T2 Relaxation Curves Research Articles

Evidence for biexponential glutamate T2 relaxation in human visual cortex at 3T: A functional MRS study.

Functional magnetic resonance spectroscopy (fMRS) measures dynamic changes in metabolite concentration in response to neural stimulation. The biophysical basis of these changes remains unclear. One hypothesis suggests that an increase or decrease in the glutamate signal detected by fMRS could be due to neurotransmitter movements between cellular compartments with different T2 relaxation times. Previous studies reporting glutamate (Glu) T2 values have generally sampled at echo times (TEs) within the range of 30-450 ms, which is not adequate to observe a component with short T2 (<20 ms). Here, we acquire MRS measurements for Glu, (t) total creatine (tCr) and total N-acetylaspartate (tNAA) from the visual cortex in 14 healthy participants at a range of TE values between 9.3-280 ms during short blocks (64 s) of flickering checkerboards and rest to examine both the short- and long-T2 components of the curve. We fit monoexponential and biexponential Glu, tCr and tNAA T2 relaxation curves for rest and stimulation and use Akaike information criterion to assess best model fit. We also include power calculations for detection of a 2% shift of Glu between compartments for each TE. Using pooled data over all participants at rest, we observed a short Glu T2-component with T2 = 10ms and volume fraction of 0.35, a short tCr T2-component with T2 = 26 ms and volume fraction of 0.25 and a short tNAA T2-component around 15 ms with volume fraction of 0.34. No statistically significant change in Glu, tCr and tNAA signal during stimulation was detected at any TE. The volume fractions of short-T2 component between rest and active conditions were not statistically different. This study provides evidence for a short T2-component for Glu, tCr and tNAA but no evidence to support the hypothesis of task-related changes in glutamate distribution between short and long T2 compartments.

Variation and prediction of unfrozen water content in different soils at extremely low temperature conditions

The unfrozen water content in frozen soil is the critical factor for liquid water migration, frost heaving and thawing settlement during freeze–thaw process, and its content largely depends on soil texture and temperature. Herein, the unfrozen water content in different soils was measured by low-field nuclear magnetic resonance (NMR) at extremely low temperature ranging from −80 to 0 °C. The effect of soil properties on the variation of pore water in soil during freezing is analysed by combining T2 relaxation curve and freezing characteristic curve. The results indicate that soil properties affect the phase transition process between liquid water and solid ice in frozen soil, and the pore size distribution plays a leading role in capillary water freezing in the severe phase transformation zone. When unfrozen water develops from the transitional phase transformation zone to the frozen stability zone, the freezing of adsorbed water in soil is affected by clay content. In addition, by analogy with the soil freezing characteristic curve, a prediction model of unfrozen water content of soil is established in accordance with the phase transition process of liquid water, and the accuracy of the model is further confirmed by comparing the test results. Sensitivity analysis of the proposed model parameters revealed that the parameters are closely related to the pore size distribution and the clay content. The proposed model expands the response range between the variation of unfrozen water and temperature by experimental means, and realizes the prediction of unfrozen water content in different types of soil at extremely low temperature. This study would provide significant reference for exploring the heat-moisture migration and mechanical strength in frozen soil.

Continuous Monitoring of the Hydration Behavior of Hydrophilic Matrix Tablets Using Time-Domain NMR.

Time-domain NMR (TD-NMR) was used for continuous monitoring of the hydration behavior of hydrophilic matrix tablets. The model matrix tablets comprised high molecular weight polyethylene oxide (PEO), hydroxypropyl methylcellulose (HPMC), and polyethylene glycol (PEG). The model tablets were immersed in water. Their T2 relaxation curves were acquired by TD-NMR with solid-echo sequence. A curve-fitting analysis was conducted on the acquired T2 relaxation curves to identify the NMR signals corresponding to the nongelated core remaining in the samples. The amount of nongelated core was estimated from the NMR signal intensity. The estimated values were consistent with the experiment measurement values. Next, the model tablets immersed in water were monitored continuously using TD-NMR. The difference in hydration behaviors of the HPMC and PEO matrix tablets was then characterized fully. The nongelated core of the HPMC matrix tablets disappeared more slowly than that of the PEO matrix tablets. The behavior of HPMC was significantly affected by the PEG content in the tablets. It is suggested that the TD-NMR method has potential to be utilized to evaluate the gel layer properties, upon replacement of the immersion medium: purified (nondeuterated) water is replaced with heavy (deuterated) water. Finally, drug-containing matrix tablets were tested. Diltiazem hydrochloride (a highly water-soluble drug) was employed for this experiment. Reasonable in vitro drug dissolution profiles, which were in accordance with the results from TD-NMR experiments, were observed. We concluded that TD-NMR is a powerful tool to evaluate the hydration properties of hydrophilic matrix tablets.

