NMR diffusion and relaxation correlation methods: New insights in heterogeneous materials

Preferential ordering of incommensurate-length guest particles in a smectic host

Using density functional theory, we study the preferential ordering of rod-like guest particles immersed in a smectic host fluid. Within a model of perfectly aligned rods and assuming that the guest particles do not perturb the smectic host fluid, simple excluded-volume arguments explain that guest particles that are comparable in length to the host particles order in phase with the smectic host density layering, whereas guest particles that are considerably shorter or longer order in antiphase. The corresponding free-energy minima are separated by energetic barriers on the order of the thermal energy kBT, suggesting that guest particles undergo hopping-type diffusion between adjacent smectic layers. Upon introducing a slight orientational mismatch between the guest particles and the perfectly aligned smectic host, an additional, smaller free-energy barrier emerges for a range of intermediate guest-to-host length ratios, which splits the free-energy minimum into two. Guest particles in this range occupy positions intermediate between in-phase ordering and in-antiphase ordering. Finally, we use Kramers' theory to identify slow, fast, and intermediate diffusive regimes for the guest particles as a function of their length. Our model is in qualitative agreement with experiment and simulation and provides an alternative, complementary explanation in terms of a free-energy landscape for the intermediate diffusive regime, which was previously hypothesized to result from temporary caging effects [M. Chiappini, E. Grelet, and M. Dijkstra, Phys. Rev. Lett. 124, 087801 (2020)]. We argue that our simple model of aligned rods captures the salient features of incommensurate-length guest particles in a smectic host if a slight orientational mismatch is introduced.

GRIN-toolbox: A versatile and light toolbox for NMR inversion

NMR technique serves as a powerful analytical tool with diverse applications in fields such as chemistry, biology, and material science. However, the effectiveness of NMR heavily relies on data post-processing which is often modeled as regularized inverse problem. Recently, we proposed the Generally Regularized INversion (GRIN) algorithm and demonstrated its effectiveness in NMR data processing. GRIN has been integrated as a friendly graphic user interface-based toolbox which was not detailed in the original paper. In this paper, to make GRIN more practically accessible to NMR practitioners, we focus on introducing the usage of GRIN-Toolbox with processing examples and the corresponding processing graphic interfaces, and the user manual is attached as Supplementary Material. GRIN-Toolbox is versatile and lightweight, where various kinds of data processing tasks can be completed with one click, including but not limited to diffusion-ordered spectroscopy processing, magnetic resonance imaging under-sampling reconstruction, Laplace (diffusion or relaxation) NMR inversion, spectrum denoising, etc. In addition, GRIN-Toolbox could be extended to more applications with user-designed inversion models and freely available at https://github.com/EricLin1993/GRIN.

What does FEXI measure?

Filter-exchange imaging (FEXI) has already been utilized in several biomedical studies for evaluating the permeability of cell membranes. The method relies on suppressing the extracellular signal using strong diffusion weighting (the mobility filter causing a reduction in the overall diffusivity) and monitoring the subsequent diffusivity recovery. Using Monte Carlo simulations, we demonstrate that FEXI is sensitive not uniquely to the transcytolemmal exchange but also to the geometry of involved compartments: complex geometry offers locations where spins remain unaffected by the mobility filter; moving to other locations afterwards, such spins contribute to the diffusivity recovery without actually permeating any membrane. This exchange mechanism is a warning for those who aim to use FEXI in complex media such as brain gray matter and opens wide scope for investigation towards crystallizing the genuine membrane permeation and characterizing the compartment geometry.

MR Fingerprinting with b-Tensor Encoding for Simultaneous Quantification of Relaxation and Diffusion in a Single Scan

PURPOSE

Although both relaxation and diffusion imaging are sensitive to tissue microstructure, studies have reported limited sensitivity and robustness of using relaxation or conventional diffusion alone to characterize tissue microstructure. Recently, it has been shown that tensor-valued diffusion encoding and joint relaxation-diffusion quantification enable more reliable quantification of compartment-specific microstructural properties. However, scan times to acquire such data can be prohibitive. Here, we aim to simultaneously quantify relaxation and diffusion using MR fingerprinting (MRF) and b-tensor encoding in a clinically feasible time.

METHODS

We developed multidimensional MRF scans (mdMRF) with linear and spherical b-tensor encoding (LTE and STE) to simultaneously quantify T1, T2, and ADC maps from a single scan. The image quality, accuracy, and scan efficiency were compared between the mdMRF using LTE and STE. Moreover, we investigated the robustness of different sequence designs to signal errors and their impact on the maps.

RESULTS

T1 and T2 maps derived from the mdMRF scans have consistently high image quality, while ADC maps are sensitive to different sequence designs. Notably, the fast imaging steady state precession (FISP)-based mdMRF scan with peripheral pulse gating provides the best ADC maps that are free of image distortion and shading artifacts.

CONCLUSION

We demonstrated the feasibility of quantifying T1, T2, and ADC maps simultaneously from a single mdMRF scan in around 24 s/slice. The map quality and quantitative values are consistent with the reference scans.

The use of time domain 1 H NMR to study proton dynamics in starch-rich foods: A review

Starch is a major contributor to the carbohydrate portion of our diet. When it is present with water, it undergoes several transformations during heating and/or cooling making it an essential structure-forming component in starch-rich food systems (e.g., bread and cake). Time domain proton nuclear magnetic resonance (TD ¹ H NMR) is a useful technique to study starch-water interactions by evaluation of molecular mobility and water distribution. The data obtained correspond to changes in starch structure and the state of water during or resulting from processing. When this technique was first applied to starch(-rich) foods, significant challenges were encountered during data interpretation of complex food systems (e.g., cake or biscuit) due to the presence of multiple constituents (proteins, carbohydrates, lipids, etc.). This article discusses the principles of TD ¹ H NMR and the tools applied that improved characterization and interpretation of TD NMR data. More in particular, the major differences in proton distribution of various dough and cooked/baked food systems are examined. The application of variable-temperature TD ¹ H NMR is also discussed as it demonstrates exceptional ability to elucidate the molecular dynamics of starch transitions (e.g., gelatinization, gelation) in dough/batter systems during heating/cooling. In conclusion, TD NMR is considered a valuable tool to understand the behavior of starch and water that relate to the characteristics and/or quality of starchy food products. Such insights are crucial for food product optimization and development in response to the needs of the food industry.

