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Diffusion measurements in the kidney are affected not only by renal microstructure but also by physiological processes (i.e., glomerular filtration, water reabsorption, and urine formation). Because of the superposition of passive tissue diffusion, blood perfusion, and tubular pre‐urine flow, the limitations of the monoexponential apparent diffusion coefficient (ADC) model in assessing pathophysiological changes in renal tissue are becoming apparent and motivate the development of more advanced diffusion‐weighted imaging (DWI) variants. These approaches take advantage of the fact that the length scale probed in DWI measurements can be adjusted by experimental parameters, including diffusion‐weighting, diffusion gradient directions and diffusion time. This forms the basis by which advanced DWI models can be used to capture not only passive diffusion effects, but also microcirculation, compartmentalization, tissue anisotropy. In this review, we provide a comprehensive overview of the recent advancements in the field of renal DWI. Following a short introduction on renal structure and physiology, we present the key methodological approaches for the acquisition and analysis of renal DWI data, including intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), non‐Gaussian diffusion, and hybrid IVIM‐DTI. We then briefly summarize the applications of these methods in chronic kidney disease and renal allograft dysfunction. Finally, we discuss the challenges and potential avenues for further development of renal DWI. Level of Evidence: 5 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Magnetic susceptibility imaging may provide valuable information about chemical composition and microstructural organization of tissue. However, its estimation from the MRI signal phase is particularly difficult as it is sensitive to magnetic tissue properties ranging from the molecular to the macroscopic scale. The MRI Larmor frequency shift measured in white matter (WM) tissue depends on the myelinated axons and other magnetizable sources such as iron‐filled ferritin. We have previously derived the Larmor frequency shift arising from a dense medium of cylinders with scalar susceptibility and arbitrary orientation dispersion. Here, we extend our model to include microscopic WM susceptibility anisotropy as well as spherical inclusions with scalar susceptibility to represent subcellular structures, biologically stored iron, and so forth. We validate our analytical results with computer simulations and investigate the feasibility of estimating susceptibility using simple iterative linear least squares without regularization or preconditioning. This is done in a digital brain phantom synthesized from diffusion MRI measurements of an ex vivo mouse brain at ultra‐high field. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the use of pre‐excitation gradients for eddy current–nulled convex optimized diffusion encoding (Pre‐ENCODE) to mitigate eddy current–induced image distortions in diffusion‐weighted MRI (DWI). Methods: DWI sequences using monopolar (MONO), ENCODE, and Pre‐ENCODE were evaluated in terms of the minimum achievable echo time (TE min$$ {}_{\mathrm{min}} $$) and eddy current‐induced image distortions using simulations, phantom experiments, and in vivo DWI in volunteers (N=6$$ N=6 $$). Results: Pre‐ENCODE provided a shorter TE min$$ {}_{\mathrm{min}} $$ than MONO (71.0 ±$$ \pm $$ 17.7ms vs. 77.6 ±$$ \pm $$ 22.9ms) and ENCODE (71.0 ±$$ \pm $$ 17.7ms vs. 86.2 ±$$ \pm $$ 14.2ms) in 100%$$ \% $$ of the simulated cases for a commercial 3T MRI system with b‐values ranging from 500 to 3000 s/mm 2$$ {}^2 $$ and in‐plane spatial resolutions ranging from 1.0 to 3.0mm 2$$ {}^2 $$. Image distortion was estimated by intravoxel signal variance between diffusion encoding directions near the phantom edges and was significantly lower with Pre‐ENCODE than with MONO (10.1%$$ \% $$ vs. 22.7%$$ \% $$, p=6−5$$ p={6}^{-5} $$) and comparable to ENCODE (10.1%$$ \% $$ vs. 10.4%$$ \% $$, p=0.12$$ p=0.12 $$). In vivo measurements of apparent diffusion coefficients were similar in global brain pixels (0.37 [0.28,1.45]×10−3$$ \times 1{0}^{-3} $$ mm 2$$ {}^2 $$/s vs. 0.38 [0.28,1.45]×10−3$$ \times 1{0}^{-3} $$mm 2$$ {}^2 $$/s, p=0.25$$ p=0.25 $$) and increased in edge brain pixels (0.80 [0.17,1.49]×10−3$$ \times 1{0}^{-3} $$ mm 2$$ {}^2 $$/s vs. 0.70 [0.18,1.48]×10−3$$ \times 1{0}^{-3} $$mm 2$$ {}^2 $$/s, p=0.02$$ p=0.02 $$) for MONO compared to Pre‐ENCODE. Conclusion: Pre‐ENCODE mitigated eddy current–induced image distortions for diffusion imaging with a shorter TE min$$ {}_{\mathrm{min}} $$ than MONO and ENCODE. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: The problem of molecular diffusion in the soup of chemical reactions attracts mounting interest across fields ranging from chemistry to biophysics to material science. Chemical reactions involve bond breakup and formation, whose time scale is typically on the orders of fs to ps, while molecular diffusion occurs at time scales of μs to ms. The two processes are often considered orthogonal, given the vastly different scales. The serial results show that the enzyme's diffusion is enhanced in a substrate‐dependent manner, which was further extended to small molecule reaction systems, challenging this classical paradigm. However, the results from different groups using different techniques do not quantitatively agree, and a general mechanism is yet to be understood. We summarize experimental studies on diffusion problems and seek to reconcile the interpretation with understanding the limits of measurement tools and the chemical nature of reaction systems. Understanding molecular diffusion in chemical reactions will provide fresh thoughts in designing chemical systems such as molecular machines that harvest work at the nanoscale in a controllable manner. [ABSTRACT FROM AUTHOR]

Background: Diffusing alpha‐emitters radiation therapy ("Alpha‐DaRT") is a new method for treating solid tumors with alpha particles, relying on the release of the short‐lived alpha‐emitting daughter atoms of radium‐224 from interstitial sources inserted into the tumor. Alpha‐DaRT tumor dosimetry is governed by the spread of radium's progeny around the source, as described by an approximate framework called the "diffusion‐leakage model". The most important model parameters are the diffusion lengths of radon‐220 and lead‐212, and their estimation is therefore essential for treatment planning. Purpose: Previous works have provided initial estimates for the dominant diffusion length, by measuring the activity spread inside mice‐borne tumors several days after the insertion of an Alpha‐DaRT source. The measurements, taken when lead‐212 was in secular equilibrium with radium‐224, were interpreted as representing the lead‐212 diffusion length. The aim of this work is to provide first experimental estimates for the diffusion length of radon‐220, using a new methodology. Methods: The diffusion length of radon‐220 was estimated from autoradiography measurements of histological sections taken from 24 mice‐borne subcutaneous tumors of five different types. Unlike previous studies, the source dwell time inside the tumor was limited to 30 min, to prevent the buildup of lead‐212. To investigate the contribution of potential non‐diffusive processes, experiments were done in two sets: fourteen in vivo tumors, where during the treatment the tumors were still carried by the mice with active blood supply, and 10 ex‐vivo tumors, where the tumors were excised before source insertion and kept in a medium at 37∘C$37^\circ {\text{C}}$ with the source inside. Results: The measured diffusion lengths of radon‐220, extracted by fitting the recorded activity pattern up to 1.5 mm from the source, lie in the range 0.25−0.6mm${0.25-0.6}\nobreakspace {\text{mm}}$, with no significant difference between the average values measured in in‐vivo and ex‐vivo tumors: LRnin−vivo=0.40±0.08mm$L_{Rn}^{in-vivo}=0.40{\pm }0.08\nobreakspace {\text{mm}}$ versus LRnex−vivo=0.39±0.07mm$L_{Rn}^{ex-vivo}=0.39{\pm }0.07\nobreakspace {\text{mm}}$. However, in‐vivo tumors display an enhanced spread of activity 2–3 mm away from the source. This effect is not explained by the current model and is much less pronounced in ex‐vivo tumors. Conclusions: The average measured radon‐220 diffusion lengths in both in‐vivo and ex‐vivo tumors are consistent with published data on the diffusion length of radon in water and lie close to the upper limit of the previously estimated range of 0.2−0.4mm$0.2-0.4\nobreakspace {\text{mm}}$. The observation that close to the source there is no apparent difference between in‐vivo and ex‐vivo tumors, and the good agreement with the theoretical model in this region suggest that the spread of radon‐220 is predominantly diffusive in this region. The departure from the model prediction in in‐vivo tumors at large radial distances may hint at potential vascular contribution, which will be the subject of future works. [ABSTRACT FROM AUTHOR]

