Distributional patterns of multiple sclerosis brain lesions. Magnetic resonance imaging--clinical correlation

Cognitive decline in multiple sclerosis: impact of topographic lesion distribution on differential cognitive deficit patterns

Background: Multiple sclerosis (MS) is often accompanied by cognitive dysfunction. A negative correlation between cerebral lesion load and atrophy and cognitive performance has been pointed out almost consistently. Further, the distribution of lesions might be critical for the emergence of specific patterns of cognitive deficits.

Objective: The current study evaluated the significance of total lesion area (TLA) and central atrophy for the prediction of general cognitive dysfunction and tested for a correspondence between lesion topography and specific cognitive deficit patterns.

Methods: Thirty-seven patients with MS underwent neuropsychological assessment and magnetic resonance imaging. Lesion burden and central atrophy were quantified. Patients were classified into three groups by means of individual lesion topography (punctiform lesions/periventricular lesions/confluencing lesions in both periventricular and extra-periventricular regions).

Results: TLA was significantly related to 7 cognitive variables, whereas third ventricle width was significantly associated with 20 cognitive parameters. The three groups differed significantly in their performances on tasks concerning alertness, mental speed, and memory function.

Conclusion: Third ventricle width as a straight-forward measure of central atrophy proved to be of substantial predictive value for cognitive dysfunction, whereas total lesion load played only a minor role. Periventricular located lesions were significantly related to decreased psychomotor speed, whereas equally distributed cerebral lesion load did not. These findings support the idea that periventricular lesions have a determinant impact on cognition in patients with MS.

Asymmetries in the spatial distributions of enhancing lesions and black holes in relapsing-remitting MS

Magnetic resonance imaging (MRI) is the most important paraclinical test in the diagnosis of multiple sclerosis (MS) and for delineating its natural history. We investigate MRIs from a longitudinal study of 24 relapsing-remitting MS patients who had monthly MRI examinations for one year, and were not receiving active MS therapy during this period. We hypothesized that lesions occur randomly throughout the brain, and that patients are homogeneous with regard to spatial patterns of lesion presentation. We recorded the numbers and locations of enhancing lesions and hypointense lesions (black holes) in all scans, and found asymmetrical patterns of lesions about the mid-transaxial, mid-coronal, and mid-sagittal planes. Furthermore, in distinct subsets of patients, enhancing lesions and black holes tend to occur in the same locations. Clustering in lesion locations may be of functional significance, with consequent therapeutic implications.

Working memory and processing speed deficits in systemic lupus erythematosus as measured by the paced auditory serial addition test

As many as 66% of systemic lupus erythematosus (SLE) patients have been reported to have cognitive deficits. These deficits are often associated with information processing speed and working memory. Similarly, processing speed and working memory impairments are the hallmark of cognitive dysfunction in multiple sclerosis (MS). The Paced Auditory Serial Addition Test (PASAT) places high demands on processing speed and working memory. Fisk and Archibald, however, demonstrated that the total score of the PASAT does not accurately reflect impairments in these cognitive processes. They found that MS patients used a chunking strategy to obtain correct responses and reduce the cognitive demands of the task. In the present study, PASAT performance was examined for 45 SLE patients and 27 controls using alternative scoring procedures. Although the total number of correct responses did not differ between SLE and controls at the 2.4 or 2.0 s presentation rates, SLE patients had fewer dyads (correct consecutive responses) than controls at the faster rate, and more chunking responses than controls at both rates. Disease activity, disease duration, depression, fatigue, and corticosteroids could not account for these differences. The findings suggest that SLE patients, like MS patients, chunk responses more often than controls, and that this scoring procedure may better reflect the working memory and processing speed deficits present in SLE.

Brain MRI lesion load quantification in multiple sclerosis: a comparison between automated multispectral and semi-automated thresholding computer-assisted techniques

