary statistic for characterizing processes. Prior work extended by deriving stronger lower bound that can be determined from time dependence diffusional kurtosis. In addition, an analytic formula giving kurtosis thin cylindrical neurites demonstrated, this applied to numerically test accuracy range parameters. Finally, measured vivo with imaging dorsal hippocampus cerebral cortex 8-month-old mice. From bound, found 46.1 ± 11.0 s-1 or greater 20.5 8.5 cortex.

Language: Английский

---

μGUIDE: a framework for quantitative imaging via generalized uncertainty-driven inference using deep learning

Published: Aug. 22, 2024

This work proposes μGUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or signal representation, with exemplar demonstration in diffusion-weighted MRI. Harnessing new deep learning architecture for automatic feature selection combined simulationbased inference and efficient sampling the distributions, μGUIDE bypasses high computational time cost conventional approaches does not rely on acquisition constraints define model-specific summary statistics. The obtained allow highlight degeneracies present definition quantify uncertainty ambiguity estimated parameters.

Language: Английский

---

μGUIDE: a framework for quantitative imaging via generalized uncertainty-driven inference using deep learning

Published: Oct. 30, 2024

This work proposes μGUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or signal representation, with exemplar demonstration in diffusion-weighted MRI. Harnessing new deep learning architecture for automatic feature selection combined simulationbased inference and efficient sampling the distributions, μGUIDE bypasses high computational time cost conventional approaches does not rely on acquisition constraints define model-specific summary statistics. The obtained allow highlight degeneracies present definition quantify uncertainty ambiguity estimated parameters.

Language: Английский

---