Freshwater ecosystems are believed to account for about half of global methane emissions, but this estimate is uncertain due to few measurements in some geographical regions and most important for our purposes, large local variations. We propose a simple, cheap (<200€), and effective solution to the urgent need for equipment that can provide accurate data on methane emission at high spatial and temporal resolution for many days, across many ecosystems and regions, and with limited inspection. A low‐cost metal oxide semiconductor sensor, placed in a floating chamber equipped with an air pump to flush the chamber's headspace at regular intervals, provides frequent and accurate high‐resolution measurements of diffusive and ebullitive methane emissions from lakes and rivers. We made several improvements that yield more useful data and enable the setup to operate for several days without visual inspection. To test the accuracy of the equipment, we compared its measurements of methane emission to parallel measurements by an expensive, commercially available laser sensor. The correspondence was excellent (R2 ≥ 0.94) and the deviation of measurements was minimal at fluxes within ranges encountered in the field. We also designed an R‐package (FluxSeparator), which associates the large sensor‐generated data sets with diffusive and ebullitive flux activity. Both the sensor setup and the R‐package are flexible and have been made publicly available to encourage others to use and further improve the equipment and calculations. Plain Language Summary: Lakes, streams and wetlands are believed to account for half of the global methane emissions. The uncertainties of these estimates are high due to limitations in measurement techniques and numbers. Here, we propose an easy and cheap do‐it‐yourself sensor and chamber system to measure methane and carbon dioxide (CO2) to overcome these limitations and a software solution to help calculate emissions from both diffusion and bubbles. The sensor is placed in the headspace of a floating chamber to measure methane concentration changes over time. After a set time interval, the sensor flushes the air in the headspace using a pump, allowing multiple automatic measurements of emissions without supervision. Comparisons with an expensive commercially available laser system showed excellent agreement. The software eases the calculation of numerous emission measurements by the automated sensor and chamber system. All the files used to create the sensor and the software are publicly available for everyone to use, while also allowing individual settings of sensors and software. Key Points: Cheap and accurate do‐it‐yourself sensor for measurements of methane and CO2 fluxesAn automated sensor allowing for multiple autonomous flux measurementsA new user‐friendly R‐packaged developed for separating and calculating multiple diffusive and ebullitive fluxes [ABSTRACT FROM AUTHOR]

Nuclear magnetic resonance (NMR) is an established method to determine self‐diffusion coefficients in liquids with high precision. The development of benchtop NMR spectrometers makes the method accessible to a wider community. In most cases, 1H NMR spectroscopy is used to determine self‐diffusion coefficients due to its high sensitivity. However, especially when using benchtop NMR spectrometers for the investigation of complex mixtures, the signals in 1H NMR spectra can overlap, hindering the precise determination of self‐diffusion coefficients. In 13C NMR spectroscopy, the signals of different compounds are generally well resolved. However, the sensitivity of 13C NMR is significantly lower than that of 1H NMR spectroscopy leading to very long measurement times, which makes diffusion coefficient measurements based on 13C NMR practically infeasible with benchtop NMR spectrometers. To circumvent this problem, we have combined two known pulse sequences, one for polarization transfer from 1H to the 13C nuclei (PENDANT) and one for the measurement of diffusion coefficients (PFG). The new method (PENPFG) was used to measure the self‐diffusion coefficients of three pure solvents (acetonitrile, ethanol and 1‐propanol) as well as in all their binary mixtures and the ternary mixture at various compositions. For comparison, also measurements of the same systems were carried out with a standard PFG‐NMR routine on a high‐field NMR instrument. The results are in good agreement and show that PENPFG is a useful tool for the measurement of the absolute value of the self‐diffusion coefficients in complex liquid mixtures with benchtop NMR spectrometers. [ABSTRACT FROM AUTHOR]

Background: Simultaneous multi‐slice diffusion‐weighted imaging (SMS‐DWI) can shorten acquisition time in abdominal imaging. Purpose: To investigate the agreement and reproducibility of apparent diffusion coefficient (ADC) from abdominal SMS‐DWI acquired with different vendors and different breathing schemes. Study Type: Prospective. Subjects: Twenty volunteers and 10 patients. Field Strength/Sequence: 3.0 T, SMS‐DWI with a diffusion‐weighted echo‐planar imaging sequence. Assessment: SMS‐DWI was acquired using breath‐hold and free‐breathing techniques in scanners from two vendors, yielding four scans in each participant. Average ADC values were measured in the liver, pancreas, spleen, and both kidneys. Non‐normalized ADC and ADCs normalized to the spleen were compared between vendors and breathing schemes. Statistical Tests: Paired t‐test or Wilcoxon signed rank test; intraclass correlation coefficient (ICC); Bland–Altman method; coefficient of variation (CV) analysis; significance level: P < 0.05. Results: Non‐normalized ADCs from the four SMS‐DWI scans did not differ significantly in the spleen (P = 0.262, 0.330, 0.166, 0.122), right kidney (P = 0.167, 0.538, 0.957, 0.086), and left kidney (P = 0.182, 0.281, 0.504, 0.405), but there were significant differences in the liver and pancreas. For normalized ADCs, there were no significant differences in the liver (P = 0.315, 0.915, 0.198, 0.799), spleen (P = 0.815, 0.689, 0.347, 0.423), pancreas (P = 0.165, 0.336, 0.304, 0.584), right kidney (P = 0.165, 0.336, 0.304, 0.584), and left kidney (P = 0.496, 0.304, 0.443, 0.371). Inter‐reader agreements of non‐normalized ADCs were good to excellent (ICCs ranged from 0.861 to 0.983), and agreement and reproducibility were good to excellent depending on anatomic location (CVs ranged from 3.55% to 13.98%). Overall CVs for abdominal ADCs from the four scans were 6.25%, 7.62%, 7.08, and 7.60%. Data Conclusion: The normalized ADCs from abdominal SMS‐DWI may be comparable between different vendors and breathing schemes, showing good agreement and reproducibility. ADC changes above approximately 8% may potentially be considered as a reliable quantitative biomarker to assess disease or treatment‐related changes. Level of Evidence: 2 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Reliable structure elucidation of covalent organic cage compounds remains challenging as routine analysis might leave ambiguities. Diffusion‐ordered NMR spectroscopy (DOSY) allows insight into the molecular size and mass of the species present in solution, but a systematic evaluation of the diffusion behavior for cage assemblies is rarely considered. Here we report the synthesis of four series of covalent organic cages based on tribenzotriquinacenes and diboronic acids with varying geometry and exohedral substituents. We provide a guideline for the consistent measurement of diffusion coefficients from 1H‐DOSY NMR spectroscopy, which was utilized to study the diffusion behavior for the whole set of cages and selected examples from the literature. For structurally similar cages, a linear correlation between the solvodynamic volume and the molecular mass allows precise size determination. For more complex systems, multiple parameters, such as window size or rigid exohedral functionalization. further modulate cage diffusion in solution. [ABSTRACT FROM AUTHOR]

Purpose: Accelerate multislice 2D MRI by using RF pulses that simultaneously act on different slices to combine contrast preparation and image acquisition. Theory and Methods: MRI applications often require the use of multiple RF pulses to generate desired contrast and prepare the signal for readout. Examples are the use of inversion prepulses to generate T1 contrast, or the use of spin‐echo preparations to generate T2 or diffusion contrast. In multislice MRI, this separation of contrast preparation and readout can render scans time‐inefficient and lengthy. We introduce a class of pulse sequences that overcomes this inefficiency by combining contrast preparation and signal readout. This is accomplished by using RF pulses that manipulate the magnetization of multiple slices simultaneously and a gradient crusher scheme that selects a target slice for readout. Results: Feasibility of the method was demonstrated for spin echo–based measurement of water diffusion and tissue pulsation in human brain at 3 T. Increases in time‐efficiency and reductions in scan time were highly dependent on specific implementation and reached as high as 25% and 53%, respectively. Conclusion: A novel approach to multislice MRI is demonstrated that reduces scan time for specific applications. [ABSTRACT FROM AUTHOR]

Self‐diffusion of poly(acrylic acid) (PAA) in dilute solution is studied by pulsed field gradient (PFG)‐NMR, also known as DOSY, and correlated to molecular weight with scaling laws at varying pH. Fitting a diffusion coefficient distribution model to PFG‐NMR data, the molecular weight polydispersity of commercial PAA samples is quantified. Due to good agreement with GPC data, diffusion measurements offer a viable alternative to estimate chain length properties with minimal sample preparation. Similar results are achieved by dynamic light scattering (DLS), however, with lower precision. The evolution of the scaling exponent with increasing pH indicates a transition from a globular state in highly acidic conditions (ν = 0.46) to a partially stretched conformation with a maximum at pH 5 (ν = 0.67). At higher pH, counterion screening counteracts chain expansion, while an unexpected increase in the width of the diffusion coefficient distribution occurs. A universal scaling law of the diffusivity applies for a chain length range of 25 ≤ N ≤ 1585, albeit with distinctly different scaling exponents. While opposing arguments are brought forward in recent literature concerning the pH‐dependence of short‐chain conformations, analysis supports the view that chain conformation is significantly affected by pH, even for short chains. [ABSTRACT FROM AUTHOR]

