Cellular Exchange Imaging (CEXI): Evaluation of a diffusion model including water exchange in cells using numerical phantoms of permeable spheres

Magnetic Resonance in Medicine, Год журнала: 2023, Номер 90(4), С. 1625 - 1640

Опубликована: Июнь 6, 2023

Purpose Biophysical models of diffusion MRI have been developed to characterize microstructure in various tissues, but existing are not suitable for tissue composed permeable spherical cells. In this study we introduce Cellular Exchange Imaging (CEXI), a model tailored cells, and compares its performance related Ball & Sphere (BS) that neglects permeability. Methods We generated DW‐MRI signals using Monte‐Carlo simulations with PGSE sequence numerical substrates made cells their extracellular space range membrane From these signals, the properties were inferred both BS CEXI models. Results outperformed impermeable by providing more stable estimates cell size intracellular volume fraction time‐independent. Notably, accurately estimated exchange time low moderate permeability levels previously reported other studies (). However, highly (), parameters less stable, particularly coefficients. Conclusion This highlights importance modeling quantify cellular substrates. Future should evaluate clinical applications such as lymph nodes, investigate potential biomarker tumor severity, develop appropriate account anisotropic membranes.

Язык: Английский

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Low-field, high-gradient NMR shows diffusion contrast consistent with localization or motional averaging of water near surfaces

Magnetic Resonance Letters, Год журнала: 2023, Номер 3(2), С. 90 - 107

Опубликована: Апрель 9, 2023

Nuclear magnetic resonance (NMR) measurements of water diffusion have been extensively used to probe microstructure in porous materials, such as biological tissue, however primarily using pulsed gradient spin echo (PGSE) methods. Low-field single-sided NMR systems built-in static gradients (SG) much stronger than typical PGSE maximum strengths, which allows for the signal attenuation at extremely high b-values be explored. Here, we perform SG (SGSE) and stimulated (SGSTE) on cells, tissues, gels. Measurements fixed live neonatal mouse spinal cord, lobster ventral nerve starved yeast cells all show multiexponential a scale b with significant fractions observed × D0 ≫ 1 400 ms/μm2. These persistent trend surface-to-volume ratios these systems, expected from media theory. An exception found case vs. cords was attributed faster exchange or permeability millisecond timescale. Data suggests existence multiple processes neural may relevant modeling time-dependent gray matter. The multi-exponential is protons not macromolecules because it remains proportional normalized when specimen washed D2O. that persists also drastically reduced after delipidation, indicating originates lipid membranes restrict diffusion. stretched exponential character appears mono-exponential viewed (b×D0)1/3, suggesting originate localization motional averaging near sub-micron length scales. To try disambiguate two contributions, curves were compared varying temperatures. While align normalizing them scale, they separate scale. This supports source non-Gaussian displacements, but this interpretation still provisional due possible confounds heterogeneity, exchange, relaxation. types gel phantoms designed mimic extracellular matrix, one charged functional groups synthesized polyacrylic acid (PAC) another uncharged polyacrylamide (PAM), both exhibit 1, potentially interacting macromolecules. preliminary finding motivate future research into contrast mechanisms tissue low-field, high-gradient NMR.

Язык: Английский

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Diffusion MRI with free gradient waveforms on a high-performance gradient system: Probing restriction and exchange in the human brain

NeuroImage, Год журнала: 2023, Номер 283, С. 120409 - 120409

Опубликована: Окт. 13, 2023

The dependence of the diffusion MRI signal on time carries signatures restricted and exchange. Here we seek to highlight these in human brain by performing experiments using free gradient waveforms that are selectively sensitive two effects. We examine six healthy volunteers both strong ultra-strong gradients (80, 200 300 mT/m). In an experiment featuring a large set with different sensitivities exchange (150 samples), our results reveal unique time-dependence grey white matter, where former is characterised latter predominantly exhibits diffusion. Furthermore, show independently varying can be used map brain. consistently find matter at least twice as fast across all subjects strengths. shortest times observed this study were cerebellar cortex (115 ms). also assess feasibility future clinical applications method work, grey-white contrast obtained 25-minute mT/m protocol preserved 4-minute 10-minute 80 protocol. Our work underlines utility for detecting due vivo, which may potentially serve tool studying diseased tissue.

Язык: Английский

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DIMOND: DIffusion Model OptimizatioN with Deep Learning

Advanced Science, Год журнала: 2024, Номер 11(24)

Опубликована: Апрель 18, 2024

Abstract Diffusion magnetic resonance imaging is an important tool for mapping tissue microstructure and structural connectivity non‐invasively in the vivo human brain. Numerous diffusion signal models are proposed to quantify microstructural properties. Nonetheless, accurate estimation of model parameters computationally expensive impeded by image noise. Supervised deep learning‐based approaches exhibit efficiency superior performance but require additional training data may be not generalizable. A new DIffusion Model OptimizatioN framework using physics‐informed self‐supervised Deep learning entitled “DIMOND” address this problem. DIMOND employs a neural network map input optimizes minimizing difference between acquired synthetic generated via parametrized outputs. produces tensor results generalizable across subjects datasets. Moreover, outperforms conventional methods fitting sophisticated including kurtosis NODDI model. Importantly, reduces time from hours minutes, or seconds leveraging transfer learning. In summary, manner, high efficacy, increase practical feasibility adoption clinical neuroscientific applications.

Язык: Английский

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Improving Microstructural Estimation in Time‐Dependent Diffusion MRI With a Bayesian Method

Journal of Magnetic Resonance Imaging, Год журнала: 2024, Номер unknown

Опубликована: Май 20, 2024

Background Accurately fitting diffusion‐time‐dependent diffusion MRI ( t d ‐dMRI) models poses challenges due to complex and nonlinear formulas, signal noise, limited clinical data acquisition. Purpose Introduce a Bayesian methodology refine microstructural within the IMPULSED (Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion) model optimize prior distribution framework. Study Type Retrospective. Population Involving 69 pediatric patients (median age 6 years, interquartile range [IQR] 3–9 61% male) with 41 low‐grade 28 high‐grade gliomas, of which 76.8% were identified brainstem or cerebellum. Field Strength/Sequence 3 T, oscillating gradient spin‐echo (OGSE) pulsed (PGSE). Assessment The method's performance in cell diameter (), intracellular volume fraction extracellular coefficient () was compared against NLLS method, considering simulated experimental data. tumor region‐of‐interest (ROI) manually delineated on b 0 images. diagnostic distinguishing high‐ gliomas assessed, accuracy validated H&E‐stained pathology. Statistical Tests T‐test, receiver operating curve (ROC), area under (AUC) DeLong's test conducted. Significance considered at P < 0.05. Results manifested increased robust estimates simulation (RMSE decreased by 29.6%, 40.9%, 13.6%, STD 29.2%, 43.5%, 24.0%, respectively for , NLLS), indicating fewer outliers reduced error. Diagnostic grade similar both methods, however, method generated smoother maps (outliers ratio 45.3% ± 19.4%) marginal enhancement correlation H&E staining result r = 0.721 0.698 using NLLS, 0.5764). Data Conclusion proposed substantially enhances robustness estimation, suggesting its potential utility characterizing cellular microstructure. Evidence Level Technical Efficacy Stage 1

Язык: Английский

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