Brain magnetic resonance imaging (MRI) lesion volume measurement is an advantageous tool for assessing disease burden in multiple sclerosis (MS). We have evaluated two computer-assisted techniques: MSA multispectral automatic technique that is based on bayesian classification of brain tissue and NIH image analysis technique that is based on local (lesion by lesion) thresholding, to establish reliability and repeatability values for each technique. Brain MRIs were obtained for 30 clinically definite relapsing-remitting MS patients using a 2.0 Tesla MR scanner with contiguous, 3 mm thick axial, T1, T2 and PD weighted modalities. Digital (Dicom 3) images were analyzed independently by three observers; each analyzed the images twice, using the two different techniques (Total 360 analyses). Accuracy of lesion load measurements using phantom images of known volumes showed significantly better results for the MSA multispectral technique (p < 0.001). The mean intra-and inter-observer variances were, respectively, 0.04 +/- 0.4 (range 0.04-0.13), and 0.09 +/- 0.6 (range 0.01-0.26) for the multispectral MSA analysis technique, 0.24 +/- 2.27 (range 0.23-0.72) and 0.33 +/- 3.8 (range 0.47-1.36) for the NIH threshold technique. These data show that the MSA multispectral technique is significantly more accurate in lesion volume measurements, with better results of within and between observers' assessments, and the lesion load measurements are not influenced by increased disease burden. Measurements by the MSA multispectral technique were also faster and decreased analysis time by 43%. The MSA multispectral technique is a promising tool for evaluating MS patients. Non-biased recognition and delineation algorithms enable high accuracy, low intra-and inter-observer variances and fast assessment of MS related lesion load.

The neuropsychology of multiple sclerosis: contributions of neuroimaging research

Multiple sclerosis (MS) is often associated with cognitive and emotional changes that affect daily activities and quality of life. Deficits in memory, executive function, processing speed, and other cognitive domains are frequently reported. In addition, mood disturbances and fatigue are common. In this article, the authors highlight research on individual differences in the neuropsychology of MS, and emphasize neuroimaging studies that help elucidate the basis of the deficits.

Increased differentiation of intracranial white matter lesions by multispectral 3D-tissue segmentation: preliminary results

MRI is a very sensitive imaging modality, however with relatively low specificity. The aim of this work was to determine the potential of image post-processing using 3D-tissue segmentation technique for identification and quantitative characterization of intracranial lesions primarily in the white matter. Forty subjects participated in this study: 28 patients with brain multiple sclerosis (MS), 6 patients with subcortical ischemic vascular dementia (SIVD), and 6 patients with lacunar white matter infarcts (LI). In routine MR imaging these pathologies may be almost indistinguishable. The 3D-tissue segmentation technique used in this study was based on three input MR images (T(1), T(2)-weighted, and proton density). A modified k-Nearest-Neighbor (k-NN) algorithm optimized for maximum computation speed and high quality segmentation was utilized. In MS lesions, two very distinct subsets were classified using this procedure. Based on the results of segmentation one subset probably represent gliosis, and the other edema and demyelination. In SIVD, the segmented images demonstrated homogeneity, which differentiates SIVD from the heterogeneity observed in MS. This homogeneity was in agreement with the general histological findings. The LI changes pathophysiologically from subacute to chronic. The segmented images closely correlated with these changes, showing a central area of necrosis with cyst formation surrounded by an area that appears like reactive gliosis. In the chronic state, the cyst intensity was similar to that of CSF, while in the subacute stage, the peripheral rim was more prominent. Regional brain lesion load were also obtained on one MS patient to demonstrate the potential use of this technique for lesion load measurements. The majority of lesions were identified in the parietal and occipital lobes. The follow-up study showed qualitatively and quantitatively that the calculated MS load increase was associated with brain atrophy represented by an increase in CSF volume as well as decrease in "normal" brain tissue volumes. Importantly, these results were consistent with the patient's clinical evolution of the disease after a six-month period. In conclusion, these results show there is a potential application for a 3D tissue segmentation technique to characterize white matter lesions with similar intensities on T(2)-weighted MR images. The proposed methodology warrants further clinical investigation and evaluation in a large patient population.

Magnetic resonance imaging based volume estimation of ovarian tumours: use of a segmentation and 3D reformation software

The application of a new segmentation software, Anatomatic in the evaluation of volumetric measurements of ovarian tumours and the new Medimag three-dimensional (3D) software in the evaluation of 3D image representation of ovarian tumours with 1.5 T magnetic resonance imaging (MRI) is described. Our goal was to compare MRI based volumetry with operative findings at laparotomy for six consecutive patients with suspected ovarian tumours. Volumetric analysis and three dimensional image reconstructions of the tumours were obtained. At laparotomy, the tumour sizes were measured in situ, and the volumes were calculated. Using Anatomatic, reproducible tumour volumes were achieved with ease and within a reasonably fast time in patients with ovarian tumours without ascites. Medimag helped achieve realistic 3D representations of the tumours. For the four solitary tumours segmentation based volumetry and laparotomy findings agreed in three cases. In one patient with an oval shaped tumour, the segmented volume was double as compared to that estimated at laparotomy. Of the two patients with multiple tumours, both patients had significant ascites, and volumetry misinterpreted the fluid as tumour cyst fluid and markedly overestimated the tumour size. In conclusion, the MRI based segmentation volumetry and 3D image reconstructions are rapid, and reproducible methods of measuring ovarian tumours in patients without significant ascites.