The ZLC technique is a powerful technique for the measurement of diffusion in nanoporous materials using mg quantities of a sample. Questions remain on whether the measured properties are representative of larger samples. A methodology has been developed to carry out single pellet experiments to determine average properties and variability of equilibrium and kinetic properties. To demonstrate the approach, n‐pentane on HISIV3000 extrudates was used as the test system, which was shown to be macropore diffusion controlled. Results on nine pellets provided average values showing a perfect match to breakthrough column experiments with a sample two orders of magnitudes larger. [ABSTRACT FROM AUTHOR]

Purpose: This paper presents a hierarchical modeling approach for estimating cardiomyocyte major and minor diameters and intracellular volume fraction (ICV) using diffusion‐weighted MRI (DWI) data in ex vivo mouse hearts. Methods: DWI data were acquired on two healthy controls and two hearts 3 weeks post transverse aortic constriction (TAC) using a bespoke diffusion scheme with multiple diffusion times (Δ$$ \Delta $$), q‐shells and diffusion encoding directions. Firstly, a bi‐exponential tensor model was fitted separately at each diffusion time to disentangle the dependence on diffusion times from diffusion weightings, that is, b‐values. The slow‐diffusing component was attributed to the restricted diffusion inside cardiomyocytes. ICV was then extrapolated at Δ=0$$ \Delta =0 $$ using linear regression. Secondly, given the secondary and the tertiary diffusion eigenvalue measurements for the slow‐diffusing component obtained at different diffusion times, major and minor diameters were estimated assuming a cylinder model with an elliptical cross‐section (ECS). High‐resolution three‐dimensional synchrotron X‐ray imaging (SRI) data from the same specimen was utilized to evaluate the biophysical parameters. Results: Estimated parameters using DWI data were (control 1/control 2 vs. TAC 1/TAC 2): major diameter—17.4 μ$$ \mu $$m/18.0 μ$$ \mu $$m versus 19.2 μ$$ \mu $$m/19.0 μ$$ \mu $$m; minor diameter—10.2 μ$$ \mu $$m/9.4 μ$$ \mu $$m versus 12.8 μ$$ \mu $$m/13.4 μ$$ \mu $$m; and ICV—62%/62% versus 68%/47%. These findings were consistent with SRI measurements. Conclusion: The proposed method allowed for accurate estimation of biophysical parameters suggesting cardiomyocyte diameters as sensitive biomarkers of hypertrophy in the heart. [ABSTRACT FROM AUTHOR]

Purpose: Preliminary study to determine whether double pulsed field gradient (PFG) diffusion MRI is sensitive to key features of muscle microstructure related to function. Methods: The restricted diffusion profile of molecules in models of muscle microstructure derived from histology were systematically simulated using a numerical simulation approach. Diffusion tensor subspace imaging analysis of the diffusion signal was performed, and spherical anisotropy (SA) was calculated for each model. Linear regression was used to determine the predictive capacity of SA on the fiber area, fiber diameter, and surface area to volume ratio of the models. Additionally, a rat model of muscle hypertrophy was scanned using a single PFG and a double PFG pulse sequence, and the restricted diffusion measurements were compared with histological measurements of microstructure. Results: Excellent agreement between SA and muscle fiber area (r2 = 0.71; p < 0.0001), fiber diameter (r2 = 0.83; p < 0.0001), and surface area to volume ratio (r2 = 0.97; p < 0.0001) in simulated models was found. In a scanned rat leg, the distribution of these microstructural features measured from histology was broad and demonstrated that there is a wide variance in the microstructural features observed, similar to the SA distributions. However, the distribution of fractional anisotropy measurements in the same tissue was narrow. Conclusions: This study demonstrates that SA—a scalar value from diffusion tensor subspace imaging analysis—is highly sensitive to muscle microstructural features predictive of function. Furthermore, these techniques and analysis tools can be translated to real experiments in skeletal muscle. The increased dynamic range of SA compared with fractional anisotropy in the same tissue suggests increased sensitivity to detecting changes in tissue microstructure. [ABSTRACT FROM AUTHOR]

The article discusses the use of the intravoxel incoherent motion (IVIM) model in liver MRI to measure diffusion and perfusion without contrast agents. It highlights challenges in obtaining repeatable measurements of IVIM parameters in abdominal organs, particularly the perfusion parameter Df. The study by Vasquez et al. in the Journal of Magnetic Resonance Imaging compares different strategies for respiratory motion management in liver IVIM, showing that different protocols yield varying results for IVIM parameters. The findings emphasize the need for standardization in IVIM acquisition and analysis techniques to improve reproducibility. [Extracted from the article]

In this article, we study inverse source problems for time‐fractional diffusion equations from a posteriori boundary measurement. Using the memory effect of these class of equations, we solve these inverse problems for several class of space‐ or time‐dependent source terms. We prove also the unique determination of a general class of space–time‐dependent separated variables source terms from such measurement. Our approach is based on the study of singularities of the Laplace transform in time of boundary traces of solutions of time‐fractional diffusion equations. [ABSTRACT FROM AUTHOR]

Deterioration of biomaterial is subject to the interaction between biomolecules and water, which is often of special interest to the realization of long‐term preservation. In previous study, we reported the relationship between dielectric rotational relaxation time and the ν2 + ν3 NIR combination band of water as well as the theory behind it using saccharide solutions as examples and proposed a method accordingly which takes advantages of the infrared microscope to measure the dielectric relaxation time of water in biomaterials. In this work, we examined protein, polypeptide, sugar alcohol and some shelf‐stable vegetables to further discuss its applicability and limit. We also proved that this method can be used for measuring water diffusion coefficient and showed example of how the diffusion coefficient can be used to predict water distribution during drying process at a significantly enhanced spatial resolution relative to other techniques. Practical applications: Molecular mobility of water determines many properties of biomaterials and can be characterized by different spectroscopies. This paper examined the applicability of a method of calculating the dielectric relaxation time of water using NIR spectrum on different solutions and proved that this method may also be used for calculating the diffusion coefficient of water, a parameter subject to molecular mobility and crucial in the drying process. This paper also provided example of the application of this method in the calculation of water distribution during drying process. The high spatial resolution of infrared microscope relative to other techniques may enable the measurement of water dynamics in biomaterials in a much quicker way and smaller scale. [ABSTRACT FROM AUTHOR]

Background: Renal diffusion‐weighted imaging (DWI) involves microstructure and microcirculation, quantified with diffusion tensor imaging (DTI), intravoxel incoherent motion (IVIM), and hybrid models. A better understanding of their contrast may increase specificity. Purpose: To measure modulation of DWI with cardiac phase and flow‐compensated (FC) diffusion gradient waveforms. Study Type: Prospective. Population: Six healthy volunteers (ages: 22–48 years, five females), water phantom. Field Strength/Sequence: 3‐T, prototype DWI sequence with 2D echo‐planar imaging, and bipolar (BP) or FC gradients. 2D Half‐Fourier Single‐shot Turbo‐spin‐Echo (HASTE). Multiple‐phase 2D spoiled gradient‐echo phase contrast (PC) MRI. Assessment: BP and FC water signal decays were qualitatively compared. Renal arteries and velocities were visualized on PC‐MRI. Systolic (peak velocity), diastolic (end stable velocity), and pre‐systolic (before peak velocity) phases were identified. Following mutual information‐based retrospective self‐registration of DWI within each kidney, and Marchenko‐Pastur Principal Component Analysis (MPPCA) denoising, combined IVIM‐DTI analysis estimated mean diffusivity (MD), fractional anisotropy (FA), and eigenvalues (λi) from tissue diffusivity (Dt), perfusion fraction (fp), and pseudodiffusivity (Dp, Dp,axial, Dp,radial), for each tissue (cortex/medulla, segmented on b0/FA respectively), phase, and waveform (BP, FC). Monte Carlo water diffusion simulations aided data interpretation. Statistical Tests: Mixed model regression probed differences between tissue types and pulse sequences. Univariate general linear model analysis probed variations among cardiac phases. Spearman correlations were measured between diffusion metrics and renal artery velocities. Statistical significance level was set at P < 0.05. Results: Water BP and FC signal decays showed no differences. Significant pulse sequence dependence occurred for λ1, λ3, FA, Dp, fp, Dp,axial, Dp,radial in cortex and medulla, and medullary λ2. Significant cortex/medulla differences occurred with BP for all metrics except MD (systole [P = 0.224]; diastole [P = 0.556]). Significant phase dependence occurred for Dp, Dp,axial, Dp,radial for BP and medullary λ1, λ2, λ3, MD for FC. FA correlated significantly with velocity. Monte Carlo simulations indicated medullary measurements were consistent with a 34 μm tubule diameter. Data Conclusion: Cardiac gating and flow compensation modulate of measurements of renal diffusion. Evidence Level: 2 Technical Efficacy Stage: 2 [ABSTRACT FROM AUTHOR]