Polymer composition optimization approach based on feature extraction of bound and free water using time-domain nuclear magnetic resonance

As global environmental sustainability becomes increasingly emphasized, the development of eco-friendly materials, including solutions to the issue of marine plastics, is thriving. However, the material parameter space is vast, making efficient search a challenge. Time-domain nuclear magnetic resonance offers material property information through the complex T2 relaxation curves resulting from multiple mobilities. In this research, we used the Carr–Purcell–Meiboom–Gill (CPMG) pulse sequence to evaluate the binding state of water (water affinity) in polymers synthesized with various monomer compositions, which were immersed in seawater. We also assessed the T2 relaxation property of the polymers using the magic sandwich echo, double quantum filter, and magic-and-polarization echo filter techniques. We separated the T2 relaxation curves of CPMG into free and bound water for polymers by employing semisupervized nonnegative matrix factorization. By employing the features of separated bound water and polymer properties, a polymer composition optimization method offered crucial factors to monomers through random forests, predicted the components of the polymer using generative topography mapping regression, and determined expected values using Bayesian optimization for polymer composition candidates with the desired high water affinity and high rigidity.

Semi-Autonomic AI LF-NMR Sensor for Industrial Prediction of Edible Oil Oxidation Status.

The evaluation of an oil's oxidation status during industrial production is highly important for monitoring the oil's purity and nutritional value during production, transportation, storage, and cooking. The oil and food industry is seeking a real-time, non-destructive, rapid, robust, and low-cost sensor for nutritional oil's material characterization. Towards this goal, a 1H LF-NMR relaxation sensor application based on the chemical and structural profiling of non-oxidized and oxidized oils was developed. This study dealt with a relatively large-scale oil oxidation database, which included crude data of a 1H LF-NMR relaxation curve, and its reconstruction into T1 and T2 spectral fingerprints, self-diffusion coefficient D, and conventional standard chemical test results. This study used a convolutional neural network (CNN) that was trained to classify T2 relaxation curves into three ordinal classes representing three different oil oxidation levels (non-oxidized, partial oxidation, and high level of oxidation). Supervised learning was used on the T2 signals paired with the ground-truth labels of oxidation values as per conventional chemical lab oxidation tests. The test data results (not used for training) show a high classification accuracy (95%). The proposed AI method integrates a large training set, an LF-NMR sensor, and a machine learning program that meets the requirements of the oil and food industry and can be further developed for other applications.

Usefulness of Applying Partial Least Squares Regression to T2 Relaxation Curves for Predicting the Solid form Content in Binary Physical Mixtures

This study applied partial least squares (PLS) regression to nuclear magnetic resonance (NMR) relaxation curves to quantify the free base of an active pharmaceutical ingredient powder. We measured the T2 relaxation of intact and moisture-absorbed physical mixtures of tetracaine free base (TC) and its hydrochloride salt (TC·HCl). The obtained T2 relaxation curves were analyzed by two methods, one using a previously reported T2 relaxation time (T2), and the other using PLS regression. The accuracy of estimating TC was inadequate when using previous T2 values because the moisture-absorbed physical mixtures showed biphasic T2 relaxation curves. By contrast, the entire measured whole of the T2 relaxation curves was used as input variables and analyzed by PLS regression to quantify the content of TC in the moisture-absorbed TC/TC·HCl. Based on scatterplots of theoretical versus predicted TC, the obtained PLS model exhibited acceptable coefficients of determination and relatively low root mean squared error values for calibration and validation data. The statistical values confirmed that an accurate and reliable PLS model was created to quantify TC in even moisture-absorbed TC/TC·HCl. The bench-top low-field NMR instrument used to apply PLS regression to the T2 relaxation curve may be a promising tool in process analytical technology.

