Clinical Proton MR Spectroscopy in Central Nervous System Disorders.

A large body of published work shows that proton (hydrogen 1 [(1)H]) magnetic resonance (MR) spectroscopy has evolved from a research tool into a clinical neuroimaging modality. Herein, the authors present a summary of brain disorders in which MR spectroscopy has an impact on patient management, together with a critical consideration of common data acquisition and processing procedures. The article documents the impact of (1)H MR spectroscopy in the clinical evaluation of disorders of the central nervous system. The clinical usefulness of (1)H MR spectroscopy has been established for brain neoplasms, neonatal and pediatric disorders (hypoxia-ischemia, inherited metabolic diseases, and traumatic brain injury), demyelinating disorders, and infectious brain lesions. The growing list of disorders for which (1)H MR spectroscopy may contribute to patient management extends to neurodegenerative diseases, epilepsy, and stroke. To facilitate expanded clinical acceptance and standardization of MR spectroscopy methodology, guidelines are provided for data acquisition and analysis, quality assessment, and interpretation. Finally, the authors offer recommendations to expedite the use of robust MR spectroscopy methodology in the clinical setting, including incorporation of technical advances on clinical units. © RSNA, 2014 Online supplemental material is available for this article.

Remote control of pulmonary blood flow: a dream comes true.

The indication for pulmonary artery banding is currently limited by several factors. Previous attempts have failed to produce adjustable pulmonary artery banding with reliable external regulation. An implantable, telemetrically controlled, battery-free device (FloWatch) developed by EndoArt SA, a medical company established in Lausanne, Switzerland, for externally adjustable pulmonary artery banding was evaluated on minipigs and proved to be effective for up to 6 months. The first human implant was performed on a girl with complete atrioventricular septal defect with unbalanced ventricles, large patent ductus arteriosus and pulmonary hypertension. At one month of age she underwent closure of the patent ductus arteriosus and FloWatch implantation around the pulmonary artery through conventional left thoracotomy. The surgical procedure was rapid and uneventful. During the entire postoperative period bedside adjustments (narrowing or release of pulmonary artery banding with echocardiographic assessment) were repeatedly required to maintain an adequate pressure gradient. The early clinical results demonstrated the clinical benefits of unlimited external telemetric adjustments. The next step will be a multi-centre clinical trial to confirm the early results and adapt therapeutic strategies to this promising technology.

Parallelizing remote sensing image geometric correction

Remote sensing spatial, spectral, and temporal resolutions of images, acquired over a reasonably sized image extent, result in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is very attractive for monitoring, management, and scienti c activities. With Moore's Law alive and well, more and more parallelism is introduced into all computing platforms, at all levels of integration and programming to achieve higher performance and energy e ciency. Being the geometric calibration process one of the most time consuming processes when using remote sensing images, the aim of this work is to accelerate this process by taking advantage of new computing architectures and technologies, specially focusing in exploiting computation over shared memory multi-threading hardware. A parallel implementation of the most time consuming process in the remote sensing geometric correction has been implemented using OpenMP directives. This work compares the performance of the original serial binary versus the parallelized implementation, using several multi-threaded modern CPU architectures, discussing about the approach to nd the optimum hardware for a cost-e ective execution.

Prediction of functional outcome 18 months after a first psychotic episode: a proton magnetic resonance spectroscopy study.

CONTEXT: Recent magnetic resonance imaging studies have attempted to relate volumetric brain measurements in early schizophrenia to clinical and functional outcome some years later. These studies have generally been negative, perhaps because gray and white matter volumes inaccurately assess the underlying dysfunction that might be predictive of outcome. OBJECTIVE: To investigate the predictive value of frontal and temporal spectroscopy measures for outcome in patients with first-episode psychoses. DESIGN: Left prefrontal cortex and left mediotemporal lobe voxels were assessed using proton magnetic resonance spectroscopy to provide the ratio of N-acetylaspartate (NAA) and choline-containing compounds to creatine and phosphocreatine (Cr) (NAA/Cr ratio). These data were used to predict outcome at 18 months after admission, as assessed by a systematic medical record audit. SETTING: Early psychosis clinic. PARTICIPANTS: Forty-six patients with first-episode psychosis. MAIN OUTCOME MEASURES: We used regression models that included age at imaging and duration of untreated psychosis to predict outcome scores on the Global Assessment of Functioning Scale, Clinical Global Impression scales, and Social and Occupational Functional Assessment Scale, as well as the number of admissions during the treatment period. We then further considered the contributions of premorbid function and baseline level of negative symptoms. RESULTS: The only spectroscopic predictor of outcome was the NAA/Cr ratio in the prefrontal cortex. Low scores on this variable were related to poorer outcome on all measures. In addition, the frontal NAA/Cr ratio explained 17% to 30% of the variance in outcome. CONCLUSIONS: Prefrontal neuronal dysfunction is an inconsistent feature of early psychosis; rather, it is an early marker of poor prognosis across the first years of illness. The extent to which this can be used to guide treatment and whether it predicts outcome some years after first presentation are questions for further research.

