Single‐ and double‐Diffusion encoding MRI for studying ex vivo apparent axon diameter distribution in spinal cord white matter

Mapping average axon diameter (AAD) and axon diameter distribution (ADD) in neuronal tissues non‐invasively is a challenging task that may have a tremendous effect on our understanding of the normal and diseased central nervous system (CNS). Water diffusion is used to probe microstructure in neuronal tissues, however, the different water populations and barriers that are present in these tissues turn this into a complex task. Therefore, it is not surprising that recently we have witnessed a burst in the development of new approaches and models that attempt to obtain, non‐invasively, detailed microstructural information in the CNS. In this work, we aim at challenging and comparing the microstructural information obtained from single diffusion encoding (SDE) with double diffusion encoding (DDE) MRI. We first applied SDE and DDE MR spectroscopy (MRS) on microcapillary phantoms and then applied SDE and DDE MRI on an ex vivo porcine spinal cord (SC), using similar experimental conditions. The obtained diffusion MRI data were fitted by the same theoretical model, assuming that the signal in every voxel can be approximated as the superposition of a Gaussian‐diffusing component and a series of restricted components having infinite cylindrical geometries. The diffusion MRI results were then compared with histological findings. We found a good agreement between the fittings and the experimental data in white matter (WM) voxels of the SC in both diffusion MRI methods. The microstructural information and apparent AADs extracted from SDE MRI were found to be similar or somewhat larger than those extracted from DDE MRI especially when the diffusion time was set to 40 ms. The apparent ADDs extracted from SDE and DDE MRI show reasonable agreement but somewhat weaker correspondence was observed between the diffusion MRI results and histology. The apparent subtle differences between the microstructural information obtained from SDE and DDE MRI are briefly discussed.     

Attempted Determination of the Structures of Complex Aliphatic Copolyesters

Attempts at the microstructural characterization of biodegradable aliphatic polyesters made via melt‐polymerization of three distinct diacids or diesters and three structurally distinct diols are described. Probes sensitive to local, short‐range microstructures (¹³C‐NMR) and potentially sensitive to overall, global, complete polymer architectures (Kerr effect) are employed. Solution ¹³C‐NMR reveals the copolyesters possess largely random comonomer sequences, without, however, complete elucidation of even their shortest comonomer sequences. The limited structural sensitivity of NMR prevents development of meaningful structure–property relations. Observed Kerr effects, on the other hand, clearly demonstrate sensitivity to the presence of distinct microstructures of longer range than are observed by ¹³C‐NMR. To identify the details of these longer‐range structural elements, will, however, require the ability to estimate the Kerr constants of these aliphatic copolyesters when they are assumed not only to possess the types and amounts of short‐range microstructures identified experimentally by ¹³C‐NMR, but which are located at different positions along their chains. This ambitious goal requires extensive experimental and calculational efforts, which are currently just beginning and will be described subsequently.     

1H PFG‐NMR Diffusion Study on a Sequence‐Defined Macromolecule: Confirming Monodispersity

A monodisperse decamer bearing ten different side chains, obtained via the iterative Passerini three‐component reaction and subsequent deprotection, is investigated by ¹H pulsed field gradient (PFG) NMR. Both the stimulated echo (PFG‐STE) and a fast spin‐echo pulse sequence (β‐PFG‐SE) are applied to explore the translational diffusion properties of the sequence‐defined decamer in solution at different concentrations with the aim of establishing an independent and new method to confirm its monodispersity. The signals decay according to the expectation of monodisperse molecules in solution with a high experimental accuracy, indeed verifying the monodispersity of these decamers. The diffusion coefficients can further be interpreted in terms of molecular weight. Both NMR methods result in comparable diffusion coefficients, while the β‐PFG‐SE reduces the experimental time by a factor of about 18.     