Determination of Hardness of a Pharmaceutical Oral Jelly by Using T2 Relaxation Behavior Measured by Time-Domain NMR.

Hardness is a critical quality characteristic of pharmaceutical oral jelly. In this study, the hardness was determined by using the T2 relaxation curves measured by time-domain NMR. For sample preparation, kappa- and iota-carrageenans, and locust bean gum, were used as gel-forming agents. Ten test jellies with different gel-forming agent composition were prepared, and their hardness and T2 relaxation curves were measured by a texture analyzer and time-domain NMR (TD-NMR). A negative correlation between T2 relaxation time (T2) and hardness was observed; however, it was difficult to determine the hardness directly from the T2 value. That is probably because the T2 relaxation curve contains information about molecular states, not only of water but also of the solute, and T2 values calculated by single-exponential curve fitting only express one property of the test jelly. By considering this issue, partial least squares (PLS) regression analysis was performed on the T2 relaxation curves for hardness determination of the test jellies. According to the analysis, an accurate and reliable PLS model was created that enabled accurate assessment of the hardness of the test jellies. TD-NMR enables the measurement of samples nondestructively and rapidly with low cost, and so could be a promising method for evaluation of the hardness of pharmaceutical oral jellies.

Characterization of the Salt and Free Base of Active Pharmaceutical Ingredients Based on NMR Relaxometry Measured by Time Domain NMR.

NMR relaxometry measurement by time domain NMR (TD-NMR) is a promising technique for characterizing the properties of active pharmaceutical ingredients (APIs). This study is dedicated to identifying the salt and free base of APIs by NMR relaxometry measured by the TD-NMR technique. Procaine (PC) and tetracaine (TC) were selected as model APIs to be tested. By using conventional methods including powder X-ray diffraction and differential scanning calorimetry, this study first confirmed that the salt and free base of the tested APIs differ from each other in their crystalline form. Subsequently, measurements of T1 and T2 relaxation were performed on the tested APIs using TD-NMR. The results demonstrated that these NMR relaxometry measurements have sufficient capacity to distinguish the difference between the free base and salt of the tested APIs. Furthermore, quantification of the composition of the binary powder blends consisting of salt and free bases was conducted by analyzing the acquired T1 and T2 relaxation curves. The analysis of the T1 relaxation curves provided a partly acceptable estimation: a good estimation of the composition was observed from PC powders, whereas for TC powders the estimation accuracy changed with the free base content in the binary blends. For the analysis on T2 relaxation curves, a precise estimation of the composition was observed from all the samples. From these findings, the NMR relaxometry measurement by TD-NMR, in particular the T2 relaxation measurement, is effective for evaluating the properties of APIs having different crystalline forms.

In Situ RheoNMR Correlation of Polymer Segmental Mobility with Mechanical Properties during Hydrogel Synthesis.

Understanding polymer gelation over multiple length‐scales is crucial to develop advanced materials. An experimental setup is developed that combines rheological measurements with simultaneous time‐domain 1H NMR relaxometry (TD‐NMR) techniques, which are used to study molecular motion (<10 nm) in soft matter. This so‐called low‐field RheoNMR setup is used to study the impact of varying degrees of crosslinking (DC) on the gelation kinetics of acrylic acid (AAc) and N,N′‐methylene bisacrylamide (MBA) free radical crosslinking copolymerization. A stretched exponential function describes the T 2 relaxation curves throughout the gelation process. The stretching exponent β decreases from 0.90 to 0.67 as a function of increasing DC, suggesting an increase in network heterogeneity with a broad T 2 distribution at higher DC. The inverse correlation of the elastic modulus G′ with T 2 relaxation times reveals a pronounced molecular rigidity for higher DC at early gelation times, indicating the formation of inelastic, rigid domains such as crosslinking clusters. The authors further correlate G′ with the polymer concentration during gelation using a T 1 filter for solvent suppression. A characteristic scaling exponent of 2.3 is found, which is in agreement with theoretical predictions of G′ based on the confining tube model in semi‐dilute entangled polymer solutions.