Background: Dynamic diffusion magnetic resonance imaging (ddMRI) metrics can assess transient microstructural alterations in tissue diffusivity but requires additional scan time hindering its clinical application. Purpose: To determine whether a diffusion gradient table can simultaneously acquire data to estimate dynamic and diffusion tensor imaging (DTI) metrics. Study Type: Prospective. Subjects: Seven healthy subjects, 39 epilepsy patients (15 female, 31 male, age ± 15). Field Strength/Sequence: Two‐dimensional diffusion MRI (b = 1000 s/mm2) at a field strength of 3 T. Sessions in healthy subjects—standard ddMRI (30 directions), standard DTI (15 and 30 directions), and nested cubes scans (15 and 30 directions). Sessions in epilepsy patients—two 30 direction (standard ddMRI, 10 nested cubes) or two 15 direction scans (standard DTI, 5 nested cubes). Assessment: Fifteen direction DTI was repeated twice for within‐session test–retest measurements in healthy subjects. Bland–Altman analysis computed bias and limits of agreement for DTI metrics using test–retest scans and standard 15 direction vs. 5 nested cubes scans. Intraclass correlation (ICC) analysis compared tensor metrics between 15 direction DTI scans (standard vs. 5 nested cubes) and the coefficients of variation (CoV) of trace and apparent diffusion coefficient (ADC) between 30 direction ddMRI scans (standard vs. 10 nested cubes). Statistical Tests: Bland–Altman and ICC analysis using a P‐value of 0.05 for statistical significance. Results: Correlations of mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were strong and significant in gray (ICC > 0.95) and white matter (ICC > 0.95) between standard vs. nested cubes DTI acquisitions. Correlation of white matter fractional anisotropy was also strong (ICC > 0.95) and significant. ICCs of the CoV of dynamic ADC measured using repeated cubes and nested cubes acquisitions were modest (ICC >0.60), but significant in gray matter. Conclusion: A nested cubes diffusion gradient table produces tensor‐based and dynamic diffusion measurements in a single acquisition. Level of Evidence: 2 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

This article discusses the accessibility and success of benchtop NMR spectroscopy, which allows for the use of NMR in situations where high-field NMR is not feasible. Benchtop NMR systems do not require cryogenic fluids, which is advantageous due to the scarcity and unstable pricing of liquid helium. The article highlights the advancements in benchtop NMR, including improved pulse sequences, signal processing, and data analysis. It also explores the wide range of applications in reaction monitoring and quality control in various fields. The special issue includes research articles and a mini-review, providing a comprehensive view of current developments and applications in benchtop/cryofree NMR. [Extracted from the article]

Reactive flash sintering is a process where off‐the‐shelf powders of elemental oxides can be simultaneously sintered and reacted to form a multicomponent oxide in a matter of seconds at low furnace temperatures. The fact that several cation species, each residing within their own particles, can migrate over distances of several micrometers, and mix on the atomic scale to form multicomponent oxides, so quickly, is quite remarkable. The question arises as to the rate of this solid‐state diffusion phenomenon. In this paper, we present measurements of this diffusion coefficient from live flash experiments. The results are obtained from millimeter scale bilayers of yttria‐stabilized zirconia and lanthana where the flash initiates in the zirconia layer and then migrates into the lanthana layer, forming lanthanum zirconates. The velocities of migration of the flash‐front, coupled with measurements of the length scale of the profile of zirconium and lanthanum interdiffusion, across the bilayer interface, provide an estimate of the effective diffusion coefficient. These measurements give a value for the cation diffusion to lie in the range of 2.5 × 10−10 m2 s−1 at 1380°C, with an activation energy of 200–250 kJ mol−1. In comparison, the cation diffusion coefficient in yttria‐stabilized zirconia, at 1350°C, is stated to be 1.1 × 10−20 m2 s−1 with an activation energy of ∼550 kJ mol−1. A pause for reflection. [ABSTRACT FROM AUTHOR]

Purpose: Oscillating gradient spin‐echo (OGSE) sequences have demonstrated an ability to probe time‐dependent microstructural features, although they often suffer from low SNR due to increased TEs. In this work we introduce frequency‐tuned bipolar (FTB) gradients as a variation of oscillating gradients with reduced TE and demonstrate their utility by mapping the frequency dispersion of kurtosis in human subjects. Methods: An FTB oscillating gradient waveform is presented that provides encoding of 1.5 net oscillation periods, thereby reducing the TE of the acquisition. Simulations were performed to determine an optimal protocol based on the SNR of kurtosis frequency dispersion—defined as the difference in kurtosis between pulsed and oscillating gradient acquisitions. Healthy human subjects were scanned at 7T using pulsed gradient and an optimized 23 Hz FTB protocol, which featured a maximum b‐value of 2500 s/mm2. In addition, to directly compare existing methods, measurements using traditional cosine OGSE were also acquired. Results: FTB oscillating gradients demonstrated equivalent frequency‐dependent diffusion measurements compared with cosine‐modulated OGSE while enabling a significant reduction in TE. Optimization and in vivo results suggest that FTB gradients provide increased SNR of kurtosis dispersion maps compared with traditional cosine OGSE. The optimized FTB gradient protocol demonstrated consistent reductions in apparent kurtosis values and increased diffusivity in generated frequency dispersion maps. Conclusions: This work presents an alternative to traditional cosine OGSE sequences, enabling more time‐efficient acquisitions of frequency‐dependent diffusion quantities as demonstrated through in vivo kurtosis frequency dispersion maps. [ABSTRACT FROM AUTHOR]

Purpose: To assess the reliability of measuring diffusivity, diffusional kurtosis, and cellular‐interstitial water exchange time with long diffusion times (100–800 ms) using stimulated‐echo DWI. Methods: Time‐dependent diffusion MRI was tested on two well‐established diffusion phantoms and in 5 patients with head and neck cancer. Measurements were conducted using an in‐house diffusion‐weighted STEAM‐EPI pulse sequence with multiple diffusion times at a fixed TE on three scanners. We used the weighted linear least‐squares fit method to estimate time‐dependent diffusivity, D(t)$$ D(t) $$, and diffusional kurtosis, K(t)$$ K(t) $$. Additionally, the Kärger model was used to estimate cellular‐interstitial water exchange time (τex$$ {\tau}_{ex} $$) from K(t)$$ K(t) $$. Results: Diffusivity measured by time‐dependent STEAM‐EPI measurements and commercial SE‐EPI showed comparable results with R2 above 0.98 and overall 5.4 ± 3.0% deviation across diffusion times. Diffusional kurtosis phantom data showed expected patterns: constant D$$ D $$ and K$$ K $$ = 0 for negative controls and slow varying D$$ D $$ and K$$ K $$ for samples made of nanoscopic vesicles. Time‐dependent diffusion MRI in patients with head and neck cancer found that the Kärger model could be considered valid in 72% ± 23% of the voxels in the metastatic lymph nodes. The median cellular‐interstitial water exchange time estimated for lesions was between 58.5 ms and 70.6 ms. Conclusions: Based on two well‐established diffusion phantoms, we found that time‐dependent diffusion MRI measurements can provide stable diffusion and kurtosis values over a wide range of diffusion times and across multiple MRI systems. Moreover, estimation of cellular‐interstitial water exchange time can be achieved using the Kärger model for the metastatic lymph nodes in patients with head and neck cancer. [ABSTRACT FROM AUTHOR]

Luciferin is one of Nature's most widespread luminophores, and enzymes that catalyze luciferin luminescence are the basis of successful commercial "glow" assays for gene expression and metabolic ATP formation. Herein we report an electrochemical method to promote firefly's luciferin luminescence in the absence of its natural biocatalyst—luciferase. We have gained experimental and computational insights on the mechanism of the enzyme‐free luciferin electrochemiluminescence, demonstrated its spectral tuning from green to red by means of electrolyte engineering, proven that the colour change does not require, as still debated, a keto/enol isomerization of the light emitter, and gained evidence of the electrostatic‐assisted stabilization of the charge‐transfer excited state by double layer electric fields. Luciferin's electrochemiluminescence, as well as the in situ generation of fluorescent oxyluciferin, are applied towards an optical measurement of diffusion coefficients. [ABSTRACT FROM AUTHOR]

Brownian motion of Cowpea mosaic virus (CPMV) in water was measured using small-angle X-ray photon correlation spectroscopy (SA-XPCS) at 19.2 ms time resolution. It was found that the decorrelation time (Q) = 1/DQ2 up to Q = 0.091 nm-1. The hydrodynamic radius RH determined from XPCS using Stokes-Einstein diffusion D = kT/(6RH) is 43% larger than the geometric radius R0 determined from SAXS in the 0.007 M K3PO4 buffer solution, whereas it is 80% larger for CPMV in 0.5 M NaCl and 104% larger in 0.5 M (NH4)2SO4, a possible effect of aggregation as well as slight variation of the structures of the capsid resulting from the salt-protein interactions. [ABSTRACT FROM AUTHOR]