Nonparametric 5D D-R2 distribution imaging with single-shot EPI at 21.1 T: Initial results for in vivo rat brain

In vivo human diffusion MRI is by default performed using single-shot EPI with greater than 50-ms echo times and associated signal loss from transverse relaxation. The individual benefits of the current trends of increasing B₀ to boost SNR and employing more advanced signal preparation schemes to improve the specificity for selected microstructural properties eventually may be cancelled by increased relaxation rates at high B₀ and echo times with advanced encoding. Here, initial attempts to translate state-of-the-art diffusion-relaxation correlation methods from 3 T to 21.1 T are made to identify hurdles that need to be overcome to fulfill the promises of both high SNR and readily interpretable microstructural information.

Disentangling the effects of restriction and exchange with diffusion exchange spectroscopy

Diffusion exchange spectroscopy (DEXSY) is a multidimensional NMR technique that can reveal how water molecules exchange between compartments within heterogeneous media, such as biological tissue. Data from DEXSY experiments is typically processed using numerical inverse Laplace transforms (ILTs) to produce a diffusion-diffusion spectrum. A tacit assumption of this ILT approach is that the signal behavior is Gaussian - i.e., the spin echo intensity decays exponentially with the degree of diffusion weighting. The assumptions that underlie Gaussian signal behavior may be violated, however, depending on the gradient strength applied and the sample under study. We argue that non-Gaussian signal behavior due to restrictions is to be expected in the study of biological tissue using diffusion NMR. Further, we argue that this signal behavior can produce confounding features in the diffusion-diffusion spectra obtained from numerical ILTs of DEXSY data - entangling the effects of restriction and exchange. Specifically, restricted signal behavior can result in broadening of peaks and in the appearance of illusory exchanging compartments with distributed diffusivities, which pearl into multiple peaks if not highly regularized. We demonstrate these effects on simulated data. That said, we suggest the use of features in the signal acquisition domain that can be used to rapidly probe exchange without employing an ILT. We also propose a means to characterize the non-Gaussian signal behavior due to restrictions within a sample using DEXSY measurements with a near zero mixing time or storage interval. We propose a combined acquisition scheme to independently characterize restriction and exchange with various DEXSY measurements, which we term Restriction and Exchange from Equally-weighted Double and Single Diffusion Encodings (REEDS-DE). We test this method on ex vivo neonatal mouse spinal cord - a sample consisting primarily of gray matter - using a low-field, static gradient NMR system. In sum, we highlight critical shortcomings of prevailing DEXSY analysis methods that conflate the effects of restriction and exchange, and suggest a viable experimental approach to disentangle them.

Nonparametric D-R1-R2 distribution MRI of the living human brain

Diffusion-relaxation correlation NMR can simultaneously characterize both the microstructure and the local chemical composition of complex samples that contain multiple populations of water. Recent developments on tensor-valued diffusion encoding and Monte Carlo inversion algorithms have made it possible to transfer diffusion-relaxation correlation NMR from small-bore scanners to clinical MRI systems. Initial studies on clinical MRI systems employed 5D D-R₁ and D-R₂ correlation to characterize healthy brain in vivo. However, these methods are subject to an inherent bias that originates from not including R₂ or R₁ in the analysis, respectively. This drawback can be remedied by extending the concept to 6D D-R₁-R₂ correlation. In this work, we present a sparse acquisition protocol that records all data necessary for in vivo 6D D-R₁-R₂ correlation MRI across 633 individual measurements within 25 min-a time frame comparable to previous lower-dimensional acquisition protocols. The data were processed with a Monte Carlo inversion algorithm to obtain nonparametric 6D D-R₁-R₂ distributions. We validated the reproducibility of the method in repeated measurements of healthy volunteers. For a post-therapy glioblastoma case featuring cysts, edema, and partially necrotic remains of tumor, we present representative single-voxel 6D distributions, parameter maps, and artificial contrasts over a wide range of diffusion-, R₁-, and R₂-weightings based on the rich information contained in the D-R₁-R₂ distributions.

Spatiotemporal characterisation of ischaemic lesions in transient stroke animal models using diffusion free water elimination and mapping MRI with echo time dependence

BACKGROUND AND PURPOSE

The excess fluid as a result of vasogenic oedema and the subsequent tissue cavitation obscure the microstructural characterisation of ischaemic tissue by conventional diffusion and relaxometry MRI. They lead to a pseudo-normalisation of the water diffusivity and transverse relaxation time maps in the subacute and chronic phases of stroke. Within the context of diffusion MRI, the free water elimination and mapping method (FWE) with echo time dependence has been proposed as a promising approach to measure the amount of free fluid in brain tissue robustly and to eliminate its biasing effect on other biomarkers. In this longitudinal study of transient middle cerebral artery occlusion (MCAo) in the rat brain, we investigated the use of FWE MRI with echo time dependence for the characterisation of the tissue microstructure and explored the potential of the free water fraction as a novel biomarker of ischaemic tissue condition.

METHODS

Adult rats received a transient MCAo. Diffusion- and transverse relaxation-weighted MRI experiments were performed longitudinally, pre-occlusion and on days 1, 3, 4, 5, 6, 7 and 10 after MCAo on four rats. Histology was performed for non-stroke and 1, 3 and 10 days after MCAo on three different rats at each time point.

RESULTS

The free water fraction was homogeneously increased in the ischaemic cortex one day after stroke. Between three and ten days after stroke, the core of the ischaemic tissue showed a progressive normalisation in the amount of free water, whereas the inner and outer border zones of the ischaemic cortex depicted a large, monotonous increase with time. The specific lesions in brain sections were verified by H&E and immunostaining. The tissue-specific diffusion and relaxometry MRI metrics in the ischaemic cortex were significantly different compared to their conventional counterpart.