Time-domain NMR analysis for the determination of water content in pharmaceutical ingredients and wet granules

The application of time-domain NMR (TD-NMR) analysis to quantify water content in pharmaceutical ingredients is demonstrated. The initial phase of the study employed a range of disintegrants with defined amounts of added water (0–30% of the total weight) as samples; the disintegrants included croscarmellose sodium, corn starch, low-substituted hydroxypropyl cellulose, and crospovidone. After acquisition of the T2 relaxation curves of the samples by TD-NMR measurements, these curves were analyzed by partial least squares (PLS) regression. According to the analysis, accurate and reliable PLS models were created that enabled accurate assessment of water content in the samples. A powder blend consisting of acetaminophen (paracetamol) and tablet excipients was also examined. Both a physical mixture of the powder blend and a wet granule prepared with a high-speed granulator were tested as samples in this study. Precise determination of water content in the powder blend was achieved by using the TD-NMR method. The accuracy of water content determination was equivalent to or better than that of the conventional loss on drying method. TD-NMR analysis samples were measured nondestructively and rapidly with low cost; thus, it could be a powerful quantitative method for determining water content in pharmaceuticals.

Relaxometric learning: a pattern recognition method for T2 relaxation curves based on machine learning supported by an analytical framework

Nuclear magnetic resonance (NMR)-based relaxometry is widely used in various fields of research because of its advantages such as simple sample preparation, easy handling, and relatively low cost compared with metabolomics approaches. However, there have been no reports on the application of the T2 relaxation curves in metabolomics studies involving the evaluation of metabolic mixtures, such as geographical origin determination and feature extraction by pattern recognition and data mining. In this study, we describe a data mining method for relaxometric data (i.e., relaxometric learning). This method is based on a machine learning algorithm supported by the analytical framework optimized for the relaxation curve analyses. In the analytical framework, we incorporated a variable optimization approach and bootstrap resampling-based matrixing to enhance the classification performance and balance the sample size between groups, respectively. The relaxometric learning enabled the extraction of features related to the physical properties of fish muscle and the determination of the geographical origin of the fish by improving the classification performance. Our results suggest that relaxometric learning is a powerful and versatile alternative to conventional metabolomics approaches for evaluating fleshiness of chemical mixtures in food and for other biological and chemical research requiring a nondestructive, cost-effective, and time-saving method.

Quantitative Evaluation of the Crystallinity of Indomethacin Using 1H T2 Relaxation Behaviors Measured by Time Domain NMR

We sought to demonstrate the usefulness of the T2 relaxation behaviors measured by time domain NMR (TD-NMR) for the quantitative evaluation of the crystallinity of an active pharmaceutical ingredient (API). This study used indomethacin as a model API. After blending amorphous and crystalline indomethacin powders at a designated ratio, T2 relaxation curves were measured by TD-NMR. Subsequently, we acquired a calibration curve to quantify crystallinity by curve fitting analysis. Validation demonstrated a good correlation between the theoretical and experimental percentage of crystallinity. Thus, this study succeeded in a precise estimation of crystallinity of indomethacin using TD-NMR. We also investigated whether the technique is practical by testing indomethacin powders with unknown proportion of crystallinity, and then compared their estimated crystallinity with that found using conventional evaluation techniques. The quantitative performance of the TD-NMR technique was comparable to that of Raman spectroscopy. Furthermore, indomethacin powder blended with excipients, which can be used to produce tablets, was tested. The TD-NMR technique was still able to quantify the crystallinity of indomethacin, even when excipients were incorporated into the sample. Therefore, the present study expands the horizon for evaluating the crystallinity of APIs in pharmaceutical sciences.

T2 Relaxation Study to Evaluate the Crystalline State of Indomethacin Containing Solid Dispersions Using Time-Domain NMR.