The importance of predicting the diffusion coefficient to describe the diffusion process in glassy polymers is obvious. However, most of the direct measurements of diffusion coefficients are time‐consuming, expensive and system intrusive. Here, we report a facile method to measure the apparent diffusion constant on the cross‐section of thin PVA film with a device that is structured with a thin conductive film. The results can employ diffusion and the solution conductivity theory well. The diffusion time and the conductivity of the device are determined by the process of solution passing through the cross‐section of thin PVA film and reaching the conductive substrate. The data detected the apparent diffusion constant in diffusion and the concentration gradient of the solution on the interface between the PVA film and conductive film. This device provides a simple, rapid, and effective way to study the apparent diffusion constant in PVA film. It holds great potential in predicting the diffusion process of a thin glassy polymer film. [ABSTRACT FROM AUTHOR]

Purpose: To examine the time‐dependent diffusion of fluorinated (19F) gas in human lungs for determination of surface‐to‐volume ratio in comparison to results from hyperpolarized 129Xe and lung function testing in healthy volunteers and patients with chronic obstructive pulmonary disease. Methods: Diffusion of fluorinated gas in the short‐time regime was measured using multiple gradient‐echo sequences with a single pair of trapezoidal gradient pulses. Pulmonary surface‐to‐volume ratio was calculated using a first‐order approximation of the time‐dependent diffusion in a study with 20 healthy volunteers and 22 patients with chronic obstructive pulmonary disease. The repeatability after 7 days as well as the correlation with hyperpolarized 129Xe diffusion MRI and lung function testing was analyzed. Results: Using 19F diffusion MRI, the median surface‐to‐volume ratio is significantly decreased in chronic obstructive pulmonary disease patients (S/V = 126 cm−1 [87–144 cm−1]) compared with healthy volunteers (S/V = 164 cm−1 [160–84 cm−1], p < 0.0001). No significant difference was found between measurements within 7 days for healthy (p = 0.88, median coefficient of variation = 4.3%) and diseased subjects (p = 0.58, median coefficient of variation= 6.7%). Linear correlations were found with S/V from 129Xe diffusion MRI (r = 0.85, p = 0.001) and the forced expiratory volume in 1 second (r = 0.68, p < 0.0001). Conclusion: Examination of lung microstructure using time‐dependent diffusion measurement of inhaled 19F is feasible, repeatable, and correlates with established measurements. [ABSTRACT FROM AUTHOR]

Diverse phytoplankton exude polysaccharides that can form a viscous mucus layer surrounding their cells, facilitating complex chemical exchanges with bacteria. An unexplored ramification of mucus production is the influence of its viscosity on the diffusion of nutrients such as vitamins to the cell. Here, I use simulations to demonstrate that mucus viscosity increases the nutrient concentration gradient but always reduces the flux to the cell. Uptake is marginally improved during nutrient pulses, as the mucus acts like a sponge by retaining nutrients near the cell. Lower uptake in the presence of mucus presents a fitness cost that any ecological benefits of mucus must outweigh. I derive a relationship between mucus viscosity and nutrient uptake that can be used to test hypotheses of mutualistic interactions between phytoplankton and bacteria. This work emphasizes the need for empirical measurements of macronutrient diffusion through mucus layers and provides a framework for interpreting those results. [ABSTRACT FROM AUTHOR]

Purpose: To design a b value and first‐order motion moment (M1) optimized data acquisition for repeatable intravoxel incoherent motion (IVIM) quantification in the liver. Methods: Cramer‐Rao lower bound optimization was performed to determine optimal monopolar and optimal 2D samplings of the b‐M1 space based on noise performance. Monte Carlo simulations were used to evaluate the bias and variability in estimates obtained using the proposed optimal samplings and conventional monopolar sampling. Diffusion MRI of the liver was performed in 10 volunteers using 3 IVIM acquisitions: conventional monopolar, optimized monopolar, and b‐M1‐optimized gradient waveforms (designed based on the optimal 2D sampling). IVIM parameter maps of diffusion coefficient, perfusion fraction, and blood velocity SD were obtained using nonlinear least squares fitting. Noise performance (SDs), stability (outlier percentage), and test–retest or scan–rescan repeatability (intraclass correlation coefficients) were evaluated and compared across acquisitions. Results: Cramer‐Rao lower bound and Monte Carlo simulations demonstrated improved noise performance of the optimal 2D sampling in comparison to monopolar samplings. Evaluating the designed b‐M1‐optimized waveforms in healthy volunteers, significant decreases (p < 0.05) in the SDs and outlier percentages were observed for measurements of diffusion coefficient, perfusion fraction, and blood velocity SD in comparison to measurements obtained using monopolar samplings. Good‐to‐excellent repeatability (intraclass correlation coefficients ≥ 0.77) was observed for all 3 parameters in both the right and left liver lobes using the b‐M1‐optimized waveforms. Conclusions: 2D b‐M1‐optimized data acquisition enables repeatable IVIM quantification with improved noise performance. 2D acquisitions may advance the establishment of IVIM quantitative biomarkers for liver diseases. [ABSTRACT FROM AUTHOR]

Purpose: To address the long echo times and relatively weak diffusion sensitization that typically limit oscillating gradient spin‐echo (OGSE) experiments, an OGSE implementation combining spiral readouts, gap‐filled oscillating gradient shapes providing stronger diffusion encoding, and a high‐performance gradient system is developed here and utilized to investigate the tradeoff between b‐value and maximum OGSE frequency in measurements of diffusion dispersion (i.e., the frequency dependence of diffusivity) in the in vivo human brain. In addition, to assess the effects of the marginal flow sensitivity introduced by these OGSE waveforms, flow‐compensated variants are devised for experimental comparison. Methods: Using DTI sequences, OGSE acquisitions were performed on three volunteers at b‐values of 300, 500, and 1000 s/mm2 and frequencies up to 125, 100, and 75 Hz, respectively; scans were performed for gap‐filled oscillating gradient shapes with and without flow sensitivity. Pulsed gradient spin‐echo DTI acquisitions were also performed at each b‐value. Upon reconstruction, mean diffusivity (MD) maps and maps of the diffusion dispersion rate were computed. Results: The power law diffusion dispersion model was found to fit best to MD measurements acquired at b = 1000 s/mm2 despite the associated reduction of the spectral range; this observation was consistent with Monte Carlo simulations. Furthermore, diffusion dispersion rates without flow sensitivity were slightly higher than flow‐sensitive measurements. Conclusion: The presented OGSE implementation provided an improved depiction of diffusion dispersion and demonstrated the advantages of measuring dispersion at higher b‐values rather than higher frequencies within the regimes employed in this study. [ABSTRACT FROM AUTHOR]

The optical response of hierarchical materials is convoluted, which hinders their direct study and property control. Transparent wood (TW) is an emerging biocomposite in this category, which adds optical function to the structural properties of wood. Nano‐ and microscale inhomogeneities in composition, structure, and at interfaces strongly affect light transmission and haze. While interface manipulation can tailor TW properties, the realization of optically clear wood requires detailed understanding of light–TW interaction mechanisms. Herein is shown how material scattering and absorption coefficients can be extracted from a combination of experimental spectroscopic measurements and a photon diffusion model. Contributions from different length scales can thus be deciphered and quantified. It is shown that forward scattering dominates haze in TW, primarily caused by refractive index (RI) mismatch between the wood substrate and the polymer phase. Rayleigh scattering from the wood cell wall and absorption from residual lignin have minor effects on transmittance, but the former affects haze. Results provide guidance for material design of transparent hierarchical composites toward desired optical functionality; it is demonstrated experimentally how transmittance and haze of TW can be controlled over a broad range. [ABSTRACT FROM AUTHOR]

Lubricating greases were investigated by nuclear magnetic resonance/magnetic resonance imaging (NMR/MRI) to get insight into their structure and into their response to mechanical forces, which is related to bleeding and aging. The investigated greases are based on metallic soaps of fatty acids and oils, whereby LiOH is often used. These organic soaps act as thickeners and provide a network in which oils and their additives are embedded. Lubricating greases can thus be considered as a class of substances similar to oleogels or even hydrogels. Questions arise about translational mobility of guest molecules, mainly base oil, in these networks. Molecular structuring and interactions within the network of thickeners are of interest as they are related to macroscopic stability. Apart from NMR spectroscopy (1H‐, 7Li‐ and 31P‐NMR), spectrally resolved relaxation and diffusion measurements are used for characterization. In addition, magic angle spinning (MAS)‐NMR was combined with 1H‐MRI to investigate the impact of mechanical stress and swelling of lubricating greases. [ABSTRACT FROM AUTHOR]