CONCLUSIONS

Our results demonstrate that the free water fraction in FWE MRI with echo time dependence is a valuable biomarker, sensitive to the progressive degeneration in ischaemic tissue. We showed that part of the heterogeneity previously observed in conventional parameter maps can be accounted for by a heterogeneous distribution of free water in the tissue. Our results suggest that the temporal evolution of the free fluid fraction map at the core and inner border zone can be associated with the pathological changes linked to the evolution of vasogenic oedema. Namely, the homogeneous increase in free water one day after stroke and its tendency to normalise in the core of the ischaemic cortex starting three days after stroke, followed by a progressive increase in free water at the inner border zone from three to ten days after stroke. Finally, the monotonous increase in free fluid in the outer border zone of the cortex reflects the formation of fluid-filled cysts.

Low-Field Functional Group Resolved Nuclear Spin Relaxation in Mesoporous Silica

Solid-fluid interactions underpin the efficacy of functional porous materials across a diverse array of chemical reaction and separation processes. However, detailed characterization of interfacial phenomena within such systems is hampered by their optically opaque nature. Motivated by the need to bridge this capability gap, we report low-magnetic-field two-dimensional (2D) ¹H nuclear spin relaxation measurements as a noninvasive probe of adsorbate identity and interfacial dynamics, exploring the relaxation characteristics exhibited by liquid hydrocarbon adsorbates confined to a model mesoporous silica. For the first time, we demonstrate the capacity of this approach in distinguishing functional group-specific relaxation phenomena across a diverse range of alcohols and carboxylic acids employed as solvents, reagents, and liquid hydrogen carriers, with distinct relaxation responses assigned to the alkyl and hydroxyl moieties of each confined liquid. Uniquely, this relaxation behavior is shown to correlate with adsorbate acidity, with the observed relationship rationalized on the basis of surface-adsorbate proton-exchange dynamics. Our results demonstrate that nuclear spin relaxation provides a molecular-level perspective on sorbent/sorbate interactions, motivating the exploration of such measurements as a unique probe of adsorbate identity within optically opaque porous media.

Ultrafast methods for relaxation and diffusion

Relaxation and diffusion NMR measurements offer an approach to studying rotational and translational motion of molecules non-invasively, and they also provide chemical resolution complementary to NMR spectra. Multidimensional experiments enable the correlation of relaxation and diffusion parameters as well as the observation of molecular exchange phenomena through relaxation or diffusion contrast. This review describes how to accelerate multidimensional relaxation and diffusion measurements significantly through spatial encoding. This so-called ultrafast Laplace NMR approach shortens the experiment time to a fraction and makes even single-scan experiments possible. Single-scan experiments, in turn, significantly facilitate the use of nuclear spin hyperpolarization methods to boost sensitivity. The ultrafast Laplace NMR method is also applicable with low-field, mobile NMR instruments, and it can be exploited in many disciplines. For example, it has been used in studies of the dynamics of fluids in porous materials, identification of intra- and extracellular metabolites in cancer cells, and elucidation of aggregation phenomena in atmospheric surfactant solutions.

Characterization of Water in Wood by Time-Domain Nuclear Magnetic Resonance Spectroscopy (TD-NMR): A Review

This review summarizes the development of the experimental technique and analytical method for using TD-NMR to study wood-water interactions in recent years. We briefly introduce the general concept of TD-NMR and magnetic resonance imaging (MRI), and demonstrate their applications for characterizing the following aspects of wood-water interactions: water state, fiber saturation state, water distribution at the cellular scale, and water migration in wood. The aim of this review is to provide an overview of the utilizations and future research opportunities of TD-NMR in wood-water relations. It should be noted that this review does not cover the NMR methods that provide chemical resolution of wood macromolecules, such as solid-state NMR.

Toward nonparametric diffusion- T1 characterization of crossing fibers in the human brain

PURPOSE

To estimate T 1 for each distinct fiber population within voxels containing multiple brain tissue types.

METHODS

A diffusion- T 1 correlation experiment was carried out in an in vivo human brain using tensor-valued diffusion encoding and multiple repetition times. The acquired data were inverted using a Monte Carlo algorithm that retrieves nonparametric distributions P ( D , R 1 ) of diffusion tensors and longitudinal relaxation rates R 1 = 1 / T 1 . Orientation distribution functions (ODFs) of the highly anisotropic components of P ( D , R 1 ) were defined to visualize orientation-specific diffusion-relaxation properties. Finally, Monte Carlo density-peak clustering (MC-DPC) was performed to quantify fiber-specific features and investigate microstructural differences between white matter fiber bundles.

RESULTS

Parameter maps corresponding to P ( D , R 1 ) 's statistical descriptors were obtained, exhibiting the expected R 1 contrast between brain tissue types. Our ODFs recovered local orientations consistent with the known anatomy and indicated differences in R 1 between major crossing fiber bundles. These differences, confirmed by MC-DPC, were in qualitative agreement with previous model-based works but seem biased by the limitations of our current experimental setup.

CONCLUSIONS

Our Monte Carlo framework enables the nonparametric estimation of fiber-specific diffusion- T 1 features, thereby showing potential for characterizing developmental or pathological changes in T 1 within a given fiber bundle, and for investigating interbundle T 1 differences.

Physico-Chemical Challenges in 3D Printing of Polymeric Nanocomposites and Hydrogels for Biomedical Applications

Additive manufacturing techniques (i.e., 3D printing) are rapidly becoming one of the most popular methods for the preparation of materials to be employed in many different fields, including biomedical applications. The main reason is the unique flexibility resulting from both the method itself and the variety of starting materials, requiring the combination of multidisciplinary competencies for the optimization of the process. In particular, this is the case of additive manufacturing processes based on the extrusion or jetting of nanocomposite materials, where the unique properties of nanomaterials are combined with those of a flowing matrix. This contribution focuses on the physico-chemical challenges typically faced in the 3D printing of polymeric nanocomposites and polymeric hydrogels intended for biomedical applications. The strategies to overcome those challenges are outlined, together with the characterization approaches that could help the advance of the field.