The aim of this study was to demonstrate the usefulness of T2 measurements conducted with a time-domain NMR (TD-NMR) for the characterization of active pharmaceutical ingredients (APIs) containing solid dosage forms. A solid dispersion (SD) and a physical mixture (PM) consisting of indomethacin (IMC) and polyvinylpyrrolidone (PVP) were prepared at different weight ratios as test samples, and then their T2 relaxation curves were measured by TD-NMR. The T2 relaxation curve of IMC was quite different from that of PVP by nature. T2 values of the SD and PM samples became gradually shortened with increasing IMC content. No difference in T2 relaxation curves was observed between SD and PM. By analyzing the T2 relaxation curves in detail, we succeeded in precisely quantifying the IMC contents incorporated in the samples. Next, this study evaluated the T2 relaxation curves of amorphous and crystalline states of powdered IMC. T2 relaxation rate of crystalline IMC was slightly but significantly higher than that of amorphous IMC, proving that the T2 measurement was sensitive enough to detect these differences. Finally, a thermal stress was imposed on SD and PM samples at 60°C for 7 d, and then an amorphous-to-crystalline transformation occurred in IMC in the PM sample and was successfully monitored by T2 measurement. We believe that T2 measurement by TD-NMR is a promising analysis for the characterization of APIs in solid dosage forms, including SD-based pharmaceuticals.

Distinguishing fractures from matrix pores based on the practical application of rock physics inversion and NMR data: A case study from an unconventional coal reservoir in China

Our main scheme in this study was distinguishing between fracture porosity and matrix porosity in coalbed methane reservoirs through a novel approach which is the joint usage of NMR transverse relaxation (T2) measurements and rock physics modeling based on Levenberg-Marquardt (LM) algorithm. For this purpose, NMR T2 relaxation curves of 34 water-saturated coal samples, prepared and processed in the laboratory, were measured and the pore size distribution inside them was investigated. Subsequently, matrix and fracture porosity were separated based on the threshold T2 relaxation time (90–110 ms) which was achieved through our particularly designed fracturing experiments (this is different from the common T2 cutoff). The T2 measurements were performed in the laboratory using our NMR machine. After that, a rock physics scheme based on Levenberg-Marquardt algorithm was applied to independently estimate matrix porosity and fracture porosity from the samples' statistic mechanical properties including compressional wave velocity (Vp), shear wave velocity (Vs), bulk modulus (K), shear modulus (G), Young's modulus (E), and Poisson's ratio (ν) which were all carefully measured in the laboratory. Afterward, both types of the abovementioned porosities were comprehensively characterized and the obtained achievements were listed. Once finished with this step, the 3D structure of the entire reservoir was extracted using the recorded data inside 32 drilled wells, and then the established models were upscaled and the unique and independent contour maps of fracture porosity and matrix pore porosity were drawn over the entire reservoir area.The proposed novel approach provides the kind of information about fractures of the media which is not obtainable with either of NMR or rock physics methods when they are used individually. This study established a novel discussion investigating application of rock physics relationships in order to determine fracture porosity of the coal reservoirs. The approach was used to successfully investigate and characterize pore-only porosity and fracture-only porosity of a coalbed methane reservoir. According to the obtained results, joint usage of rock physics modeling and LM algorithm would be considered as a reliable technique in quick or deeper exploration of unconventional coal reservoirs.

A Time-Domain NMR Study of the State of Water in Wet Granules with Different Fillers and Its Contribution to the Wet Granulation Process and to the Characteristics of Granules.

