Analgesic efficacy of intravenous perfusion of lidocaine, ketamine or a combination after laparotomy in a placebo-controlled, randomized, double-blind prospective study

In acute postoperative pain management intravenous lidocaine and/or ketamine have been advocated because of their morphine-sparing effect. The goal of this prospective, randomised, double-blind study was to assess morphine consumption with different regimens of intravenous infusion of lidocaine, ketamine or both during 48 hours following laparotomy. Patients were randomised into four groups. Group L, K, and KL received intravenous lidocaine, ketamine or a combination, respectively, before incision and during 48 hours postoperatively. The control group (C) received a similar volume of saline bolus and infusion. Postoperative analgesia included morphine delivered by a patient-controlled analgesia device. Primary outcome was the cumulative morphine consumption and pain, sedation scores, pressure algometry and side effects were our secondary outcomes. Cognition and psychomotor performance were also tested. Out of 57 eligible patients, 44 completed the study. Lidocaine reduced the cumulative morphine consumption compared with the control group (mean 0.456 mg.kg-1 +/- 0.244 (SD) versus 0.705 +/- 0.442, respectively, Ρ < 0.001). Pain scores during movement were statistically lower in all three treatment groups. Psychometric tests showed that the lidocaine group expressed more depressed feelings and sadness compared to the control group. Lidocaine administration had a morphine-sparing effect with a 36% reduction of morphine consumption while ketamine alone or combined with lidocaine did not. As a whole, our results suggest that intravenous lidocaine may offer advantages for postoperative analgesia. We propose lidocaine as a new alternative for pain control that needs to be studied further in future multicentric studies.

The use of contrast-enhanced MRI angiography for planning interventional catheterization

Background: Interventional catheterization is being increasingly used for relief of residual lesions in congenital heart disease. Exact anatomical imaging is crucial in the planning of an intervention. This can be provided non-invasively and without radiation by contrast-enhanced MR angiography (CEMRA). Aim: To evaluate the accuracy of the measurements of the vessels obtained by CEMRA in comparison to those obtained by conventional X-ray angiography (CXA). Methods: Retrospective blinded measurement of the diameters of aorta and pulmonary arteries on the CEMRA and CXA images, in the same locations. Comparison of the results by Pearson correlation and by calculating the limits of agreement. Results: Twenty-one children with congenital heart disease, mean age 5.6 +- 5.2 years, weight 21.1 +- 18.4 kg, underwent CEMRA and catheterization for assessment or treatment of a residual lesion. The time interval between the CEMRA and the CXA examination was 2.6 +- 2.3 months. A total of 98 measurements, 37 of the aorta and 61 of the pulmonary arteries were performed on the images obtained by each technique. The correlation between CEMRA and CXA measurements was excellent, r = 0.97, p < 0.0001. The mean difference between the two techniques was 0.018 +- 1.1mm; the limits of agreement were -2.14 and +2.18mm. Similar agreement was found for measures of the aorta (r +- 0.97, mean difference 0.20 = 1.08 mm) and of the pulmonary arteries (r +- 0.97, mean difference 0.048 = 0.89 mm). Conclusions: CEMRA provide accurate quantitative anatomical information, which highly agrees with CXA data, and can therefore be used for planning interventional catheterization.

Assessment of a new MRI classification of spinal stenosis based on cross-sectional morphology of the dural sac : FM2

Introduction: Quantitative measures of degree of lumbar spinal stenosis (LSS) such as antero-posterior diameter of the canal or dural sac cross sectional area vary widely and do not correlate with clinical symptoms or results of surgical decompression. In an effort to improve quantification of stenosis we have developed a grading system based on the morphology of the dural sac and its contents as seen on T2 axial images. The grading comprises seven categories ranging form normal to the most severe stenosis and takes into account the ratio of rootlet/CSF content. Material and methods: Fifty T2 axial MRI images taken at disc level from twenty seven symptomatic lumbar spinal stenosis patients who underwent decompressive surgery were classified into seven categories by five observers and reclassified 2 weeks later by the same investigators. Intra- and inter-observer reliability of the classification were assessed using Cohen's and Fleiss' kappa statistics, respectively. Results: Generally, the morphology grading system itself was well adopted by the observers. Its success in application is strongly influenced by the identification of the dural sac. The average intraobserver Cohen's kappa was 0.53 ± 0.2. The inter-observer Fleiss' kappa was 0.38 ± 0.02 in the first rating and 0.3 ± 0.03 in the second rating repeated after two weeks. Discussion: In this attempt, the teaching of the observers was limited to an introduction to the general idea of the morphology grading system and one example MRI image per category. The identification of the dimension of the dural sac may be a difficult issue in absence of complete T1 T2 MRI image series as it was the case here. The similarity of the CSF to possibly present fat on T2 images was the main reason of mismatch in the assignment of the cases to a category. The Fleiss correlation factors of the five observers are fair and the proposed morphology grading system is promising.

Robust T1-Weighted Structural Brain Imaging and Morphometry at 7T Using MP2RAGE.

PURPOSE: To suppress the noise, by sacrificing some of the signal homogeneity for numerical stability, in uniform T1 weighted (T1w) images obtained with the magnetization prepared 2 rapid gradient echoes sequence (MP2RAGE) and to compare the clinical utility of these robust T1w images against the uniform T1w images. MATERIALS AND METHODS: 8 healthy subjects (29.0±4.1 years; 6 Male), who provided written consent, underwent two scan sessions within a 24 hour period on a 7T head-only scanner. The uniform and robust T1w image volumes were calculated inline on the scanner. Two experienced radiologists qualitatively rated the images for: general image quality; 7T specific artefacts; and, local structure definition. Voxel-based and volume-based morphometry packages were used to compare the segmentation quality between the uniform and robust images. Statistical differences were evaluated by using a positive sided Wilcoxon rank test. RESULTS: The robust image suppresses background noise inside and outside the skull. The inhomogeneity introduced was ranked as mild. The robust image was significantly ranked higher than the uniform image for both observers (observer 1/2, p-value = 0.0006/0.0004). In particular, an improved delineation of the pituitary gland, cerebellar lobes was observed in the robust versus uniform T1w image. The reproducibility of the segmentation results between repeat scans improved (p-value = 0.0004) from an average volumetric difference across structures of ≈6.6% to ≈2.4% for the uniform image and robust T1w image respectively. CONCLUSIONS: The robust T1w image enables MP2RAGE to produce, clinically familiar T1w images, in addition to T1 maps, which can be readily used in uniform morphometry packages.

Weighted Kernel regression

In the fixed design regression model, additional weights areconsidered for the Nadaraya--Watson and Gasser--M\"uller kernel estimators.We study their asymptotic behavior and the relationships between new andclassical estimators. For a simple family of weights, and considering theIMSE as global loss criterion, we show some possible theoretical advantages.An empirical study illustrates the performance of the weighted estimatorsin finite samples.

Weighted metric multidimensional scaling

This paper establishes a general framework for metric scaling of any distance measure between individuals based on a rectangular individuals-by-variables data matrix. The method allows visualization of both individuals and variables as well as preserving all the good properties of principal axis methods such as principal components and correspondence analysis, based on the singular-value decomposition, including the decomposition of variance into components along principal axes which provide the numerical diagnostics known as contributions. The idea is inspired from the chi-square distance in correspondence analysis which weights each coordinate by an amount calculated from the margins of the data table. In weighted metric multidimensional scaling (WMDS) we allow these weights to be unknown parameters which are estimated from the data to maximize the fit to the original distances. Once this extra weight-estimation step is accomplished, the procedure follows the classical path in decomposing a matrix and displaying its rows and columns in biplots.

Prediction of postnatal outcome in fetuses with pulmonary hypoplasia by fetal-MRI: Comparison between lung volumetry and pulmonary signal intensity values

Purpose: Pulmonary hypoplasia is a determinant parameter for extra-uterine life. In the last years, MRI appears as a complement to US in order to evaluate the degree of pulmonary hypoplasia in foetuses with congenital anomalies, by using different methods - fetal lung volumetry (FLV), lung-to-liver signal intensity ratio (LLSIR)-. But until now, information about the correlation between the MRI prediction and the real postnatal outcome is limited. Methods and materials: We retrospectively reviewed the fetal MRI performed at our Institution in the last 8 years and selected the cases with suspicion of fetal pulmonary hypoplasia (n = 30). The pulmonary volumetry data of these foetuses were collected and the lung-to-liver signal intensity ratio (LLSIR) measures performed. These data were compared with those obtained from a control group of 25 foetuses considered as normal at MRI. The data of the study group were also correlated with the autopsy records or the post-natal clinical information of the patients. Results: As expected, the control group showed higher FLV and LLSIR values than the problem group at all gestational ages. Higher values of FLV and LLSIR were associated with a better post-natal outcome. Sensitivity, specificity, positive and negative predictive values and accuracy for the relative LLSIR and the relative FLV showed no significant differences. Conclusion: Our data show that not only the FLV but also the relative LLSIR inform about the degree of fetal lung development. This information may help to predict the fetal outcome and to evaluate the need for neonatal intensive care.

Grey matter changes in motor conversion disorder.

OBJECTIVE: To detect anatomical differences in areas related to motor processing between patients with motor conversion disorder (CD) and controls. METHODS: T1-weighted 3T brain MRI data of 15 patients suffering from motor CD (nine with hemiparesis and six with paraparesis) and 25 age- and gender-matched healthy volunteers were compared using voxel-based morphometry (VBM) and voxel-based cortical thickness (VBCT) analysis. RESULTS: We report significant cortical thickness (VBCT) increases in the bilateral premotor cortex of hemiparetic patients relative to controls and a trend towards increased grey matter volume (VBM) in the same region. Regression analyses showed a non-significant positive correlation between cortical thickness changes and symptom severity as well as illness duration in CD patients. CONCLUSIONS: Cortical thickness increases in premotor cortical areas of patients with hemiparetic CD provide evidence for altered brain structure in a condition with presumed normal brain anatomy. These may either represent premorbid vulnerability or a plasticity phenomenon related to the disease with the trends towards correlations with clinical variables supporting the latter.

Automatic classification of MR scans in Alzheimer's disease.

To be diagnostically useful, structural MRI must reliably distinguish Alzheimer's disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states. The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether data-sets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1-weighted MR scans from pathologically proven AD patients and cognitively normal elderly individuals obtained from two centres with different scanning equipment. Because the clinical diagnosis of mild AD is difficult we also tested the ability of support vector machines to differentiate control scans from patients without post-mortem confirmation. Finally we sought to use these methods to differentiate scans between patients suffering from AD from those with frontotemporal lobar degeneration. Up to 96% of pathologically verified AD patients were correctly classified using whole brain images. Data from different centres were successfully combined achieving comparable results from the separate analyses. Importantly, data from one centre could be used to train a support vector machine to accurately differentiate AD and normal ageing scans obtained from another centre with different subjects and different scanner equipment. Patients with mild, clinically probable AD and age/sex matched controls were correctly separated in 89% of cases which is compatible with published diagnosis rates in the best clinical centres. This method correctly assigned 89% of patients with post-mortem confirmed diagnosis of either AD or frontotemporal lobar degeneration to their respective group. Our study leads to three conclusions: Firstly, support vector machines successfully separate patients with AD from healthy aging subjects. Secondly, they perform well in the differential diagnosis of two different forms of dementia. Thirdly, the method is robust and can be generalized across different centres. This suggests an important role for computer based diagnostic image analysis for clinical practice.

Fetal MRI as complement to US in the diagnosis and characterization of anomalies of the genito-urinary tract.

The purpose of this study is to compare the accuracy of prenatal ultrasound (US) and prenatal magnetic resonance imaging (MRI) in the diagnosis and characterization of congenital abnormalities of the genito-urinary tract and to determine if the additional information obtained by MRI may influence the management of the fetus. We retrospectively evaluate 15 cases of congenital genito-urinary tract anomalies detected by prenatal US and with echographic inconclusive diagnosis. We compare the MRI findings with the US findings and the final diagnosis, obtained from neonatal outcomes, imaging studies and pathology records. Fetal US diagnosis was correct in 9 cases (60%) and MRI in 13 cases (86.7%). Prenatal MRI revealed additional information to US in 9 cases (60%), which modified the initial US diagnosis in 5 cases (33.3%) and changed the therapeutic approach in 5 fetuses (33.3%). Fetal MRI was better than US in cases of oligoamnios and in fetuses with genito-urinary pathology concerning the pelvic and perineum region. We believe that MRI should be considered as a complementary diagnostic method in cases of echographic suspicion of congenital pathology of the genito-urinary tract and inconclusive prenatal US.

Continuous arterial spin labeling of mouse cerebral blood flow using an actively-detuned two-coil system at 9.4T.

Among numerous magnetic resonance imaging (MRI) techniques, perfusion MRI provides insight into the passage of blood through the brain's vascular network non-invasively. Studying disease models and transgenic mice would intrinsically help understanding the underlying brain functions, cerebrovascular disease and brain disorders. This study evaluates the feasibility of performing continuous arterial spin labeling (CASL) on all cranial arteries for mapping murine cerebral blood flow at 9.4 T. We showed that with an active-detuned two-coil system, a labeling efficiency of 0.82 ± 0.03 was achieved with minimal magnetization transfer residuals in brain. The resulting cerebral blood flow of healthy mouse was 99 ± 26 mL/100g/min, in excellent agreement with other techniques. In conclusion, high magnetic fields deliver high sensitivity and allowing not only CASL but also other MR techniques, i.e. (1)H MRS and diffusion MRI etc, in studying murine brains.

Weighted Euclidean biplots

We construct a weighted Euclidean distance that approximates any distance or dissimilarity measure between individuals that is based on a rectangular cases-by-variables data matrix. In contrast to regular multidimensional scaling methods for dissimilarity data, the method leads to biplots of individuals and variables while preserving all the good properties of dimension-reduction methods that are based on the singular-value decomposition. The main benefits are the decomposition of variance into components along principal axes, which provide the numerical diagnostics known as contributions, and the estimation of nonnegative weights for each variable. The idea is inspired by the distance functions used in correspondence analysis and in principal component analysis of standardized data, where the normalizations inherent in the distances can be considered as differential weighting of the variables. In weighted Euclidean biplots we allow these weights to be unknown parameters, which are estimated from the data to maximize the fit to the chosen distances or dissimilarities. These weights are estimated using a majorization algorithm. Once this extra weight-estimation step is accomplished, the procedure follows the classical path in decomposing the matrix and displaying its rows and columns in biplots.