Identification of extracellular nanoparticle subsets by nuclear magnetic resonance

Exosomes are a subset of secreted lipid envelope-encapsulated extracellular vesicles (EVs) of 50-150 nm diameter that can transfer cargo from donor to acceptor cells. In the current purification protocols of exosomes, many smaller and larger nanoparticles such as lipoproteins, exomers and microvesicles are typically co-isolated as well. Particle size distribution is one important characteristics of EV samples, as it reflects the cellular origin of EVs and the purity of the isolation. However, most of the physicochemical analytical methods today cannot illustrate the smallest exosomes and other small particles like the exomers. Here, we demonstrate that diffusion ordered spectroscopy (DOSY) nuclear magnetic resonance (NMR) method enables the determination of a very broad distribution of extracellular nanoparticles, ranging from 1 to 500 nm. The range covers sizes of all particles included in EV samples after isolation. The method is non-invasive, as it does not require any labelling or other chemical modification. We investigated EVs secreted from milk as well as embryonic kidney and renal carcinoma cells. Western blot analysis and immuno-electron microscopy confirmed expression of exosomal markers such as ALIX, TSG101, CD81, CD9, and CD63 in the EV samples. In addition to the larger particles observed by nanoparticle tracking analysis (NTA) in the range of 70-500 nm, the DOSY distributions include a significant number of smaller particles in the range of 10-70 nm, which are visible also in transmission electron microscopy images but invisible in NTA. Furthermore, we demonstrate that hyperpolarized chemical exchange saturation transfer (Hyper-CEST) with 129Xe NMR indicates also the existence of smaller and larger nanoparticles in the EV samples, providing also additional support for DOSY results. The method implies also that the Xe exchange is significantly faster in the EV pool than in the lipoprotein/exomer pool.

Multidimensional correlation spectroscopic imaging of exponential decays: From theoretical principles to in vivo human applications

Multiexponential modeling of relaxation or diffusion MR signal decays is a popular approach for estimating and spatially mapping different microstructural tissue compartments. While this approach can be quite powerful, it is also limited by the fact that one-dimensional multiexponential modeling is an ill-posed inverse problem with substantial ambiguities. In this article, we present an overview of a recent multidimensional correlation spectroscopic imaging approach to this problem. This approach helps to alleviate ill-posedness by making advantageous use of multidimensional contrast encoding (e.g., 2D diffusion-relaxation encoding or 2D relaxation-relaxation encoding) combined with a regularized spatial-spectral estimation procedure. Theoretical calculations, simulations, and experimental results are used to illustrate the benefits of this approach relative to classical methods. In addition, we demonstrate an initial proof-of-principle application of this kind of approach to in vivo human MRI experiments.

Low-field NMR relaxation-exchange measurements for the study of gas admission in microporous solids

Understanding the uptake and storage of gases by microporous materials is important for our future energy security. As such, we demonstrate here the application of two-dimensional NMR relaxation experiments for probing the admission and corresponding exchange dynamics of methane within microporous zeolites. Specifically, we report low-field (12.7 MHz) 1H NMR relaxation-exchange correlation measurements of methane within commercial LTA zeolites (3A and 4A) at 25 and 35 bar and ambient temperature. Our results demonstrate the clear identification of bulk-pore and pore-pore exchange processes within zeolite 4A, facilitating the calculation and comparison of effective exchange rate dynamics across varying diffusion length scales and gas pressures. Additional data acquired for zeolite 3A reveals the sensitivity of NMR relaxation phenomena to size-exclusive gas admission phenomena, illustrating the potential of benchtop NMR protocols for material screening applications.

Strong diffusion gradients allow the separation of intra- and extra-axonal gradient-echo signals in the human brain

The quantification of brain white matter properties is a key area of application of Magnetic Resonance Imaging (MRI), with much effort focused on using MR techniques to quantify tissue microstructure. While diffusion MRI probes white matter (WM) microstructure by characterising the sensitivity of Brownian motion of water molecules to anisotropic structures, susceptibility-based techniques probe the tissue microstructure by observing the effect of interaction between the tissue and the magnetic field. Here, we unify these two complementary approaches by combining ultra-strong (300 mT/m) gradients with a novel Diffusion-Filtered Asymmetric Spin Echo (D-FASE) technique. Using D-FASE we can separately assess the evolution of the intra- and extra-axonal signals under the action of susceptibility effects, revealing differences in the behaviour in different fibre tracts. We observed that the effective relaxation rate of the ASE signal in the corpus callosum decreases with increasing b-value in all subjects (from 17.1 ± 0.7 s⁻¹ at b = 0 s/mm² to 14.6 ± 0.7 s⁻¹ at b = 4800 s/mm²), while this dependence on b in the corticospinal tract is less pronounced (from 12.0± 1.1 s⁻¹ at b = 0s/mm² to 10.7 ± 0.5 s⁻¹ at b = 4800 s/mm²). Voxelwise analysis of the signal evolution with respect to b-factor and acquisition delay using a microscopic model demonstrated differences in gradient echo signal evolution between the intra- and extra-axonal pools.

Dedicated diffusion phantoms for the investigation of free water elimination and mapping: insights into the influence of T2 relaxation properties

Conventional diffusion-weighted (DW) MRI suffers from free water contamination due to the finite voxel size. The most common case of free water contamination occurs with cerebrospinal fluid (CSF) in voxels located at the CSF-tissue interface, such as at the ventricles in the human brain. Another case refers to intra-tissue free water as in vasogenic oedema. In order to avoid the bias in diffusion metrics, several multi-compartment methods have been introduced, which explicitly model the presence of a free water compartment. However, fitting multi-compartment models in DW MRI represents a well known ill conditioned problem. Although during the last decade great effort has been devoted to mitigating this estimation problem, the research field remains active. The aim of this work is to introduce the design, characterise the NMR properties and demonstrate the use of two dedicated anisotropic diffusion fibre phantoms, useful for the study of free water elimination (FWE) and mapping models. In particular, we investigate the recently proposed FWE diffusion tensor imaging approach, which takes explicit account of differences in the transverse relaxation times between the free water and tissue compartments.