The different states of water incorporated in wet granules were studied by a low-field benchtop 1H-NMR time-domain NMR (TD-NMR) instrument. Wet granules consisting different fillers [cornstarch (CS), microcrystalline cellulose (MCC), and D-mannitol (MAN)] with different water contents were prepared using a high-speed granulator, and then their spin-spin relaxation time (T2) was measured using the NMR relaxation technique. The experimental T2 relaxation curves were analyzed by the two-component curve fitting, and then the individual T2 relaxation behaviors of solid and water in wet granules were identified. According to the observed T2 values, it was confirmed that the molecular mobility of water in CS and MCC granules was more restricted than that in the MAN granule. The state of water appeared to be associated with the drying efficiency and moisture absorption capacity of wet granules. Thus, it was confirmed that the state of water significantly affected the wet granulation process and the characteristics of the resultant granules. In the final phase of this study, the effects of binders on the molecular mobility of water in granulation fluids and wet granules were examined. The state of water in granulation fluids was substantially changed by changing the binders. The difference was still detected in wet granules prepared by addition of these fluids to the fillers. In conclusion, TD-NMR can offer valuable knowledge on wet granulation from the viewpoint of molecular mobility of water.

Assessment of chemical exchange in tryptophan–albumin solution through 19F multicomponent transverse relaxation dispersion analysis

A number of NMR methods possess the capability of probing chemical exchange dynamics in solution. However, certain drawbacks limit the applications of these NMR approaches, particularly, to a complex system. Here, we propose a procedure that integrates the regularized nonnegative least squares (NNLS) analysis of multiexponential T2 relaxation into Carr-Purcell-Meiboom-Gill (CPMG) relaxation dispersion experiments to probe chemical exchange in a multicompartmental system. The proposed procedure was validated through analysis of (19)F T2 relaxation data of 6-fluoro-DL-tryptophan in a two-compartment solution with and without bovine serum albumin. Given the regularized NNLS analysis of a T2 relaxation curve acquired, for example, at the CPMG frequency υ CPMG =125, the nature of two distinct peaks in the associated T2 distribution spectrum indicated 6-fluoro-DL-tryptophan either retaining the free state, with geometric mean */multiplicative standard deviation(MSD) =1851.2ms */1.51, or undergoing free/albumin-bound interconversion, with geometric mean */MSD=236.8ms */1.54, in the two-compartment system. Quantities of the individual tryptophan species were accurately reflected by the associated T2 peak areas, with an interconversion state-to-free state ratio of 0.45±0.11. Furthermore, the CPMG relaxation dispersion analysis estimated the exchange rate between the free and albumin-bound states in this fluorinated tryptophan analog and the corresponding dissociation constant of the fluorinated tryptophan-albumin complex in the chemical-exchanging, two-compartment system.

G.P.122: T2-spectrum: A novel NMR approach for the characterization of muscle disorders

Skeletal muscle MRI T2-mapping is known to detect oedematous/inflammatory/necrotic sites, which are all characterized by elevated global (monoexponential) water T2-values. Although revealing disease activity, the lack of specificity of this measurement precludes its application for the characterization of the specific pathophysiological mechanisms underlying disease progression. The T2 relaxation of 1 H NMR signals in skeletal muscle tissue has been long known to be multiexponential. This is currently agreed to reveal anatomical compartmentation of myowater. The spectroscopic CPMG sequence allows the acquisition of T2-relaxation curves with echo-time sampling and SNR significantly higher than MRI methods and offers the possibility for robust multiexponential analysis. CPMG data ( Nb of echoes =1000, inter-echo-spacing =1ms) were acquired from within the soleus of 8 healthy volunteers and 3 patients with muscular disorders. T2-spectra were extracted for each set of data using a regularized inversion method. T2-spectra observed in healthy volunteers systematically presented two T2-peaks centred at T2=32 and 132ms, with relative fractions of 93% and 7%, respectively. In patients, the observed T2-spectra were drastically altered; the peaks are shifted and closer to each other and in some cases coalescent. The results demonstrated the high sensitivity of T2-spectra to disease activity. In healthy subjects, prior knowledge about mean transmembrane water exchange rates allows a compartmental-exchange-analysis of the T2-spectra in order to characterize the different tissue compartments by their T2 and relative fraction. Although more exhaustive investigation is necessary to quantitatively interpret the alterations of T2-spectra in diseased tissue, such information shall give specificity to MRI T2 measurements by revealing physiological mechanisms underlying observed monoexponential T2 alterations.