Although nanometer‐sized aluminum hydroxide clusters (i.e., ϵ‐Al13, [Al13O4(OH)24(H2O)12]7+) command a central role in aluminum ion speciation and transformations between minerals, measurement of their translational diffusion is often limited to indirect methods. Here, 27Al pulsed field gradient stimulated echo nuclear magnetic resonance (PFGSTE NMR) spectroscopy has been applied to the AlO4 core of the ϵ‐Al13 cluster with complementary theoretical simulations of the diffusion coefficient and corresponding hydrodynamic radii from a boundary element‐based calculation. The tetrahedral AlO4 center of the ϵ‐Al13 cluster is symmetric and exhibits only weak quadrupolar coupling, which results in favorable T1 and T227Al NMR relaxation coefficients for 27Al PFGSTE NMR studies. Stokes–Einstein relationship was used to relate the 27Al diffusion coefficient of the ϵ‐Al13 cluster to the hydrodynamic radius for comparison with theoretical simulations, dynamic light scattering from literature, and previously published 1H PFGSTE NMR studies of chelated Keggin clusters. This first‐of‐its‐kind observation proves that 27Al PFGSTE NMR diffusometry can probe symmetric Al environments in polynuclear clusters of greater molecular weight than previously considered. [ABSTRACT FROM AUTHOR]

Purpose: Amide proton transfer‐weighted (APTw) MRI provides a non‐invasive pH‐sensitive image, complementing perfusion and diffusion imaging for refined stratification of ischemic tissue. Although the commonly used magnetization transfer (MT) asymmetry (MTRasym) calculation reasonably corrects the direct RF saturation effect, it is susceptible to the concomitant semisolid macromolecular MT contribution. Therefore, this study aimed to compare the performance of MTRasym and magnetization transfer and relaxation‐normalized APT (MRAPT) analyses under 2 representative experimental conditions. Methods: Multiparametric MRI scans were performed in a rodent model of acute stroke, including relaxation, diffusion, and Z spectral images under 2 representative RF levels of 0.75 and 1.5 µT. Both MTRasym and MRAPT values in the ischemic diffusion lesion and the contralateral normal areas were compared using correlation and Bland‐Altman tests. In addition, the acidic lesion volumes were compared. Results: MRAPT measurements from the diffusion lesion under the 2 conditions were highly correlated (R2 = 0.97) versus MTRasym measures (R2 = 0.58). The pH lesion sizes determined from MRAPT analysis were in good agreement (178 ± 43 mm3 vs. 186 ± 55 mm3 for B1 of 0.75 and 1.5 µT, respectively). Conclusions: The study demonstrated that MRAPT analysis could be generalized to moderately different RF amplitudes, providing a more consistent depiction of acidic lesions than the MTRasym analysis. [ABSTRACT FROM AUTHOR]

Purpose: Supervised machine learning (ML) provides a compelling alternative to traditional model fitting for parameter mapping in quantitative MRI. The aim of this work is to demonstrate and quantify the effect of different training data distributions on the accuracy and precision of parameter estimates when supervised ML is used for fitting. Methods: We fit a two‐ and three‐compartment biophysical model to diffusion measurements from in‐vivo human brain, as well as simulated diffusion data, using both traditional model fitting and supervised ML. For supervised ML, we train several artificial neural networks, as well as random forest regressors, on different distributions of ground truth parameters. We compare the accuracy and precision of parameter estimates obtained from the different estimation approaches using synthetic test data. Results: When the distribution of parameter combinations in the training set matches those observed in healthy human data sets, we observe high precision, but inaccurate estimates for atypical parameter combinations. In contrast, when training data is sampled uniformly from the entire plausible parameter space, estimates tend to be more accurate for atypical parameter combinations but may have lower precision for typical parameter combinations. Conclusion: This work highlights that estimation of model parameters using supervised ML depends strongly on the training‐set distribution. We show that high precision obtained using ML may mask strong bias, and visual assessment of the parameter maps is not sufficient for evaluating the quality of the estimates. [ABSTRACT FROM AUTHOR]

Purpose: To use diffusion measurements to map the spatial dependence of the magnetic field produced by the gradient coils of an MRI scanner with sufficient accuracy to correct errors in quantitative diffusion MRI (DMRI) caused by gradient nonlinearity and gradient amplifier miscalibration. Theory and Methods: The field produced by the gradient coils is expanded in regular solid harmonics. The expansion coefficients are found by fitting a model to a minimum set of diffusion‐weighted images of an isotropic diffusion phantom. The accuracy of the resulting gradient coil field maps is evaluated by using them to compute corrected b‐matrices that are then used to process a multi‐shell diffusion tensor imaging (DTI) dataset with 32 diffusion directions per shell. Results: The method substantially reduces both the spatial inhomogeneity of the computed mean diffusivities (MD) and the computed values of the fractional anisotropy (FA), as well as virtually eliminating any artifactual directional bias in the tensor field secondary to gradient nonlinearity. When a small scaling miscalibration was purposely introduced in the x, y, and z, the method accurately detected the amount of miscalibration on each gradient axis. Conclusion: The method presented detects and corrects the effects of gradient nonlinearity and gradient gain miscalibration using a simple isotropic diffusion phantom. The correction would improve the accuracy of DMRI measurements in the brain and other organs for both DTI and higher order diffusion analysis. In particular, it would allow calibration of MRI systems, improving data harmony in multicenter studies. [ABSTRACT FROM AUTHOR]

Ischemic events related to carotid disease are far more strongly associated with plaque instability than stenosis. 3D high‐resolution diffusion‐weighted (DW) imaging can provide quantitative diffusion measurements on carotid atherosclerosis and may improve detection of vulnerable intraplaque hemorrhage (IPH). The 3D DW‐stack of stars (SOS) sequence was implemented with 3D SOS acquisition combined with DW preparation. After simulation of signals created from 3D DW‐SOS, phantom studies were performed. Three healthy subjects and 20 patients with carotid disease were recruited. Apparent diffusion coefficient (ADC) values were statistically analyzed on three subgroups by using a two‐group comparison Wilcoxon–Mann–Whitney U test with p values less than 0.05: symptomatic versus asymptomatic; IPH‐positive versus IPH‐negative; and IPH‐positive symptomatic versus asymptomatic plaques to determine the relationship with plaque vulnerability. ADC values calculated by 3D DW‐SOS provided values similar to those calculated from other techniques. Mean ADC of symptomatic plaque was significantly lower than asymptomatic plaque (0.68 ± 0.18 vs. 0.98 ± 0.16 x 10−3 mm2/s, p < 0.001). ADC was also significantly lower in IPH‐positive versus IPH‐negative plaque (0.68 ± 0.13 vs. 1.04 ± 0.11 x 10−3 mm2/s, p < 0.001). Additionally, ADC was significantly lower in symptomatic versus asymptomatic IPH‐positive plaque (0.57 ± 0.09 vs. 0.75 ± 0.11 x 10−3 mm2/s, p < 0.001). Our results provide strong evidence that ADC measurements from 3D DW‐SOS correlate with the symptomatic status of extracranial internal carotid artery plaque. Further, ADC improved discrimination of symptomatic plaque in IPH. These data suggest that diffusion characteristics may improve detection of destabilized plaque leading to elevated stroke risk. [ABSTRACT FROM AUTHOR]

Purpose: To develop a time‐division multiplexing echo‐planar imaging (TDM‐EPI) sequence for approximately two‐ to threefold acceleration when acquiring joint relaxation‐diffusion MRI data with multiple TEs. Methods: The proposed TDM‐EPI sequence interleaves excitation and data collection for up to 3 separate slices at different TEs and uses echo‐shifting gradients to disentangle the overlapping echo signals during the readout period. By properly arranging the sequence event blocks for each slice and adjusting the echo‐shifting gradients, diffusion‐weighted images from separate slices can be acquired. Therefore, we present 2 variants of the sequence. A single‐TE TDM‐EPI is presented to demonstrate the concept. Next, a multi‐TE TDM‐EPI is presented to highlight the advantages of the TDM approach for relaxation‐diffusion imaging. These sequences were evaluated on a 3 Tesla scanner with a water phantom and in vivo human brain data. Results: The single‐TE TDM‐EPI sequence can simultaneously acquire 2 slices with a maximum b value of 3000 s/mm2 and 2.5 mm isotropic resolution using interleaved readout windows with TE ≈ 78 ms. With the same b value and resolution, the multi‐TE TDM‐EPI sequence can simultaneously acquire 2 or 3 separate slices using interleaved readout sections with shortest TE ≈ 70 ms and ΔTE ≈ 30 ms. Phantom and in vivo experiments have shown that the proposed TDM‐EPI sequences can provide similar image quality and diffusion measures as conventional EPI readouts with multiple echoes but can reduce the overall relaxation‐diffusion protocol scan time by approximately two‐ to threefold. Conclusion: TDM‐EPI is a novel approach to acquire diffusion imaging data at multiple TEs. This enables a significant reduction in acquisition time for relaxation‐diffusion MRI experiments but without compromising image quality and diffusion measurements, thus removing a significant barrier to the adoption of relaxation‐diffusion MRI in clinical research studies of neurological and mental disorders. [ABSTRACT FROM AUTHOR]