Magnetic resonance measurements of cellular and sub-cellular membrane structures in live and fixed neural tissue

We develop magnetic resonance (MR) methods for real-time measurement of tissue microstructure and membrane permeability of live and fixed excised neonatal mouse spinal cords. Diffusion and exchange MR measurements are performed using the strong static gradient produced by a single-sided permanent magnet. Using tissue delipidation methods, we show that water diffusion is restricted solely by lipid membranes. Most of the diffusion signal can be assigned to water in tissue which is far from membranes. The remaining 25% can be assigned to water restricted on length scales of roughly a micron or less, near or within membrane structures at the cellular, organelle, and vesicle levels. Diffusion exchange spectroscopy measures water exchanging between membrane structures and free environments at 100 s^-1.

Nonlinear sampling in ultrafast Laplace NMR

Ultrafast Laplace NMR (UF-LNMR) reduces the experiment time of multidimensional relaxation and diffusion measurements to a fraction. Here, we demonstrate a method for nonlinear (in this case logarithmic) sampling of the indirect dimension in UF-LNMR measurements. The method is based on the use of frequency-swept pulses with the frequency nonlinearly increasing with time. This leads to an optimized detection of exponential experimental data and significantly improved resolution of LNMR parameters.

Investigating pore geometry in heterogeneous porous samples using spatially resolved G0-Δχapp and G0-Δν correlations

This study presents a two-dimensional NMR pulse sequence for obtaining spatially resolved correlations between magnetic susceptibility induced internal gradients (G₀), and both the apparent difference in magnetic susceptibility (Δχ_app) and spectral frequency (Δν). G₀-Δχ_app correlations were utilized to generate spatially resolved pore size distributions, while the G₀-Δν correlations were used to additionally evaluate sample heterogeneity. The spatially resolved measurements were performed on a water saturated heterogeneous porous sample which contains one layer of 5-50 μm glass spheres (top layer) and one layer of 140-165 μm glass spheres (bottom layer). The slice selection was validated by applying the pulse sequence on a liquid mineral oil and water sample as well as on the porous sample. The resulting spatially resolved pore size distributions show very good agreement with results from our previously published non slice selective pulse sequence in the 140-165 μm glass spheres and good agreement in the 5-50 μm glass spheres. The G₀-Δν correlations correctly indicate a slightly higher degree of heterogeneity in the 5-50 μm glass spheres compared to the 140-165 μm glass spheres.

Combined diffusion-relaxometry MRI to identify dysfunction in the human placenta

Purpose

A combined diffusion‐relaxometry MR acquisition and analysis pipeline for in vivo human placenta, which allows for exploration of coupling between T2* and apparent diffusion coefficient (ADC) measurements in a sub 10‐minute scan time.

Methods

We present a novel acquisition combining a diffusion prepared spin echo with subsequent gradient echoes. The placentas of 17 pregnant women were scanned in vivo, including both healthy controls and participants with various pregnancy complications. We estimate the joint T2*‐ADC spectra using an inverse Laplace transform.

Results

T2*‐ADC spectra demonstrate clear quantitative separation between normal and dysfunctional placentas.

Conclusions

Combined T2*‐diffusivity MRI is promising for assessing fetal and maternal health during pregnancy. The T2*‐ADC spectrum potentially provides additional information on tissue microstructure, compared to measuring these two contrasts separately. The presented method is immediately applicable to the study of other organs.

Rapid detection of the presence of diffusion exchange

Diffusion exchange spectroscopy (DEXSY) provides a detailed picture of how fluids in different microenvironments communicate with one another but requires a large amount of data. For DEXSY MRI, a simple measure of apparent exchanging fractions may suffice to characterize and differentiate materials and tissues. Reparameterizing signal intensity from a PGSE-storage-PGSE experiment as a function of the sum, b_s=b₁+b₂, and difference b_d=b₂-b₁ of the diffusion encodings separates diffusion weighting from exchange weighting. Exchange leads to upward curvature along a slice of constant b_s. Exchanging fractions can be measured rapidly by a finite difference approximation of the curvature using four data points. The method is generalized for non-steady-state and multi-site exchange. We apply the method to image exchanging fractions and calculate exchange rates of water diffusing across the bulk water interface of a glass capillary array.

TE dependent Diffusion Imaging (TEdDI) distinguishes between compartmental T2 relaxation times

Biophysical modeling of macroscopic diffusion-weighted MRI signal in terms of microscopic cellular parameters holds the promise of quantifying the integrity of white matter. Unfortunately, even fairly simple multi-compartment models of proton diffusion in the white matter do not provide a unique, biophysically plausible solution. Here we report a nontrivial diffusion MRI signal dependence on echo time (TE) in human white matter in vivo. We demonstrate that such TE dependence originates from compartment-specific T2 values and that it is a promising "orthogonal measure" able to break the degeneracy in parameter estimation, and to yield important relaxation metrics robustly. We thereby enable the precise estimation of the intra- and extra-axonal water T2 relaxation times, which is precluded by a limited signal-to-noise ratio when using multi-echo relaxometry alone.

Integrated and efficient diffusion-relaxometry using ZEBRA

The emergence of multiparametric diffusion models combining diffusion and relaxometry measurements provides powerful new ways to explore tissue microstructure, with the potential to provide new insights into tissue structure and function. However, their ability to provide rich analyses and the potential for clinical translation critically depends on the availability of efficient, integrated, multi-dimensional acquisitions. We propose a fully integrated sequence simultaneously sampling the acquisition parameter spaces required for T1 and T2* relaxometry and diffusion MRI. Slice-level interleaved diffusion encoding, multiple spin/gradient echoes and slice-shuffling are combined for higher efficiency, sampling flexibility and enhanced internal consistency. In-vivo data was successfully acquired on healthy adult brains. Obtained parametric maps as well as clustering results demonstrate the potential of the technique to provide eloquent data with an acceleration of roughly 20 compared to conventionally used approaches. The proposed integrated acquisition, which we call ZEBRA, offers significant acceleration and flexibility compared to existing diffusion-relaxometry studies, and thus facilitates wider use of these techniques both for research-driven and clinical applications.