Characterization of unsaturated porous media by high‐field and low‐field NMR relaxometry

A comparison study of nuclear magnetic resonance relaxometry at high and low magnetic field (7 and 0.1 T) has been initiated for investigating the influence of the magnetic field strength, variable clay content, and different degrees of saturation on the relaxometric properties of four ideal porous media. The samples consisted of medium sand with increasing fractions of kaolin clay ranging from 0 to 15%. Six different volumetric water contents between saturation and θ = 0.05 were used. Changes in water content of the samples were achieved by slow evaporation.T2relaxation curves were monitored by the Carr‐Purcell‐Meiboom‐Gill sequence and were further analyzed by inverse Laplace transformation, yieldingT2distribution functions. Sand shows a slight continuous shift with decreasing water content of a bimodal distribution function ofT2to faster relaxation at high and low magnetic field. Sand‐clay mixtures show broad, bimodal distribution functions for both magnetic field intensities which shift slightly with decreasing water content. Signal amplitude behavior with variation of saturation degree was also monitored. An expected proportionality of the total signal amplitude with water content was observed for all samples at 0.1 T, whereas at 7 T deviations occurred for samples with a clay content higher than 5%, which are assigned to loss of signal in the first echo periods. The relaxivity in unsaturated clay‐based porous media is mostly surface dominated, as the weak and comparable dependence of 1/T2onTEat both field strengths shows. Nevertheless, for a reliable determination of water content in mixed systems with varying texture and saturation the employment of multiecho sequences at low magnetic field strength are preferable.

Non-invasive spatial tissue discrimination in ancient mummies and bones in situ by portable nuclear magnetic resonance

Historic mummies and skeletons have been investigated extensively by modern diagnostic imaging using computed tomography. But magnetic resonance imaging (MRI) has never been applied successfully to mummies in a non-invasive way without tissue rehydration. The aim of this study is to show the feasibility and diagnostic impact of mobile MR technology to historic human tissues. The natural glacier mummy Iceman, a mummified recent human cadaver, historic mummified body parts, historic bones, and living volunteers have been analysed by non-invasive, single sided NMR with the NMR-MOUSE®. We acquired high-resolution depth profiles and T2 relaxation curves of the head region of the Iceman mummy in situ in the storage room at the Museum and of the cadaver in the hospital. A spatial differentiation of surface ice layer, cutis, and skull bone up to a depth of 5 mm was possible. In ancient Egyptian mummified specimens, the thickness of a fingernail and a differentiation of a single bandage layer versus the skin underneath were possible. A comparison of depth profiles through different foreheads of mummies, skulls, and living people gives strong evidence, that single-sided NMR with the NMR-MOUSE is a non-invasive technique to determine bone density. Our results demonstrate for the first time the feasibility of non-clinical MRI to visualize historic human tissues in a non-invasive approach.

Moisture measurement in cheese analogue using stretched and multi-exponential models of the magnetic resonance T2 relaxation curve.

The dairy industry would benefit from rapid and non-destructive determination of moisture content of cheese products. The two components primarily responsible for the low-field magnetic resonance (MR) spin-spin relaxation (T2) signal of cheese products are fat and the water bound to protein. If the moisture component of the signal can be distinguished from the fat component, it should be possible to measure moisture using an MR sensor. Therefore, a key aspect of the development of an MR moisture measurement method is examination of techniques for analysis of T2 relaxation curves. One common method of T2 analysis of complex foods, such as cheese, is to fit multi-term exponential models to the curves. An alternative approach is proposed which uses stretched exponential models. The single-term stretched exponential model has been used for porous rock systems and polymers, but not for foods. The T2 relaxation curves were analysed using both models and the results were compared. The number of unknowns in the three-term exponential and two-term stretched exponential models was reduced by assuming the relaxation curve of the fat component was the same as the relaxation curve of pure fat. In each model, one of the exponential terms described the behaviour of the water in the cheese analogue, while the remaining term or terms described the behaviour of the fat. For each model the T2 relaxation time associated with the water was well correlated with moisture content. Coefficients of determination of the relaxation time versus moisture from each of the two models were nearly identical. The advantages and disadvantages of the two models are discussed.