Use of NIR spectroscopy and multivariate process spectra calibration methodology for pharmaceutical solid samples analysis

Accomplish high quality of final products in pharmaceutical industry is a challenge that requires the control and supervision of all the manufacturing steps. This request created the necessity of developing fast and accurate analytical methods. Near infrared spectroscopy together with chemometrics, fulfill this growing demand. The high speed providing relevant information and the versatility of its application to different types of samples lead these combined techniques as one of the most appropriated. This study is focused on the development of a calibration model able to determine amounts of API from industrial granulates using NIR, chemometrics and process spectra methodology.

Active learning methods for remote sensing image classification

In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.

Profiling of counterfeit medicines by vibrational spectroscopy

Counterfeit pharmaceutical products have become a widespread problem in the last decade. Various analytical techniques have been applied to discriminate between genuine and counterfeit products. Among these, Near-infrared (NIR) and Raman spectroscopy provided promising results.The present study offers a methodology allowing to provide more valuable information fororganisations engaged in the fight against counterfeiting of medicines.A database was established by analyzing counterfeits of a particular pharmaceutical product using Near-infrared (NIR) and Raman spectroscopy. Unsupervised chemometric techniques (i.e. principal component analysis - PCA and hierarchical cluster analysis - HCA) were implemented to identify the classes within the datasets. Gas Chromatography coupled to Mass Spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FT-IR) were used to determine the number of different chemical profiles within the counterfeits. A comparison with the classes established by NIR and Raman spectroscopy allowed to evaluate the discriminating power provided by these techniques. Supervised classifiers (i.e. k-Nearest Neighbors, Partial Least Squares Discriminant Analysis, Probabilistic Neural Networks and Counterpropagation Artificial Neural Networks) were applied on the acquired NIR and Raman spectra and the results were compared to the ones provided by the unsupervised classifiers.The retained strategy for routine applications, founded on the classes identified by NIR and Raman spectroscopy, uses a classification algorithm based on distance measures and Receiver Operating Characteristics (ROC) curves. The model is able to compare the spectrum of a new counterfeit with that of previously analyzed products and to determine if a new specimen belongs to one of the existing classes, consequently allowing to establish a link with other counterfeits of the database.

A double-quadrature radiofrequency coil design for proton-decoupled carbon-13 magnetic resonance spectroscopy in humans at 7T

Purpose Carbon-13 magnetic resonance spectroscopy (13C-MRS) is challenging because of the inherent low sensitivity of 13C detection and the need for radiofrequency transmission at the 1H frequency while receiving the 13C signal, the latter requiring electrical decoupling of the 13C and 1H radiofrequency channels. In this study, we added traps to the 13C coil to construct a quadrature-13C/quadrature-1H surface coil, with sufficient isolation between channels to allow simultaneous operation at both frequencies without compromise in coil performance. Methods Isolation between channels was evaluated on the bench by measuring all coupling parameters. The quadrature mode of the quadrature-13C coil was assessed using in vitro 23Na gradient echo images. The signal-to-noise ratio (SNR) was measured on the glycogen and glucose resonances by 13C-MRS in vitro, compared with that obtained with a linear-13C/quadrature-1H coil, and validated by 13C-MRS in vivo in the human calf at 7T. Results Isolation between channels was better than â^'30 dB. The 23Na gradient echo images indicate a region where the field is strongly circularly polarized. The quadrature coil provided an SNR enhancement over a linear coil of 1.4, in vitro and in vivo. Conclusion It is feasible to construct a double-quadrature 13C-1H surface coil for proton decoupled sensitivity enhanced 13C-NMR spectroscopy in humans at 7T. Magn Reson Med, 2014. © 2014 Wiley Periodicals, Inc.