A metabolomic data fusion approach to support gliomas grading

Magnetic resonance imaging (MRI) is the current gold standard for the diagnosis of brain tumors. However, despite the development of MRI techniques, the differential diagnosis of central nervous system (CNS) primary pathologies, such as lymphoma and glioblastoma or tumor‐like brain lesions and glioma, is often challenging. MRI can be supported by in vivo magnetic resonance spectroscopy (MRS) to enhance its diagnostic power and multiproject‐multicenter evaluations of classification of brain tumors have shown that an accuracy around 90% can be achieved for most of the pairwise discrimination problems. However, the survival rate for patients affected by gliomas is still low. The High‐Resolution Magic‐Angle‐Spinning Nuclear Magnetic Resonance (HR‐MAS NMR) metabolomics studies may be helpful for the discrimination of gliomas grades and the development of new strategies for clinical intervention. Here, we propose to use T₂‐filtered, diffusion‐filtered and conventional water‐presaturated spectra to try to extract as much information as possible, fusing the data gathered by these different NMR experiments and applying a chemometric approach based on Multivariate Curve Resolution (MCR). Biomarkers important for glioma's discrimination were found. In particular, we focused our attention on cystathionine (Cyst) that shows promise as a biomarker for the better prognosis of glioma tumors.     

Multiple‐echo diffusion tensor acquisition technique (MEDITATE) on a 3T clinical scanner

This article describes the concepts and implementation of an MRI method, the multiple‐echo diffusion tensor acquisition technique (MEDITATE), which is capable of acquiring apparent diffusion tensor maps in two scans on a 3T clinical scanner. In each MEDITATE scan, a set of RF pulses generates multiple echoes, the amplitudes of which are diffusion weighted in both magnitude and direction by a pattern of diffusion gradients. As a result, two scans acquired with different diffusion weighting strengths suffice for accurate estimation of diffusion tensor imaging (DTI) parameters. The MEDITATE variation presented here expands previous MEDITATE approaches to adapt to the clinical scanner platform, such as exploiting longitudinal magnetization storage to reduce T₂ weighting. Fully segmented multi‐shot Cartesian encoding is used for image encoding. MEDITATE was tested on isotropic (agar gel), anisotropic diffusion phantoms (asparagus) and in vivo skeletal muscle in healthy volunteers with cardiac gating. Comparisons of accuracy were performed with standard twice‐refocused spin echo (TRSE) DTI in each case and good quantitative agreement was found between diffusion eigenvalues, mean diffusivity and fractional anisotropy derived from TRSE DTI and from the MEDITATE sequence. Orientation patterns were correctly reproduced in both isotropic and anisotropic phantoms, and approximately for in vivo imaging. This illustrates that the MEDITATE method of compressed diffusion encoding is feasible on the clinical scanner platform. With future development and employment of appropriate view‐sharing image encoding, this technique may be used in clinical applications requiring time‐sensitive acquisition of DTI parameters such as dynamical DTI in muscle. Copyright © 2013 John Wiley & Sons, Ltd.     

Validation of a new multiple osteochondromas classification through Switching Neural Networks

Multiple osteochondromas (MO), previously known as hereditary multiple exostoses (HME), is an autosomal dominant disease characterized by the formation of several benign cartilage‐capped bone growth defined osteochondromas or exostoses. Various clinical classifications have been proposed but a consensus has not been reached. The aim of this study was to validate (using a machine learning approach) an “easy to use” tool to characterize MO patients in three classes according to the number of bone segments affected, the presence of skeletal deformities and/or functional limitations. The proposed classification has been validated (with a highly satisfactory mean accuracy) by analyzing 150 different variables on 289 MO patients through a Switching Neural Network approach (a novel classification technique capable of deriving models described by intelligible rules in if‐then form). This approach allowed us to identify ankle valgism, Madelung deformity and limitation of the hip extra‐rotation as “tags” of the three clinical classes. In conclusion, the proposed classification provides an efficient system to characterize this rare disease and is able to define homogeneous cohorts of patients to investigate MO pathogenesis. © 2013 Wiley Periodicals, Inc.     

Diffusion in Polymer Solutions: Molecular Weight Distribution by PFG‐NMR and Relation to SEC

Quantification of diffusion coefficient distribution (DCD) and correlation with molecular weight distribution (MWD) of polymers is still an issue in pulsed field‐gradient nuclear magnetic resonance (PFG‐NMR). The conventional scaling law utilized so far to relate diffusion coefficient and molecular weight only holds true for the determination of MWD at sufficiently low concentrations. To extend measurement limits and to get a good signal‐to‐noise ratio, an exponential correlation is introduced to describe the effect of polymer concentration on diffusion in PFG‐NMR. Two model polymers (polystyrene and poly(methyl methacrylate)) dissolved in deuterated chloroform are studied at different concentrations in the range of 0.16–8 wt%. The DCDs are determined by modeling the measured signal attenuation with three methods (gamma distribution, log normal distribution, and tailored norm regularization). It is shown that the proposed method applies to the PFG‐NMR measurements on polymer solutions over a wide concentration range, providing almost the same MWDs as those obtained at low concentrations. The MWDs retrieved from NMR experiments agree well with those by size exclusion chromatography.