Identification of Intracellular and Extracellular Metabolites in Cancer Cells Using 13C Hyperpolarized Ultrafast Laplace NMR

Ultrafast Laplace NMR (UF-LNMR), which is based on the spatial encoding of multidimensional data, enables one to carry out 2D relaxation and diffusion measurements in a single scan. Besides reducing the experiment time to a fraction, it significantly facilitates the use of nuclear spin hyperpolarization to boost experimental sensitivity, because the time-consuming polarization step does not need to be repeated. Here we demonstrate the usability of hyperpolarized UF-LNMR in the context of cell metabolism, by investigating the conversion of pyruvate to lactate in the cultures of mouse 4T1 cancer cells. We show that ¹³C ultrafast diffusion–T₂ relaxation correlation measurements, with the sensitivity enhanced by several orders of magnitude by dissolution dynamic nuclear polarization (D-DNP), allows the determination of the extra- vs intracellular location of metabolites because of their significantly different values of diffusion coefficients and T₂ relaxation times. Under the current conditions, pyruvate was located predominantly in the extracellular pool, while lactate remained primarily intracellular. Contrary to the small flip angle diffusion methods reported in the literature, the UF-LNMR method does not require several scans with varying gradient strength, and it provides a combined diffusion and T₂ contrast. Furthermore, the ultrafast concept can be extended to various other multidimensional LNMR experiments, which will provide detailed information about the dynamics and exchange processes of cell metabolites.

Probing molecular dynamics with hyperpolarized ultrafast Laplace NMR using a low-field, single-sided magnet

Laplace NMR (LNMR) offers deep insights on diffusional and rotational motion of molecules. The so-called "ultrafast" approach, based on spatial data encoding, enables one to carry out a multidimensional LNMR experiment in a single scan, providing from 10 to 1000-fold acceleration of the experiment. Here, we demonstrate the feasibility of ultrafast diffusion-T2 relaxation correlation (D-T2) measurements with a mobile, low-field, relatively low-cost, single-sided NMR magnet. We show that the method can probe a broad range of diffusion coefficients (at least from 10-8 to 10-12 m2 s-1) and reveal multiple components of fluids in heterogeneous materials. The single-scan approach is demonstrably compatible with nuclear spin hyperpolarization techniques because the time-consuming hyperpolarization process does not need to be repeated. Using dynamic nuclear polarization (DNP), we improved the NMR sensitivity of water molecules by a factor of 105 relative to non-hyperpolarized NMR in the 0.3 T field of the single-sided magnet. This enabled us to acquire a D-T2 map in a single, 22 ms scan, despite the low field and relatively low mole fraction (0.003) of hyperpolarized water. Consequently, low-field, hyperpolarized ultrafast LNMR offers significant prospects for advanced, mobile, low-cost and high-sensitivity chemical and medical analysis.

Hyperpolarized Laplace NMR

Laplace nuclear magnetic resonance (NMR), dealing with NMR relaxation and diffusion experiments, reveals details of molecular motion and provides chemical resolution complementary to NMR spectra. Laplace NMR has witnessed a great progress in past decades due to the development of methodology and signal processing, and it has lots of extremely interesting applications in various fields, including chemistry, biochemistry, geology, archaeology, and medicine. The aim of this minireview is to give a pedagogically oriented overview of Laplace NMR. It does not provide a full literature review of the field, but, instead, it elucidate the benefits and features of Laplace NMR methods through few selected examples. The minireview describes also recent progress in multidimensional Laplace NMR and Laplace inversion methods. Furthermore, the potential of modern hyperpolarization methods as well as ultrafast approach to increase the sensitivity and time-efficiency of the Laplace NMR experiments is highlighted.

Applications of dissolution dynamic nuclear polarization in chemistry and biochemistry

Sensitivity of detection is one of the most limiting aspects when applying NMR spectroscopy to current problems in the molecular sciences. A number of hyperpolarization methods exist for increasing the population difference between nuclear spin Zeeman states and enhance the signal-to-noise ratio by orders of magnitude. Among these methods, dissolution dynamic nuclear polarization (D-DNP) is unique in its capability of providing high spin polarization for many types of molecules in the liquid state. Originally proposed for biomedical applications including in vivo imaging, applications in high resolution NMR spectroscopy are now emerging. These applications are the focus of the present review. Using D-DNP, a small sample aliquot is first hyperpolarized as a frozen solid at low temperature, followed by dissolution into the liquid state. D-DNP extends the capabilities of liquid state NMR spectroscopy towards shorter timescales and enables the study of nonequilibrium processes, such as the kinetics and mechanisms of reactions. It allows the determination of intermolecular interactions, in particular based on spin relaxation parameters. At the same time, a challenge in the application of this hyperpolarization method is that spin polarization is nonrenewable. Substantial effort has been devoted to develop methods for enabling rapid correlation spectroscopy, the measurement of time-dependent signals, and the extension of the observable time window. With these methods, D-DNP has the potential to open new application areas in the chemical and biochemical sciences.

Structure and dynamics elucidation of ionic liquids using multidimensional Laplace NMR

We demonstrate the ability of multidimensional Laplace NMR (LNMR), comprising relaxation and diffusion experiments, to reveal essential information about microscopic phase structures and dynamics of ionic liquids that is not observable using conventional NMR spectroscopy or other techniques.