Purpose: To investigate and remove Gibbs‐ringing artifacts caused by partial Fourier (PF) acquisition and zero filling interpolation in MRI data. Theory and Methods: Gibbs ringing of fully sampled data, leading to oscillations around tissue boundaries, is caused by the symmetric truncation of k‐space. Such ringing can be removed by conventional methods, with the local subvoxel shifts method being the state‐of‐the‐art. However, the asymmetric truncation of k‐space in routinely used PF acquisitions leads to additional ringings of wider intervals in the PF sampling dimension that cannot be corrected solely based on magnitude images reconstructed via zero filling. Here, we develop a pipeline for the Removal of PF‐induced Gibbs ringing (RPG) to remove ringing patterns of different periods by applying the conventional method twice. The proposed pipeline is validated on numerical phantoms, demonstrated on in vivo diffusion MRI measurements, and compared with the conventional method and neural network‐based approach. Results: For PF = 7/8 and 6/8, Gibbs‐ringings and subsequent bias in diffusion metrics induced by PF acquisition and zero filling are robustly removed by using the proposed RPG pipeline. For PF = 5/8, however, ringing removal via RPG leads to excessive image blurring due to the interplay of image phase and convolution kernel. Conclusions: RPG corrects Gibbs‐ringing artifacts in magnitude images of PF acquired data and reduces the bias in quantitative MR metrics. Considering the benefit of PF acquisition and the feasibility of ringing removal, we suggest applying PF = 6/8 when PF acquisition is necessary. [ABSTRACT FROM AUTHOR]

A new homogenization scheme is proposed to model the effects of surface phenomena occurring at the pore fluid/solid interface on the effective transport properties of multiscale and reactive geosystems, e.g., clayey geomaterials. This new scheme is a generalization of the so‐called differential scheme (DS) used to infer the effective properties of a composite made up of several materials. It is named the fractional differential scheme and is based on two key elements: (i) the concept of realizability of the DS itself and (ii) a fractional integral formulation of the DS for a porous medium considered as a two‐component composite. The formulation of the fractional DS introduces two parameters: a cementation exponent m and a fractional order α. The cementation exponent m is related to the microstructure of the material. The fractional order α accounts for the amplitude of the "surface" transport of ions resulting from the physico‐chemical interactions between hydrated cations and swelling clay minerals. Both parameters m and α are inverted from two sets of data: electrical conductivity measurements obtained on clayey rocks and effective diffusion coefficient measurements acquired from a Na‐montmorillonite geomaterial. The inversion results demonstrate that the fractional DS model is able to capture the dependence of the cation concentration on the effective transport properties of the clayey materials under study. Our results also show that the fractional order α can be considered an indirect indicator of the amplitude of physico‐chemical interactions between hydrated cations and swelling clay minerals occurring at the pore fluid/solid interface. [ABSTRACT FROM AUTHOR]

Purpose: Recent advances in diffusion‐weighted MRI provide "restricted diffusion signal fraction" and restricting pore size estimates. Materials based on co‐electrospun oriented hollow cylinders have been introduced to provide validation for such methods. This study extends this work, exploring accuracy and repeatability using an extended acquisition on a 300 mT/m gradient human MRI scanner, in substrates closely mimicking tissue, that is, non‐circular cross‐sections, intra‐voxel fiber crossing, intra‐voxel distributions of pore‐sizes, and smaller pore‐sizes overall. Methods: In a single‐blind experiment, diffusion‐weighted data were collected from a biomimetic phantom on a 3T Connectom system using multiple gradient directions/diffusion times. Repeated scans established short‐term and long‐term repeatability. The total scan time (54 min) matched similar protocols used in human studies. The number of distinct fiber populations was estimated using spherical deconvolution, and median pore size estimated through the combination of CHARMED and AxCaliber3D framework. Diffusion‐based estimates were compared with measurements derived from scanning electron microscopy. Results: The phantom contained substrates with different orientations, fiber configurations, and pore size distributions. Irrespective of one or two populations within the voxel, the pore‐size estimates (~5 μm) and orientation‐estimates showed excellent agreement with the median values of pore‐size derived from scanning electron microscope and phantom configuration. Measurement repeatability depended on substrate complexity, with lower values seen in samples containing crossing‐fibers. Sample‐level repeatability was found to be good. Conclusion: While no phantom mimics tissue completely, this study takes a step closer to validating diffusion microstructure measurements for use in vivo by demonstrating the ability to quantify microgeometry in relatively complex configurations. [ABSTRACT FROM AUTHOR]

Purpose: To introduce a joint reconstruction method for highly undersampled multi‐shot diffusion weighted (msDW) scans. Methods: Multi‐shot EPI methods enable higher spatial resolution for diffusion MRI, but at the expense of long scan‐time. Highly accelerated msDW scans are needed to enable their utilization in advanced microstructure studies, which require high q‐space coverage. Previously, joint k‐q undersampling methods coupled with compressed sensing were shown to enable very high acceleration factors. However, the reconstruction of this data using sparsity priors is challenging and is not suited for multi‐shell data. We propose a new reconstruction that recovers images from the combined k‐q data jointly. The proposed qModeL reconstruction brings together the advantages of model‐based iterative reconstruction and machine learning, extending the idea of plug‐and‐play algorithms. Specifically, qModeL works by prelearning the signal manifold corresponding to the diffusion measurement space using deep learning. The prelearned manifold prior is incorporated into a model‐based reconstruction to provide a voxel‐wise regularization along the q‐dimension during the joint recovery. Notably, the learning does not require in vivo training data and is derived exclusively from biophysical modeling. Additionally, a plug‐and‐play total variation denoising provides regularization along the spatial dimension. The proposed framework is tested on k‐q undersampled single‐shell and multi‐shell msDW acquisition at various acceleration factors. Results: The qModeL joint reconstruction is shown to recover DWIs from 8‐fold accelerated msDW acquisitions with error less than 5% for both single‐shell and multi‐shell data. Advanced microstructural analysis performed using the undersampled reconstruction also report reasonable accuracy. Conclusion: qModeL enables the joint recovery of highly accelerated multi‐shot dMRI utilizing learning‐based priors. The bio‐physically driven approach enables the use of accelerated multi‐shot imaging for multi‐shell sampling and advanced microstructure studies. [ABSTRACT FROM AUTHOR]

Pulsed‐field gradient (PFG) NMR is an important tool for characterization of biomolecules and supramolecular assemblies. However, for micrometer‐sized objects, such as amyloid fibrils, these experiments become difficult to interpret because in addition to translational diffusion they are also sensitive to rotational diffusion. We have constructed a mathematical theory describing the outcome of PFG NMR experiments on rod‐like fibrils. To test its validity, we have studied the fibrils formed by Sup35NM segment of the prion protein Sup35. The interpretation of the PFG NMR data in this system is fully consistent with the evidence from electron microscopy. Contrary to some previously expressed views, the signals originating from disordered regions in the fibrils can be readily differentiated from the similar signals representing small soluble species (e.g. proteolytic fragments). This paves the way for diffusion‐sorted NMR experiments on complex amyloidogenic samples. [ABSTRACT FROM AUTHOR]

We have developed a generalizable strategy to quantify the effect of surface barriers on zeolite catalysis. Isomerization of n‐pentane, catalyzed by Pt/Beta, is taken as a model reaction system. Firstly, the surface modification by chemical liquid deposition of SiO2 was carried out to control the surface barriers on zeolite Beta crystals. The deposition of SiO2 leads to a very slight change in the physical properties of Beta crystals, but an obvious reduction in Brønsted acid sites. Diffusion measurements by the zero‐length column (ZLC) method show that the apparent diffusivity of n‐pentane can be more than doubled after SiO2 deposition, indicating that the surface barriers have been weakened. Catalytic performance was tested in a fixed‐bed reactor, showing that the apparent catalytic activity improved by 51–131 % after SiO2 deposition. These results provide direct proof that reducing surface barriers can be an effective route to improve zeolite catalyst performance deteriorated by transport limitations. [ABSTRACT FROM AUTHOR]

