Localized in vivo hyperpolarization transfer sequences.

In vivo localized and fully adiabatic homonuclear and heteronuclear polarization transfer experiments were designed and performed in the rat brain at 9.4 T after infusion of hyperpolarized sodium [1,2-(13)C(2)] and sodium [1-(13)C] acetate. The method presented herein leads to highly enhanced in vivo detection of short-T(1) (13)C as well as attached protons. This indirect detection scheme allows for probing additional molecular sites in hyperpolarized substrates and their metabolites and can thus lead to improved spectral resolution such as in the case of (13)C-acetate metabolism.

Primary localized rectal/pararectal gastrointestinal stromal tumors: results of surgical and multimodal therapy from the French Sarcoma group.

BACKGROUND: Rectal and pararectal gastrointestinal stromal tumors (GISTs) are rare. The optimal management strategy for primary localized GISTs remains poorly defined. METHODS: We conducted a retrospective analysis of 41 patients with localized rectal or pararectal GISTs treated between 1991 and 2011 in 13 French Sarcoma Group centers. RESULTS: Of 12 patients who received preoperative imatinib therapy for a median duration of 7 (2-12) months, 8 experienced a partial response, 3 had stable disease, and 1 had a complete response. Thirty and 11 patients underwent function-sparing conservative surgery and abdominoperineal resection, respectively. Tumor resections were mostly R0 and R1 in 35 patients. Tumor rupture occurred in 12 patients. Eleven patients received postoperative imatinib with a median follow-up of 59 (2.4-186) months. The median time to disease relapse was 36 (9.8-62) months. The 5-year overall survival rate was 86.5%. Twenty patients developed local recurrence after surgery alone, two developed recurrence after resection combined with preoperative and/or postoperative imatinib, and eight developed metastases. In univariate analysis, the mitotic index (≤5) and tumor size (≤5 cm) were associated with a significantly decreased risk of local relapse. Perioperative imatinib was associated with a significantly reduced risk of overall relapse and local relapse. CONCLUSIONS: Perioperative imatinib therapy was associated with improved disease-free survival. Preoperative imatinib was effective. Tumor shrinkage has a clear benefit for local excision in terms of feasibility and function preservation. Given the complexity of rectal GISTs, referral of patients with this rare disease to expert centers to undergo a multidisciplinary approach is recommended.

Quality assurance in the 22991 EORTC ROG trial in localized prostate cancer: dummy run and individual case review.

INTRODUCTION: EORTC trial 22991 was designed to evaluate the addition of concomitant and adjuvant short-term hormonal treatments to curative radiotherapy in terms of disease-free survival for patients with intermediate risk localized prostate cancer. In order to assess the compliance to the 3D conformal radiotherapy protocol guidelines, all participating centres were requested to participate in a dummy run procedure. An individual case review was performed for the largest recruiting centres as well. MATERIALS AND METHODS: CT-data of an eligible prostate cancer patient were sent to 30 centres including a description of the clinical case. The investigator was requested to delineate the volumes of interest and to perform treatment planning according to the protocol. Thereafter, the investigators of the 12 most actively recruiting centres were requested to provide data on five randomly selected patients for an individual case review. RESULTS: Volume delineation varied significantly between investigators. Dose constraints for organs at risk (rectum, bladder, hips) were difficult to meet. In the individual case review, no major protocol deviations were observed, but a number of dose reporting problems were documented for centres using IMRT. CONCLUSIONS: Overall, results of this quality assurance program were satisfactory. The efficacy of the combination of a dummy run procedure with an individual case review is confirmed in this study, as none of the evaluated patient files harboured a major protocol deviation. Quality assurance remains a very important tool in radiotherapy to increase the reliability of the trial results. Special attention should be given when designing quality assurance programs for more complex irradiation techniques.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

MR spectroscopy of the human brain with enhanced signal intensity at ultrashort echo times on a clinical platform at 3T and 7T.