Cortical deficits in multiple sclerosis on the basis of subcortical lesions

Patients suffering from multiple sclerosis have a high frequency of cognitive deficits usually attributable to demyelination and axonal loss in the subcortical white matter. Neurologic abnormalities referable to cortical function are uncommon but have been described. The present study describes three patients with clinically definite MS with deficits in cognitive function referable to cortical location. Two of the patients underwent positron emission tomography and showed profound cortical hypometabolism adjacent to subcortical white matter lesions seen on MRI. This paper points out that neurologic deficits referable to cortical sites may be caused by subcortical white matter lesions and that cognitive dysfunction in patients with MS may progress rapidly in the absence of motoria deficits or other evidence of clinical deterioration. Multiple Sclerosis (2000) 6 50 - 55

Automated segmentation of multiple sclerosis lesions in multispectral MR imaging using fuzzy clustering

A method is presented for fully automated detection of Multiple Sclerosis (MS) lesions in multispectral magnetic resonance (MR) imaging. Based on the Fuzzy C-Means (FCM) algorithm, the method starts with a segmentation of an MR image to extract an external CSF/lesions mask, preceded by a local image contrast enhancement procedure. This binary mask is then superimposed on the corresponding data set yielding an image containing only CSF structures and lesions. The FCM is then reapplied to this masked image to obtain a mask of lesions and some undesired substructures which are removed using anatomical knowledge. Any lesion size found to be less than an input bound is eliminated from consideration. Results are presented for test runs of the method on 10 patients. Finally, the potential of the method as well as its limitations are discussed.

Computerised volumetric analysis of lesions in multiple sclerosis using new semi-automatic segmentation software

The paper describes the application of new semi-automatic segmentation software to the task of detection of anatomical structures and lesion and their three-dimensional (3D) visualisation in 23 patients with secondary progressive multiple sclerosis (MS). The purpose is to study the correlation between magnetic resonance imaging (MRI) parameters (volumes of plaques and cerebrospinal fluid spaces) and clinical deficits (neurological deficits in the form of EDSS and RFSS scores, and neuropsychological deficits). The software operates in PC/Windows and PC/NeXTstep environments and utilises graphical user interfaces. Quantitative accuracy is measured by performing segmentation of fluid-filled syringes (relative error of 1.5%), and reproducibility is measured by intra- and inter-observer studies (3% and 7% variability, respectively). The mean volumes of MS plaques show significant correlations with the total RFSS scores (p = 0.04). Relative intracranial cerebrospinal fluid (CSF) space volumes show statistically significant correlation with EDSS scores (p = 0.01). The mean volume of MS plaques shows a significant correlation with the overall neuropsychological deficits (p = 0.03). 3D visualisation helps to understand the relationship of lesions to the surrounding brain structures. The use of semiautomatic segmentation techniques is recommended in the clinical diagnosis of MS patients.

Multiple sclerosis lesion quantification using fuzzy-connectedness principles

Multiple sclerosis (MS) is a disease of the white matter. Magnetic resonance imaging (MRI) is proven to be a sensitive method of monitoring the progression of this disease and of its changes due to treatment protocols. Quantification of the severity of the disease through estimation of MS lesion volume via MR imaging is vital for understanding and monitoring the disease and its treatment. This paper presents a novel methodology and a system that can be routinely used for segmenting and estimating the volume of MS lesions via dual-echo fast spin-echo MR imagery. A recently developed concept of fuzzy objects forms the basis of this methodology. An operator indicates a few points in the images by pointing to the white matter, the grey matter, and the cerebro-spinal fluid (CSF). Each of these objects is then detected as a fuzzy connected set. The holes in the union of these objects correspond to potential lesion sites which are utilized to detect each potential lesion as a three-dimensional (3-D) fuzzy connected object. These objects are presented to the operator who indicates acceptance/rejection through the click of a mouse button. The number and volume of accepted lesions is then computed and output. Based on several evaluation studies, we conclude that the methodology is highly reliable and consistent, with a coefficient of variation (due to subjective operator actions) of 0.9% (based on 20 patient studies, three operators, and two trials) for volume and a mean false-negative volume fraction of 1.3%, with a 95% confidence interval of 0%-2.8% (based on ten patient studies).