Multidimensional correlation of nuclear relaxation rates and diffusion tensors for model-free investigations of heterogeneous anisotropic porous materials

Despite their widespread use in non-invasive studies of porous materials, conventional MRI methods yield ambiguous results for microscopically heterogeneous materials such as brain tissue. While the forward link between microstructure and MRI observables is well understood, the inverse problem of separating the signal contributions from different microscopic pores is notoriously difficult. Here, we introduce an experimental protocol where heterogeneity is resolved by establishing 6D correlations between the individual values of isotropic diffusivity, diffusion anisotropy, orientation of the diffusion tensor, and relaxation rates of distinct populations. Such procedure renders the acquired signal highly specific to the sample's microstructure, and allows characterization of the underlying pore space without prior assumptions on the number and nature of distinct microscopic environments. The experimental feasibility of the suggested method is demonstrated on a sample designed to mimic the properties of nerve tissue. If matched to the constraints of whole body scanners, this protocol could allow for the unconstrained determination of the different types of tissue that compose the living human brain.

Magnetic resonance microdynamic imaging reveals distinct tissue microenvironments

Magnetic resonance imaging (MRI) provides a powerful set of tools with which to investigate biological tissues noninvasively and in vivo. Tissues are heterogeneous in nature; an imaging voxel contains an ensemble of different cells and extracellular matrix components. A long-standing challenge has been to infer the content of and interactions among these microscopic tissue components within a macroscopic imaging voxel. Spatially resolved multidimensional relaxation-diffusion correlation (REDCO) spectroscopy holds the potential to deliver such microdynamic information. However, to date, vast data requirements have mostly relegated these type of measurements to nuclear magnetic resonance applications and prevented them from being widely and successfully used in conjunction with imaging. By using a novel data acquisition and processing strategy in this study, spatially resolved REDCO could be performed in reasonable scanning times with excellent prospects for clinical applications. This new MR imaging framework-which we term "magnetic resonance microdynamic imaging (MRMI)"-permits the simultaneous noninvasive and model-free quantification of multiple subcellular, cellular, and interstitial tissue microenvironments within a voxel. MRMI is demonstrated with a fixed spinal cord specimen, enabling the quantification of microscopic tissue components with unprecedented specificity. Tissue components, such as axons, neuronal and glial soma, and myelin were identified on the basis of their multispectral signature within individual imaging voxels. These tissue elements could then be composed into images and be correlated with immunohistochemistry findings. MRMI provides novel image contrasts of tissue components and a new family of microdynamic biomarkers that could lead to new diagnostic imaging approaches to probe biological tissue alterations accompanied by pathological or developmental changes.

Analysis of 2D NMR relaxation data using Chisholm approximations

To analyze 2D NMR relaxation data based on a discrete delta-like relaxation map we extended the Padé-Laplace method to two dimensions. We approximate the forward Laplace image of the time domain signal by a Chisholm approximation, i.e. a rational polynomial in two dimensions. The poles and residues of this approximation correspond to the relaxation rates and weighting factors of the underlying relaxation map. In this work we explain the principle ideas of our algorithm and demonstrate its applicability. Therefore we compare the inversion results of the Chisholm approximation and Tikhonov regularization method as a function of SNR when the investigated signal is based on a given discrete relaxation map. Our algorithm proved to be reliable for SNRs larger than 50 and is able to compete with the Tikhonov regularization method. Furthermore we show that our method is also able to detect the simulated relaxation compartments of narrow Gaussian distributions with widths less or equal than 0.05s^-1. Finally we investigate the resolution limit with experimental data. For a SNR of 750 the Chisholm approximation method was able to resolve two relaxation compartments in 8 of 10 cases when both compartments differ by a factor of 1.7.

NMR quantification of diffusional exchange in cell suspensions with relaxation rate differences between intra and extracellular compartments

Water transport across cell membranes can be measured non-invasively with diffusion NMR. We present a method to quantify the intracellular lifetime of water in cell suspensions with short transverse relaxation times, T₂, and also circumvent the confounding effect of different T₂ values in the intra- and extracellular compartments. Filter exchange spectroscopy (FEXSY) is specifically sensitive to exchange between compartments with different apparent diffusivities. Our investigation shows that FEXSY could yield significantly biased results if differences in T₂ are not accounted for. To mitigate this problem, we propose combining FEXSY with diffusion-relaxation correlation experiment, which can quantify differences in T₂ values in compartments with different diffusivities. Our analysis uses a joint constrained fitting of the two datasets and considers the effects of diffusion, relaxation and exchange in both experiments. The method is demonstrated on yeast cells with and without human aquaporins.

Multidimensional diffusion MRI

Principles from multidimensional NMR spectroscopy, and in particular solid-state NMR, have recently been transferred to the field of diffusion MRI, offering non-invasive characterization of heterogeneous anisotropic materials, such as the human brain, at an unprecedented level of detail. Here we revisit the basic physics of solid-state NMR and diffusion MRI to pinpoint the origin of the somewhat unexpected analogy between the two fields, and provide an overview of current diffusion MRI acquisition protocols and data analysis methods to quantify the composition of heterogeneous materials in terms of diffusion tensor distributions with size, shape, and orientation dimensions. While the most advanced methods allow estimation of the complete multidimensional distributions, simpler methods focus on various projections onto lower-dimensional spaces as well as determination of means and variances rather than actual distributions. Even the less advanced methods provide simple and intuitive scalar parameters that are directly related to microstructural features that can be observed in optical microscopy images, e.g. average cell eccentricity, variance of cell density, and orientational order - properties that are inextricably entangled in conventional diffusion MRI. Key to disentangling all these microstructural features is MRI signal acquisition combining isotropic and directional dimensions, just as in the field of multidimensional solid-state NMR from which most of the ideas for the new methods are derived.