     

Solid‐State NMR Characterization of the Multiphase Structure of Polypropylene In‐reactor Alloy

A variety of solid‐state NMR techniques were used to characterize the chain dynamics, miscibility and the micro‐phase structure of a polypropylene (PP) in‐reactor alloy system. The alloy was physically separated into three fractions, and the molecular dynamics and relaxation behavior of the pure fractions was then compared with the components in the alloy to achieve comprehensive understanding of the phase structure of the PP in‐reactor alloy. The miscibility among different components of the alloy was studied by the rotational frame spin‐lattice relaxation time. Proton spin‐diffusion methods were used to quantify the domain thicknesses of different regions in the alloy. The results show that the alloy is composed of three phases, namely, a homo‐polyethylene (HPE) matrix, a homo‐polypropylene (HPP) dispersed phase, and a linear low‐density polyethylene (LLDPE) interphase. The thickness of the LLDPE interphase is estimated to be 7.7 nm at room temperature, and changes dramatically with temperature. Finally, based on all the solid‐state NMR results, a model for the micro‐phase‐structure of the PP in‐reactor alloy is proposed, and a correlation between the micro‐phase structure and the excellent mechanical property is established.

     

Dynamics of Sodium Ions and Water in Swollen Superabsorbent Hydrogels as Studied by 23Na‐ and 1H‐NMR

Dynamics of sodium ions and water in swollen superabsorbent polymer (SAP) hydrogels are studied by ²³Na‐ and ¹H‐NMR, respectively. The apparent diffusion coefficients of water in swollen SAPs, probed by ¹H pulsed field gradient NMR, decreases with increasing diffusion time. The degree of hindrance depends on structural and synthesis parameters. It is quantified within a tortuosity model. Based on the results, the swelling degree has the highest impact on the ion mobility, apart from synthesis parameters leading to different levels of physical and chemical crosslinks. ²³Na‐NMR relaxation and diffusion reveal the ²³Na⁺ mobility in swollen SAPs. A higher degree of neutralization leads to faster relaxation and to a smaller apparent diffusion coefficient. Surface crosslinking restricts water mobility, but has a smaller impact on the dynamics of sodium ions. The experimental results indicate an influence of SAP structure on the dynamics of ions and water molecules.     

Metabolite profiling of fecal water extracts from human colorectal cancer

Colorectal cancer is the second leading cause of cancer death in developed countries. There is a need for better preventive strategies to improve the outcome of this disease. The increasing availability of high‐throughput methodologies opens up new possibilities for screening new markers. The application of NMR metabolic profiling to fecal water extracts has interesting potential as a diagnostic tool for detecting colorectal cancer. We obtained NMR metabolic profiles of fecal water extracts from patients with colorectal cancer and healthy individuals, to characterize possible differences between them and to identify potential diagnostic markers. Our results show that metabolic profiling of fecal water extracts is a cheap, reproducible and effective method for detecting colorectal cancer markers and therefore complements other stool‐screening methods. A low concentration of short‐chain fatty acids, such as acetate and butyrate, previously associated with the development of colorectal cancer, appears to be the most effective marker. Concentrations of proline and cysteine, which are major components of most colonic epithelium mucus glycoproteins, also display significant changes in samples from colorectal cancer. Differentiation between fecal water extracts from controls and patients with colorectal cancer by NMR spectroscopy combined with chemometric techniques opens up new possibilities for developing new, efficient, high‐throughput screening protocols. Copyright © 2008 John Wiley & Sons, Ltd.     

Online Coupling of Size‐Exclusion Chromatography and Low‐Field 1H NMR Spectroscopy

Current results and limitations of a novel SEC–MR–NMR (size‐exclusion chromatography–medium resolution nuclear magnetic resonance) system are presented, delivering a direct correlation of chemical structure and molecular weight. A 20 MHz ¹H NMR spectrometer based on permanent magnets was combined with a SEC setup. The NMR sensitivity was improved by digital filters, shims, and a self‐built flow probe when compared with time‐domain instruments. For optimum NMR signal‐to‐noise ratio (S/N) of polymer fractions and concomitantly reduced S/N of solvent signals, deuterated solvents or protonated solvents, with solvent suppression, were applied. Results are presented after optimization of both NMR and chromatographic components such as column dimension and volume flow.     