Chemical characterization and infrared spectroscopy of soil organic matter from two southern brazilian soils

Soil organic matter from the surface horizon of two Brazilian soils (a Latosol and a Chernosol), in bulk samples (in situ SOM) and in HF-treated samples (SOM), was characterized by elemental analyses, diffuse reflectance (DRIFT) and transmission Fourier transform infrared spectroscopy (T-FTIR). Humic acids (HA), fulvic acids (FA) and humin (HU) isolated from the SOM were characterized additionally by ultraviolet-visible spectroscopy (UV-VIS). After sample oxidation and alkaline treatment, the DRIFT technique proved to be more informative for the detection of "in situ SOM" and of residual organic matter than T-FTIR. The higher hydrophobicity index (HI) and H/C ratio obtained in the Chernosol samples indicate a stronger aliphatic character of the organic matter in this soil than the Latosol. In the latter, a pronounced HI decrease was observed after the removal of humic substances (HS). The weaker aliphatic character, the higher O/C ratio, and the T-FTIR spectrum obtained for the HU fraction in the Latosol suggest the occurrence of surface coordination of carboxylate ions. The Chernosol HU fraction was also oxygenated to a relatively high extent, but presented a stronger hydrophobic character in comparison with the Latosol HU. These differences in the chemical and functional group composition suggest a higher organic matter protection in the Latosol. After the HF treatment, decreases in the FA proportion and the A350/A550 ratio were observed. A possible loss of FA and condensation of organic molecules due to the highly acid medium should not be neglected.

Diffusion-weighted spectroscopy: a novel approach to determine macromolecule resonances in short-echo time 1H-MRS.

Quantification of short-echo time proton magnetic resonance spectroscopy results in >18 metabolite concentrations (neurochemical profile). Their quantification accuracy depends on the assessment of the contribution of macromolecule (MM) resonances, previously experimentally achieved by exploiting the several fold difference in T(1). To minimize effects of heterogeneities in metabolites T(1), the aim of the study was to assess MM signal contributions by combining inversion recovery (IR) and diffusion-weighted proton spectroscopy at high-magnetic field (14.1 T) and short echo time (= 8 msec) in the rat brain. IR combined with diffusion weighting experiments (with δ/Δ = 1.5/200 msec and b-value = 11.8 msec/μm(2)) showed that the metabolite nulled spectrum (inversion time = 740 msec) was affected by residuals attributed to creatine, inositol, taurine, choline, N-acetylaspartate as well as glutamine and glutamate. While the metabolite residuals were significantly attenuated by 50%, the MM signals were almost not affected (< 8%). The combination of metabolite-nulled IR spectra with diffusion weighting allows a specific characterization of MM resonances with minimal metabolite signal contributions and is expected to lead to a more precise quantification of the neurochemical profile.

1H-[13C] NMR spectroscopy of the rat brain during infusion of [2-13C] acetate at 14.1 T.

Full signal intensity (1)H-[(13)C] NMR spectroscopy, combining a preceding (13)C-editing block based on an inversion BISEP (B(1)-insensitive spectral editing pulse) with a spin-echo coherence-based localization, was developed and implemented at 14.1 T. (13)C editing of the proposed scheme was achieved by turning on and off the (13)C adiabatic full passage in the (13)C-editing block to prepare inverted and noninverted (13)C-coupled (1)H coherences along the longitudinal axis prior to localization. The novel (1)H-[(13)C] NMR approach was applied in vivo under infusion of the glia-specific substrate [2-(13)C] acetate. Besides a approximately 50% improvement in sensitivity, spectral dispersion was enhanced at 14.1 T, especially for J-coupled metabolites such as glutamate and glutamine. A more distinct spectral structure at 1.9-2.2 ppm(parts per million) was observed, e.g., glutamate C3 showed a doublet pattern in both simulated (1)H spectrum and in vivo (13)C-edited (1)H NMR spectra. Besides (13)C time courses of glutamate C4 and glutamine C4, the time courses of glutamate C3 and glutamine C3 obtained by (1)H-[(13)C] NMR spectroscopy were reported for the first time. Such capability should greatly improve the ability to study neuron-glial metabolism using (1)H-observed (13)C-edited NMR spectroscopy.

Semi-supervised remote sensing image classification with cluster kernels

A semisupervised support vector machine is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictions.