Analysis Of Multiple Sclerosis Lesions Research Articles

Location-adjusted Wald statistics for scalar parameters

Inference about a scalar parameter of interest is a core statistical task that has attracted immense research in statistics. The Wald statistic is a prime candidate for the task, on the grounds of the asymptotic validity of the standard normal approximation to its finite-sample distribution, simplicity and low computational cost. It is well known, though, that this normal approximation can be inadequate, especially when the sample size is small or moderate relative to the number of parameters. A novel, algebraic adjustment to the Wald statistic is proposed, delivering significant improvements in inferential performance with only small implementation and computational overhead, predominantly due to additional matrix multiplications. The Wald statistic is viewed as an estimate of a transformation of the model parameters and is appropriately adjusted, using either maximum likelihood or reduced-bias estimators, bringing its expectation asymptotically closer to zero. The location adjustment depends on the expected information, an approximation to the bias of the estimator, and the derivatives of the transformation, which are all either readily available or easily obtainable in standard software for a wealth of models. An algorithm for the implementation of the location-adjusted Wald statistics in general models is provided, as well as a bootstrap scheme for the further scale correction of the location-adjusted statistic. Ample analytical and numerical evidence is presented for the adoption of the location-adjusted statistic in prominent modelling settings, including inference about log-odds and binomial proportions, logistic regression in the presence of nuisance parameters, beta regression, and gamma regression. The location-adjusted Wald statistics are used for the construction of significance maps for the analysis of multiple sclerosis lesions from MRI data.

ANALYSIS OF MULTIPLE SCLEROSIS LESIONS VIA SPATIALLY VARYING COEFFICIENTS.

Magnetic resonance imaging (MRI) plays a vital role in the scientific investigation and clinical management of multiple sclerosis. Analyses of binary multiple sclerosis lesion maps are typically "mass univariate" and conducted with standard linear models that are ill suited to the binary nature of the data and ignore the spatial dependence between nearby voxels (volume elements). Smoothing the lesion maps does not entirely eliminate the non-Gaussian nature of the data and requires an arbitrary choice of the smoothing parameter. Here we present a Bayesian spatial model to accurately model binary lesion maps and to determine if there is spatial dependence between lesion location and subject specific covariates such as MS subtype, age, gender, disease duration and disease severity measures. We apply our model to binary lesion maps derived from T2-weighted MRI images from 250 multiple sclerosis patients classified into five clinical subtypes, and demonstrate unique modeling and predictive capabilities over existing methods.

Analysis of multiple sclerosis lesions using a fusion of 3.0 T FLAIR and 7.0 T SWI phase: FLAIR SWI

To improve multiple sclerosis (MS) research by introducing a new type of contrast, namely, the combination of fluid-attenuated inversion recovery (FLAIR) data acquired at 3.0 T and 7.0 T susceptibility-weighted imaging (SWI) phase data. The approach of this new contrast is whole-brain coverage with 3.0 T-FLAIR data for lesion detection--currently limited at 7.0 T due to specific absorption rate (SAR) limits--overlaid with high-resolution, small vessel, and iron-related 7.0 T SWI contrast. Lesion analysis in terms of penetrating veins and local iron depositions were performed. Data from 10 MS patients were acquired at 3.0 T and at 7.0 T. FLAIR data, acquired at 3.0 T, were registered to 7.0 T SWI phase data and SWI image processing was performed using 3.0 T FLAIR data instead of SWI magnitude data. A total of 299 MS plaques were detected in eight MS patients. Penetrating veins were found in 75 MS plaques, iron depositions in 48 MS plaques, and veins accompanied with iron depositions in 44 MS plaques. FLAIR-SWI provides radiologically known, hyperintense definition of MS lesions overlaid with high-resolution visualization of iron deposits and venous blood vessels and offers new insights into MS lesions.

Application of spherical harmonics derived space rotation invariant indices to the analysis of multiple sclerosis lesions' geometry by MRI.

In the longitudinal study of multiple sclerosis (MS) lesions, varying position of the patient inside the MRI scanner is one of the major sources of assessment errors. We propose to use analytical indices that are invariant to spatial orientation to describe the lesions, rather than focus on patient repositioning or image realignment. Studies were made on simulated lesions systematically rotated, from in vitro MS lesions scanned on different days, and from in vivo MS lesions from a patient that was scanned five times the same day with short intervals of time between scans. Each of the lesions' 3D surfaces was approximated using spherical harmonics, from which indices that are invariant to space rotation were derived. From these indices, an accurate and highly reproducible volume estimate can be derived, which is superior to the common approach of 2D slice stacking. The results indicate that the suggested approach is useful in reducing part of the errors that affect the analysis of changes of MS lesions during follow-up studies. In conclusion, our proposed method circumvents the need for precise patient repositioning and can be advantageous in MRI longitudinal studies of MS patients.

Chemical Analysis of Multiple Sclerosis Lesions by FT-IR Microspectroscopy

Fourier transform infrared microspectroscopy can be used to collect infrared spectra from microscopic regions of tissue sections. If spectra are collected along a grid pattern, then maps of chemical functional groups can be produced and correlated to tissue histopathology. In the present study, white matter from multiple sclerosis and control brains were examined. Mapping experiments were designed such that 17 spectra were collected at 200 μm intervals along a line that was partially or wholly within a multiple sclerosis lesion site or within a representative white matter region of control tissue. Data analysis was based on earlier in vitro studies which found that the carbonyl at 1740 cm −1 increases when lipids become oxidized ( Free Rad. Biol. Med. 16:591–601, 1994), and the amide I peak at ∼1660 cm −1 broadens when proteins become oxidized ( FEBS Let. 362:165–170, 1995). The results indicated that the CO to CH 2 ratio (1740 cm −1:1468 cm −1) was elevated at several collection points in lesion sites from multiple sclerosis brains compared to values from white matter of control brains. Inspection of the amide I peak at 1657 cm −1 revealed that it was broadened towards 1652 cm −1 in multiple sclerosis tissues but not control tissues. These results suggest that lipids and proteins could be oxidized at active multiple sclerosis lesion sites. The localization of these products to lesion sites supports a role for free radicals in the pathogenesis of multiple sclerosis.

MR multispectral analysis of multiple sclerosis lesions.

Although quantification of the lesion burden from serial MR examinations of patients with multiple sclerosis (MS) is a common technique to assess disease activity in clinical trials, pathologic change may occur within a lesion without a corresponding change in volume. Therefore, measures of lesion volume and composition may improve the sensitivity of detecting disease activity. A new technique has been developed that provides information about the intensity composition of MS lesions in standard spin-echo MR examinations. The new technique is based on the multispectral "feature space" intensity distributions of the lesions and normal tissues. Analysis of MR examinations of materials with known T1 and T2 times showed that feature space position from spin-echo examinations is largely determined from proton density (rho), T2, and the interecho delay. Information about intensity composition was obtained by reducing the multidimensional intensity distribution to one dimension while minimizing the loss of information. This technique was used to analyze eight lesions in standard spin-echo MR examinations of three patients with MS. Lesion distributions were compared between examinations by first calibrating the examinations based on the intensity distributions of cerebrospinal fluid (CSF), an internal reference tissue. Many of the lesion distributions had a distinctive peak at low intensity, corresponding to normal-appearing white matter (WM). Within the lesion distributions, increases in high intensity peaks generally were accompanied by reductions in the WM peak. Serial analysis of the lesion distributions revealed some dramatic fluctuations, even when lesion volume remained constant.

Computer-assisted identification and quantification of multiple sclerosis lesions in MR imaging volumes in the brain.

Magnetic resonance (MR) imaging is the principal imaging technique for the diagnosis of multiple sclerosis (MS). However, quantifying the number and extent of lesions on MR images manually is arduous. The authors have developed a computerized three-dimensional (3D) quantitative system to assist in the identification and analysis of MS lesions in proton-density (PD)- and T2-weighted volumes of the head. The system provides intuitive, interactive operations that allow flexible extraction of information from the data. Use of the system to analyze MR examinations of a phantom containing regular "lesions" showed that accurate (average error, < 0.21 cm3) and precise (10% or better for lesions > 1 cm3) measurements of objects less than 7 cm3 is possible, and that an estimate of the quantization error predicted the uncertainty in the volume. Analysis of four MR examinations of a chronic-progressive MS patient conducted over an 18-month period was performed. A two-dimensional histogram showing the frequency of voxels with particular PD- and T2-weighted intensities revealed a distinct cluster only in histograms of sections that contained lesions. Measurements and 3D volume rendering of lesions clearly showed changes in lesion shape, position, and size.

Accuracy and reproducibility in volumetric analysis of multiple sclerosis lesions.

The accuracy and reproducibility of dual-contrast segmentation based on nonparametric feature map analysis have been investigated in a multicomponent gelatin phantom. The root mean square errors in volume ranged from 0.02 cm3 for small volumes to 3.8 cm3 for larger volumes, with a mean error of 0.97 cm3. Average inter- and intraobserver coefficients of variation were found to be < 7% for all compartments. To evaluate the reproducibility of segmentation of clinical image data, volumes of total brain, CSF, and multiple sclerosis (MS) lesions were obtained from five image sets of MS patients. Inter- and intraobserver coefficients of variations were computed for the patient data and were found to be < 5% for brain, 17% for CSF, and 20% for MS lesions. Such variations were found to be reduced by appropriate preprocessing of the images.