Recently, the spin-echo full-intensity acquired localized (SPECIAL) spectroscopy technique was proposed to unite the advantages of short TEs on the order of milliseconds (ms) with full sensitivity and applied to in vivo rat brain. In the present study, SPECIAL was adapted and optimized for use on a clinical platform at 3T and 7T by combining interleaved water suppression (WS) and outer volume saturation (OVS), optimized sequence timing, and improved shimming using FASTMAP. High-quality single voxel spectra of human brain were acquired at TEs below or equal to 6 ms on a clinical 3T and 7T system for six volunteers. Narrow linewidths (6.6 +/- 0.6 Hz at 3T and 12.1 +/- 1.0 Hz at 7T for water) and the high signal-to-noise ratio (SNR) of the artifact-free spectra enabled the quantification of a neurochemical profile consisting of 18 metabolites with Cramér-Rao lower bounds (CRLBs) below 20% at both field strengths. The enhanced sensitivity and increased spectral resolution at 7T compared to 3T allowed a two-fold reduction in scan time, an increased precision of quantification for 12 metabolites, and the additional quantification of lactate with CRLB below 20%. Improved sensitivity at 7T was also demonstrated by a 1.7-fold increase in average SNR (= peak height/root mean square [RMS]-of-noise) per unit-time.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Simultaneous B(0)- and B(1)+-map acquisition for fast localized shim, frequency, and RF power determination in the heart at 3 T.

High-field (>or=3 T) cardiac MRI is challenged by inhomogeneities of both the static magnetic field (B(0)) and the transmit radiofrequency field (B(1)+). The inhomogeneous B fields not only demand improved shimming methods but also impede the correct determination of the zero-order terms, i.e., the local resonance frequency f(0) and the radiofrequency power to generate the intended local B(1)+ field. In this work, dual echo time B(0)-map and dual flip angle B(1)+-map acquisition methods are combined to acquire multislice B(0)- and B(1)+-maps simultaneously covering the entire heart in a single breath hold of 18 heartbeats. A previously proposed excitation pulse shape dependent slice profile correction is tested and applied to reduce systematic errors of the multislice B(1)+-map. Localized higher-order shim correction values including the zero-order terms for frequency f(0) and radiofrequency power can be determined based on the acquired B(0)- and B(1)+-maps. This method has been tested in 7 healthy adult human subjects at 3 T and improved the B(0) field homogeneity (standard deviation) from 60 Hz to 35 Hz and the average B(1)+ field from 77% to 100% of the desired B(1)+ field when compared to more commonly used preparation methods.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Identification of early metabolic markers of various degrees of ischemia in the mouse brain by localized H-1 magnetic resonance spectroscopy

Objectives: Magnetic resonance (MR) imaging and spectroscopy (MRS) allow the establishment of the anatomical evolution and neurochemical profiles of ischemic lesions. The aim of the present study was to identify markers of reversible and irreversible damage by comparing the effects of 10-mins middle cerebral artery occlusion (MCAO), mimicking a transient ischemic attack, with the effects of 30-mins MCAO, inducing a striatal lesion. Methods: ICR-CD1 mice were subjected to 10-mins (n = 11) or 30-mins (n = 9) endoluminal MCAO by filament technique at 0 h. The regional cerebral blood flow (CBF) was monitored in all animals by laser- Doppler flowmetry with a flexible probe fixed on the skull with < 20% of baseline CBF during ischemia and > 70% during reperfusion. All MR studies were carried out in a horizontal 14.1T magnet. Fast spin echo images with T2-weighted parameters were acquired to localize the volume of interest and evaluate the lesion size. Immediately after adjustment of field inhomogeneities, localized 1H MRS was applied to obtain the neurochemical profile from the striatum (6 to 8 microliters). Six animals (sham group) underwent nearly identical procedures without MCAO. Results: The 10-mins MCAO induced no MR- or histologically detectable lesion in most of the mice and a small lesion in some of them. We thus had two groups with the same duration of ischemia but a different outcome, which could be compared to sham-operated mice and more severe ischemic mice (30-mins MCAO). Lactate increase, a hallmark of ischemic insult, was only detected significantly after 30-mins MCAO, whereas at 3 h post ischemia, glutamine was increased in all ischemic mice independently of duration and outcome. In contrast, glutamate, and even more so, N-acetyl-aspartate, decreased only in those mice exhibiting visible lesions on T2-weighted images at 24 h. Conclusions: These results suggest that an increased glutamine/glutamate ratio is a sensitive marker indicating the presence of an excitotoxic insult. Glutamate and NAA, on the other hand, appear to predict permanent neuronal damage. In conclusion, as early as 3 h post ischemia, it is possible to identify early metabolic markers manifesting the presence of a mild ischemic insult as well as the lesion outcome at 24 h.