Conventional analysis of fluorescence recovery after photobleaching (FRAP) data for diffusion coefficient estimation typically involves fitting an analytical or numerical FRAP model to the recovery curve data using non‐linear least squares. Depending on the model, this can be time consuming, especially for batch analysis of large numbers of data sets and if multiple initial guesses for the parameter vector are used to ensure convergence. In this work, we develop a completely new approach, DeepFRAP, utilizing machine learning for parameter estimation in FRAP. From a numerical FRAP model developed in previous work, we generate a very large set of simulated recovery curve data with realistic noise levels. The data are used for training different deep neural network regression models for prediction of several parameters, most importantly the diffusion coefficient. The neural networks are extremely fast and can estimate the parameters orders of magnitude faster than least squares. The performance of the neural network estimation framework is compared to conventional least squares estimation on simulated data, and found to be strikingly similar. Also, a simple experimental validation is performed, demonstrating excellent agreement between the two methods. We make the data and code used publicly available to facilitate further development of machine learning‐based estimation in FRAP. Lay description: Fluorescence recovery after photobleaching (FRAP) is one of the most frequently used methods for microscopy‐based diffusion measurements and broadly used in materials science, pharmaceutics, food science and cell biology. In a FRAP experiment, a laser is used to photobleach fluorescent particles in a region. By analysing the recovery of the fluorescence intensity due to the diffusion of still fluorescent particles, the diffusion coefficient and other parameters can be estimated. Typically, a confocal laser scanning microscope (CLSM) is used to image the time evolution of the recovery, and a model is fit using least squares to obtain parameter estimates. In this work, we introduce a new, fast and accurate method for analysis of data from FRAP. The new method is based on using artificial neural networks to predict parameter values, such as the diffusion coefficient, effectively circumventing classical least squares fitting. This leads to a dramatic speed‐up, especially noticeable when analysing large numbers of FRAP data sets, while still producing results in excellent agreement with least squares. Further, the neural network estimates can be used as very good initial guesses for least squares estimation in order to make the least squares optimization convergence much faster than it otherwise would. This provides for obtaining, for example, diffusion coefficients as soon as possible, spending minimal time on data analysis. In this fashion, the proposed method facilitates efficient use of the experimentalist's time which is the main motivation to our approach. The concept is demonstrated on pure diffusion. However, the concept can easily be extended to the diffusion and binding case. The concept is likely to be useful in all application areas of FRAP, including diffusion in cells, gels and solutions. [ABSTRACT FROM AUTHOR]

The discussion focuses on the application of a Payne cell to the measurement of diffusion and solubility coefficients in polymer/solvent systems. Payne cells have, thus far, been used exclusively to measure steady‐state permeation rates of solvents. An analytical model has been developed to describe transient gravimetric sorption and desorption measurements performed with a Payne cell. The model has been validated by a complementary numerical simulation and has been applied to evaluate diffusion and solubility coefficients in two different toluene‐silicone rubber systems. The data measured using the Payne cell are found to compare very well with diffusion coefficient and isotherm data measured by traditional gravimetric sorption experiments. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the neuroanatomical underpinning of healthy macaque brain cortical microstructure measured by diffusion kurtosis imaging (DKI), which characterizes non‐Gaussian water diffusion. Methods: High‐resolution DKI was acquired from 6 postmortem macaque brains. Neurofilament density (ND) was quantified based on structure tensor from neurofilament histological images of a different macaque brain sample. After alignment of DKI‐derived mean kurtosis (MK) maps to the histological images, MK and histology‐based ND were measured at corresponding regions of interests characterized by distinguished cortical MK values in the prefrontal/precentral‐postcentral and temporal cortices. Pearson correlation was performed to test significant correlation between these cortical MK and ND measurements. Results: Heterogeneity of cortical MK across different cortical regions was revealed, with significantly and consistently higher MK measurements in the prefrontal/precentral‐postcentral cortex compared to those in the temporal cortex across all six scanned macaque brains. Corresponding higher ND measurements in the prefrontal/precentral‐postcentral cortex than in the temporal cortex were also found. The heterogeneity of cortical MK is associated with heterogeneity of histology‐based ND measurements, with significant correlation between cortical MK and corresponding ND measurements (P <.005). Conclusion: These findings suggested that DKI‐derived MK can potentially be an effective noninvasive biomarker quantifying underlying neuroanatomical complexity inside the cerebral cortical mantle for clinical and neuroscientific research. [ABSTRACT FROM AUTHOR]

Purpose: Proton therapy could benefit from noninvasively gaining tumor microstructure information, at both planning and monitoring stages. The anatomical location of brain tumors, such as meningiomas, often hinders the recovery of such information from histopathology, and conventional noninvasive imaging biomarkers, like the apparent diffusion coefficient (ADC) from diffusion‐weighted MRI (DW‐MRI), are nonspecific. The aim of this study was to retrieve discriminative microstructural markers from conventional ADC for meningiomas treated with proton therapy. These markers were employed for tumor grading and tumor response assessment. Methods: DW‐MRIs from patients affected by meningioma and enrolled in proton therapy were collected before (n = 35) and 3 months after (n = 25) treatment. For the latter group, the risk of an adverse outcome was inferred by their clinical history. Using Monte Carlo methods, DW‐MRI signals were simulated from packings of synthetic cells built with well‐defined geometrical and diffusion properties. Patients' ADC was modeled as a weighted sum of selected simulated signals. The weights that best described a patient's ADC were determined through an optimization procedure and used to estimate a set of markers of tumor microstructure: diffusion coefficient (D), volume fraction (vf), and radius (R). Apparent cellularity (ρapp) was estimated from vf and R for an easier clinical interpretability. Differences between meningothelial and atypical subtypes, and low‐ and high‐grade meningiomas were assessed with nonparametric statistical tests, whereas sensitivity and specificity with ROC analyses. Similar analyses were performed for patients showing low or high risk of an adverse outcome to preliminary evaluate response to treatment. Results: Significant (P < 0.05) differences in median ADC, D, vf, R, and ρapp values were found when comparing meningiomas' subtypes and grades. ROC analyses showed that estimated microstructural parameters reached higher specificity than ADC for subtyping (0.93 for D and vf vs 0.80 for ADC) and grading (0.75 for R vs 0.67 for ADC). High‐ and low‐risk patients showed significant differences in ADC and microstructural parameters. The skewness of ρapp was the parameter with highest AUC (0.90) and sensitivity (0.75). Conclusions: Matching measured with simulated ADC yielded a set of potential imaging markers for meningiomas grading and response monitoring in proton therapy, showing higher specificity than conventional ADC. These markers can provide discriminative information about spatial patterns of tumor microstructure implying important advantages for patient‐specific proton therapy workflows. [ABSTRACT FROM AUTHOR]

Apoptosis maintains an equilibrium between cell proliferation and cell death. Many diseases, including cancer, develop because of defects in apoptosis. A known metabolic marker of apoptosis is a notable increase in 1H NMR‐observable resonances associated with lipids stored in lipid droplets. However, standard one‐dimensional NMR experiments allow the quantification of lipid concentration only, without providing information about physical characteristics such as the size of lipid droplets, viscosity of the cytosol, or cytoskeletal rigidity. This additional information can improve monitoring of apoptosis‐based cancer treatments in intact cells and provide us with mechanistic insight into why these changes occur. In this paper, we use high‐resolution magic angle spinning (HRMAS) 1H NMR spectroscopy to monitor lipid concentrations and apparent diffusion coefficients of mobile lipid in intact cells treated with the apoptotic agents cisplatin or etoposide. We also use solution‐state NMR spectroscopy to study changes in lipid profiles of organic solvent cell extracts. Both NMR techniques show an increase in the concentration of lipids but the relative changes are 10 times larger by HRMAS 1H NMR spectroscopy. Moreover, the apparent diffusion rates of lipids in apoptotic cells measured by HRMAS 1H NMR spectroscopy decrease significantly as compared with control cells. Slower diffusion rates of mobile lipids in apoptotic cells correlate well with the formation of larger lipid droplets as observed by microscopy. We also compared the mean lipid droplet displacement values calculated from the two methods. Both methods showed shorter displacements of lipid droplets in apoptotic cells. Our results demonstrate that the NMR‐based diffusion experiments on intact cells discriminate between control and apoptotic cells. Apparent diffusion measurements in conjunction with 1H NMR spectroscopy‐derived lipid signals provide a novel means of following apoptosis in intact cells. This method could have potential application in enhancing drug discovery by monitoring drug treatments in vitro, particularly for agents that cause portioning of lipids such as apoptosis. [ABSTRACT FROM AUTHOR]

Purpose: To investigate intravoxel incoherent motion (IVIM) tensor imaging of the in vivo human heart and elucidate whether the estimation of IVIM tensors is affected by the complexity of pseudo‐diffusion components in myocardium. Methods: The cardiac IVIM data of 10 healthy subjects were acquired using a diffusion weighted spin‐echo echo‐planar imaging sequence along 6 gradient directions with 10 b values (0~400 s/mm2). The IVIM data of left ventricle myocardium were fitted to the IVIM tensor model. The complexity of myocardial pseudo‐diffusion components was reduced through exclusion of low b values (0 and 5 s/mm2) from the IVIM curve‐fitting analysis. The fractional anisotropy, mean fraction/mean diffusivity, and Westin measurements of pseudo‐diffusion tensors (fp and D*) and self‐diffusion tensor (D), as well as the angle between the main eigenvector of fp (or D*) and that of D, were computed and compared before and after excluding low b values. Results: The fractional anisotropy values of fp and D* without low b value participation were significantly higher (P <.001) than those with low b value participation, but an opposite trend was found for the mean fraction/diffusivity values. Besides, after removing low b values, the angle between the main eigenvector of fp (or D*) and that of D became small, and both fp and D* tensors presented significant decrease of spherical components and significant increase of linear components. Conclusion: The presence of multiple pseudo‐diffusion components in myocardium indeed influences the estimation of IVIM tensors. The IVIM tensor model needs to be further improved to account for the complexity of myocardial microcirculatory network and blood flow. [ABSTRACT FROM AUTHOR]