Standing out from the crowd: How to identify plasma cells

Being the sole source of antibody, plasmablasts and plasma cells are essential for protective immunity. Due to their relative rarity, heterogeneity and the loss of many canonical B‐cell markers, antibody‐secreting cells (ASCs) have often been problematic to identify and further characterize. In the mouse, the combination of the expression of CD138 and BLIMP‐1, has led to many insights into ASC biology, although this approach requires the use of a GFP reporter strain. In the current issue of the European Journal of Immunology, two independent studies by Wilmore et al. and Pracht et al. provide alternative approaches to identify all murine ASCs using antibodies against the cell surface proteins, Sca‐1 and TACI, respectively. Here we will discuss the advantages of these new approaches to identify ASCs in the context of our emerging knowledge of the cell surface phenotype and gene expression program of various ASC subsets in the murine and human systems.     

Fast multidimensional NMR spectroscopy for sparse spectra

Multidimensional NMR spectroscopy is widely used for studies of molecular and biomolecular structure. A major disadvantage of multidimensional NMR is the long acquisition time which, regardless of sensitivity considerations, may be needed to obtain the final multidimensional frequency domain coefficients. In this article, a method for under‐sampling multidimensional NMR acquisition of sparse spectra is presented. The approach is presented in the case of two‐dimensional NMR acquisitions. It relies on prior knowledge about the support in the two‐dimensional frequency domain to recover an over‐determined system from the under‐determined system induced in the linear acquisition model when under‐sampled acquisitions are performed. This over‐determined system can then be solved with linear least squares. The prior knowledge is obtained efficiently at a low cost from the one‐dimensional NMR acquisition, which is generally acquired as a first step in multidimensional NMR. If this one‐dimensional acquisition is intrinsically sparse, it is possible to reconstruct the corresponding two‐dimensional acquisition from far fewer observations than those imposed by the Nyquist criterion, and subsequently to reduce the acquisition time. Further improvements are obtained by optimizing the sampling procedure for the least‐squares reconstruction using the sequential backward selection algorithm. Theoretical and experimental results are given in the case of a traditional acquisition scheme, which demonstrate reliable and fast reconstructions with acceleration factors in the range 3–6. The proposed method outperforms the CS methods (OMP, L1) in terms of the reconstruction performance, implementation and computation time. The approach can be easily extended to higher dimensions and spectroscopic imaging. Copyright © 2014 John Wiley & Sons, Ltd.     

Topological Insight into Superabsorbent Hydrogel Network Structures: a 1H Double‐Quantum NMR Study

Superabsorbent polymer (SAP) hydrogels have pronounced water‐absorbing and water‐storing capacities, which are essential for numerous potential applications. It remains a challenge to better understand the network topology because of their amorphous and anisotropic structures. Synthesis parameters such as monomer concentration, degree of neutralization and crosslinking, and surface crosslinking are varied to correlate structural changes in the network with low‐field proton double‐quantum (¹H DQ) NMR results. ¹H DQ‐NMR data are processed by a reliable, user‐independent analysis approach to determine the fractions of network defects, of mobile sol components, and of network chains as well as the residual dipolar coupling distribution in SAPs. In addition, results obtained by applying different distributions to describe the DQ buildup curves are quantified and compared. The correlation between topological and synthesis parameters as well as the impact of temperature, swelling, and solvent of SAP on DQ signals is investigated and discussed.     

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.     

A metabonomic approach to early prognostic evaluation of experimental sepsis by 1H NMR and pattern recognition

This study proposes an NMR‐based metabonomic approach to early prognostic evaluation of sepsis. Forty septic rats receiving cecal ligation and puncture (CLP) were divided into the surviving group and nonsurviving group on day 6, while 20 sham‐operated rats served as the control group. Serum samples were collected from septic and sham‐operated rats at 12 h after surgery and analyzed using ¹H NMR spectroscopy. Orthogonal partial least squares (OPLS) were applied and showed clustering according to predefined groups, indicating that NMR‐based metabolic profiling could reveal pathologic characteristics in the serum of sham‐operated, surviving, and nonsurviving septic rats. In addition, six characteristic metabolites including lactate, alanine, acetate, acetoacetate, hydroxybutyrate, and formate, which are mainly involved in energy metabolism, changed markedly in septic rats, especially in the nonsurvivors. Using these metabolites, a predictive model for prognostic evaluation of sepsis was constructed using a radial basis function neural network (RBFNN) with a prediction accuracy of about 87% by test samples. The results indicated that the NMR‐based metabonomic approach is a potential technique for the early prognostic evaluation of sepsis. Copyright © 2009 John Wiley & Sons, Ltd.     