Sensitivity and resolution of two-dimensional NMR diffusion-relaxation measurements

The performance of 2D NMR diffusion-relaxation measurements for fluid typing applications is analyzed. In particular, we delineate the region in the diffusion - relaxation plane that can be determined with a given gradient strength and homogeneity, and compare the performance of the single and double echo encoding with the stimulated echo diffusion encoding. We show that the diffusion editing based approach is able to determine the diffusion coefficient only if the relaxation time T2 exceeds a cutoff value T2,cutoff, that scales like T2,cutoff∝g(-2/3)D(-1/3). For stimulated echo encoding, the optimal diffusion encoding times (Td and δ), that provide the best diffusion sensitivity, rely only on the T1/T2 ratios and not on the diffusion coefficients of the fluids or the applied gradient strengths. Irrespective of T1, for high enough gradients (i.e. when γ(2)g(2)DT2(3)>10(2)), the Hahn echo based encoding is superior to encoding based on the stimulated echo. For weaker gradients, the stimulated echo is superior only if the T1/T2 ratio is much larger than 1. For single component systems, the diffusion sensitivity is not adversely impacted by the uniformity of the gradients and the diffusion distributions can be well measured. The presence of non-uniform gradients can affect the determination of the diffusion distributions when you have two fluids of comparable T2. In such situations the effective single component diffusion coefficient is always closer to the geometric mean diffusion coefficient of the two fluids.

The Power of Heterogeneity: Parameter Relationships from Distributions

Complex scientific data is becoming the norm, many disciplines are growing immensely data-rich, and higher-dimensional measurements are performed to resolve complex relationships between parameters. Inherently multi-dimensional measurements can directly provide information on both the distributions of individual parameters and the relationships between them, such as in nuclear magnetic resonance and optical spectroscopy. However, when data originates from different measurements and comes in different forms, resolving parameter relationships is a matter of data analysis rather than experiment. We present a method for resolving relationships between parameters that are distributed individually and also correlated. In two case studies, we model the relationships between diameter and luminescence properties of quantum dots and the relationship between molecular weight and diffusion coefficient for polymers. Although it is expected that resolving complicated correlated relationships require inherently multi-dimensional measurements, our method constitutes a useful contribution to the modelling of quantitative relationships between correlated parameters and measurements. We emphasise the general applicability of the method in fields where heterogeneity and complex distributions of parameters are obstacles to scientific insight.

Quantification of microcirculatory parameters by joint analysis of flow-compensated and non-flow-compensated intravoxel incoherent motion (IVIM) data

The aim of this study was to improve the accuracy and precision of perfusion fraction and blood velocity dispersion estimates in intravoxel incoherent motion (IVIM) imaging, using joint analysis of flow-compensated and non-flow-compensated motion-encoded MRI data. A double diffusion encoding sequence capable of switching between flow-compensated and non-flow-compensated encoding modes was implemented. In vivo brain data were collected in eight healthy volunteers and processed using the joint analysis. Simulations were used to compare the performance of the proposed analysis method with conventional IVIM analysis. With flow compensation, strong rephasing was observed for the in vivo data, approximately cancelling the IVIM effect. The joint analysis yielded physiologically reasonable perfusion fraction maps. Estimated perfusion fractions were 2.43 ± 0.81% in gray matter, 1.81 ± 0.90% in deep gray matter, and 1.64 ± 0.72% in white matter (mean ± SD, n = 8). Simulations showed improved accuracy and precision when using joint analysis of flow-compensated and non-flow-compensated data, compared with conventional IVIM analysis. Double diffusion encoding with flow compensation was feasible for in vivo imaging of the perfusion fraction in the brain. The strong rephasing implied that blood flowing through the cerebral microvascular system was closer to the ballistic limit than the diffusive limit.

Two dimensional IR-FID-CPMG acquisition and adaptation of a maximum entropy reconstruction

By acquiring the FID signal in two-dimensional TD-NMR spectroscopy, it is possible to characterize mixtures or complex samples composed of solid and liquid phases. We have developed a new sequence for this purpose, called IR-FID-CPMG, making it possible to correlate spin-lattice T1 and spin-spin T2 relaxation times, including both liquid and solid phases in samples. We demonstrate here the potential of a new algorithm for the 2D inverse Laplace transformation of IR-FID-CPMG data based on an adapted reconstruction of the maximum entropy method, combining the standard decreasing exponential decay function with an additional term drawn from Abragam's FID function. The results show that the proposed IR-FID-CPMG sequence and its related inversion model allow accurate characterization and quantification of both solid and liquid phases in multiphasic and compartmentalized systems. Moreover, it permits to distinguish between solid phases having different T1 relaxation times or to highlight cross-relaxation phenomena.

Ultrafast Multidimensional Laplace NMR Using a Single-Sided Magnet

Laplace NMR (LNMR) consists of relaxation and diffusion measurements providing detailed information about molecular motion and interaction. Here we demonstrate that ultrafast single- and multidimensional LNMR experiments, based on spatial encoding, are viable with low-field, single-sided magnets with an inhomogeneous magnetic field. This approach shortens the experiment time by one to two orders of magnitude relative to traditional experiments, and increases the sensitivity per unit time by a factor of three. The reduction of time required to collect multidimensional data opens significant prospects for mobile chemical analysis using NMR. Particularly tantalizing is future use of hyperpolarization to increase sensitivity by orders of magnitude, allowed by single-scan approach.

Two-Dimensional Correlation of Isotropic and Directional Diffusion Using NMR

Diffusion nuclear magnetic resonance (NMR) is a powerful technique for studying porous media, but yields ambiguous results when the sample comprises multiple regions with different pore sizes, shapes, and orientations. Inspired by solid-state NMR techniques for correlating isotropic and anisotropic chemical shifts, we propose a diffusion NMR method to resolve said ambiguity. Numerical data inversion relies on sparse representation of the data in a basis of radial and axial diffusivities. Experiments are performed on a composite sample with a cell suspension and a liquid crystal.

Gamma convolution models for self-diffusion coefficient distributions in PGSE NMR

We introduce a closed-form signal attenuation model for pulsed-field gradient spin echo (PGSE) NMR based on self-diffusion coefficient distributions that are convolutions of n gamma distributions, n⩾1. Gamma convolutions provide a general class of uni-modal distributions that includes the gamma distribution as a special case for n=1 and the lognormal distribution among others as limit cases when n approaches infinity. We demonstrate the usefulness of the gamma convolution model by simulations and experimental data from samples of poly(vinyl alcohol) and polystyrene, showing that this model provides goodness of fit superior to both the gamma and lognormal distributions and comparable to the common inverse Laplace transform.