Magnetic resonance imaging of time‐varying magnetic fields from therapeutic devices

While magnetic resonance imaging of static magnetic fields generated by external probes has been previously demonstrated, there is an unmet need to image time‐varying magnetic fields such as those generated by transcranial magnetic stimulators and radiofrequency hyperthermia probes. A method to image such time‐varying magnetic fields is introduced in this study. This article presents the theory behind the method and provides proof of concept by imaging time‐varying magnetic fields generated by a figure‐eight coil inside simple phantoms over a range of frequencies and intensities using a 7T small animal MRI scanner. The method was able to reconstruct the three‐dimensional components of the oscillating magnetic field vector. Copyright © 2013 John Wiley & Sons, Ltd.     

Morphology and Phase Characteristics of High‐Density Polyethylene Probed by NMR Spin Diffusion and Second Moment Analysis

Summary: A dipolar filter pulse sequence combined with cross‐polarization‐MAS is applied to characterize the phase distribution, morphology, and spin diffusion within a high‐density polyethylene sample. A new method to obtain quantitative ¹³C NMR by combining cross‐polarization‐MAS and spin diffusion NMR is presented. The derived crystallinity is consistent with the corresponding crystallinity obtained by ¹H NMR.

Illustration of the pulse sequence(s) applied in the present work.     

VERDICT‐AMICO: Ultrafast fitting algorithm for non‐invasive prostate microstructure characterization

VERDICT (vascular, extracellular and restricted diffusion for cytometry in tumours) estimates and maps microstructural features of cancerous tissue non‐invasively using diffusion MRI. The main purpose of this study is to address the high computational time of microstructural model fitting for prostate diagnosis, while retaining utility in terms of tumour conspicuity and repeatability. In this work, we adapt the accelerated microstructure imaging via convex optimization (AMICO) framework to linearize the estimation of VERDICT parameters for the prostate gland. We compare the original non‐linear fitting of VERDICT with the linear fitting, quantifying accuracy with synthetic data, and computational time and reliability (performance and precision) in eight patients. We also assess the repeatability (scan‐rescan) of the parameters. Comparison of the original VERDICT fitting versus VERDICT‐AMICO showed that the linearized fitting (1) is more accurate in simulation for a signal‐to‐noise ratio of 20 dB; (2) reduces the processing time by three orders of magnitude, from 6.55 seconds/voxel to 1.78 milliseconds/voxel; (3) estimates parameters more precisely; (4) produces similar parametric maps and (5) produces similar estimated parameters with a high Pearson correlation between implementations, r² > 0.7. The VERDICT‐AMICO estimates also show high levels of repeatability. Finally, we demonstrate that VERDICT‐AMICO can estimate an extra diffusivity parameter without losing tumour conspicuity and retains the fitting advantages. VERDICT‐AMICO provides microstructural maps for prostate cancer characterization in seconds.     

Metabolic profile of intact tissue from uterine leiomyomas using high‐resolution magic‐angle‐spinning 1H NMR spectroscopy

High‐resolution magic‐angle‐spinning (HRMAS) one‐ and two‐dimensional ¹H and ¹³C NMR spectroscopy was used to study intact healthy (myometrium) and benign (leiomyoma) uterine tissues of 10 patients. Twenty‐eight metabolites were detected and assigned in both types of tissue. Principal component analysis (PCA) was applied to a conventional water‐suppressed ¹H HRMAS NMR spectrum, and two NMR spectral editing methods, namely Carr–Purcell– Meiboom–Gill (CPMG) spin‐echo and diffusion‐edited techniques, were used. Only the PCA of CPMG spectra resulted in a good differentiation between the two tissue types. The CPMG spin‐echo spectra were also useful in indicating depleted levels of taurine in uterine leiomyomas, which were well correlated with the histopathological determination. In addition, statistical analysis revealed that most leiomyomas contained elevated concentrations of glutamate and glutamine. Our results suggest that HRMAS represents a valuable adjunct to histopathology to improve the diagnostic accuracy of uterine leiomyomas, whilst concomitantly reducing the diagnosis time. Copyright © 2010 John Wiley & Sons, Ltd.