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BACKGROUND AND PURPOSE: Lesion volume change (LVC) assessment is essential in monitoring MS progression. LVC is usually measured by independently segmenting serial MR imaging examinations. Subtraction imaging has been proposed for improved visualization and characterization of lesion change. We compare segmentation of subtraction images (SSEG) with serial single time-point conventional segmentation (CSEG) by assessing the LVC relationship to brain atrophy and disease duration, as well as scan-rescan reproducibility and annual rates of lesion accrual. MATERIALS AND METHODS: Pairs of scans were acquired 1.5 to 4.7 years apart in 21 patients with multiple sclerosis (MS). Scan-rescan MR images were acquired within 30 minutes in 10 patients with MS. LVC was measured with CSEG and SSEG after coregistration and normalization. Coefficient of variation (COV) and Bland-Altman analyses estimated method reproducibility. Spearman rank correlations probed associations between LVC and other measures. RESULTS: Atrophy rate and net LVC were associated for SSEG ( R = −0.446; P < .05) but not when using CSEG ( R = −0.180; P = .421). Disease duration did not show an association with net lesion volume change per year measured by CSEG ( R = −0.360; P = .11) but showed an inverse correlation with SSEG-derived measurements ( R = −0.508; P < .05). Scan-rescan COV was lower for SSEG (0.98% ± 1.55%) than for CSEG (8.64% ± 9.91%). CONCLUSION: SSEG unveiled a relationship between T2 LVC and concomitant brain atrophy and demonstrated significantly higher measurement reproducibility. SSEG, a promising tool providing detailed analysis of subtle alterations in lesion size and intensity, may provide critical outcome measures for clinical trials of novel treatments, and may provide further insight into progression patterns in MS.

A method for incorporating prior knowledge into the fuzzy connectedness image segmentation framework is presented. This prior knowledge is in the form of probabilistic feature distribution and feature size maps, in a standard anatomical space, and ldquointensity hintsrdquo selected by the user that allow for a skewed distribution of the feature intensity characteristics. The fuzzy affinity between pixels is modified to encapsulate this domain knowledge. The method was tested by using it to segment brain lesions in patients with multiple sclerosis, and the results compared to an established method for lesion outlining based on edge detection and contour following. With the fuzzy connections (FC) method, the user is required to identify each lesion with a mouse click, to provide a set of seed pixels. The algorithm then grows the features from the seeds to define the lesions as a set of objects with fuzzy connectedness above a preset threshold. The FC method gave improved interobserver reproducibility of lesion volumes, and the set of pixels determined to be lesion was more consistent compared to the contouring method. The operator interaction time required to evaluate one subject was reduced from an average of 111 min with contouring to 16 min with the FC method.

Recent studies have indicated that brain atrophy is more closely associated with cognitive impairment in multiple sclerosis (MS) than are conventional MRI lesion measures. Enlargement of the third ventricle shows a particularly strong correlation with cognitive impairment, suggesting clinical relevance of damage to surrounding structures, such as the thalamus. Previous imaging and pathology studies have demonstrated thalamic involvement in MS. In this study, we tested the hypothesis that thalamic volume is lower in MS than in normal subjects, and that thalamic atrophy in MS correlates with cognitive function.We studied 79 patients with MS and 16 normal subjects. A subgroup of 31 MS subjects underwent cognitive testing. The thalamus was segmented in whole from three-dimensional MRI scans. We also determined whole brain atrophy (brain parenchymal fraction), third ventricular width, and whole brain T2-weighted (fluid-attenuated inversion recovery) hyperintense, T1 hypointense, and gadolinium-enhanced lesion volumes.Normalized thalamic volume was 16.8% lower in the MS group (p0.0001) vs controls. Cognitive performance in all domains was moderately to strongly related to thalamic volume in the MS group (r = 0.506 to 0.724, p0.005), and thalamic volume entered and remained in all regression models predicting cognitive performance. Thalamic volume showed a weak relationship to physical disability score (r = -0.316, p = 0.005).These findings suggest that thalamic atrophy is a clinically relevant biomarker of the neurodegenerative disease process in multiple sclerosis.

Cognitive dysfunction is common in patients with multiple sclerosis (MS), and has been associated with MRI measures of lesion burden and atrophy. Little is known about the prevalence of cognitive impairment in patients with early MS. The associations between cognitive impairment and MRI measures of disease severity early in the disease course are also unclear. This study used a brief battery of cognitive tests to determine the prevalence and pattern of cognitive impairment in patients with clinically isolated syndromes or newly diagnosed MS. The associations between cognitive impairment and MRI measures of disease severity early in the disease course were also examined. Ninety-two patients with clinically isolated syndromes or the diagnosis of MS within the last 3 years participating in the CLIMB study underwent a neurologic examination, neuropsychological evaluation and MRI at 1.5T. Forty-nine percent of patients were impaired on one or more cognitive measures. There were no significant correlations between cognitive scores and MRI measures of disease severity including total T2 lesion volume, normal appearing white matter volume, grey matter volume, and brain parenchymal fraction. These findings suggest that cognitive impairment may predate the appearance of gross structural abnormalities on MRI and serve as an early marker of disease activity in MS. Multiple Sclerosis 2007; 13: 1004—1010. http://msj.sagepub.com

This article discusses and reviews advanced forms of serial morphometry in the context of a disease progression model in multiple sclerosis (MS). This model of disease activity distinguishes between overall disease activity and the proportion thereof that becomes permanent damage. This translates into a progression model that features a repair potential, which, when exhausted, marks the conversion or progression from relapsing to progressive disease. The level of repair capacity at a given time determines the rate of progression. Both clinical and MRI variables appear to be in support of such a model. We examine possible MRI markers for this repair capacity, particularly the short-term behavior of new MRI lesions, quantified by methods of time-series analysis--that is, capturing lesion dynamics in the form of MRI intensity change directly, rather than shape or volume change. Lower rates of individual lesion recovery may represent lower repair and greater proximity to a progressive stage. Individuals with low transient lesion turnover appear to undergo more rapid progression and atrophy. Because disease-modifying therapies aim to alter the pathophysiological chain of inflammation, demyelination, and axonal loss, a therapeutic effect may therefore be more readily apparent as a change in lesion dynamics and recovery rate and level, rather than a change in total lesion burden or enhancing lesion number.

Objective:To develop covariate specific short-term disability curves to demonstrate the probability of progressing by Expanded Disability Status Scale (EDSS) at semiannual visits.Methods:Semiannual EDSS scores were prospectively collected in 218 relapsing-remitting (RR) and clinically isolated syndrome (CIS) patients as part of the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB) study. Baseline brain parenchymal fraction (BPF) and T2 lesion volume were available on 205 patients. A partial proportional odds model determined the influence of covariates on the change in EDSS score at subsequent visits. A discrete second order Markov transitional model was fit and generated a probability matrix for each subject; the 6-month probabilities of EDSS change were graphically represented.Results:The univariate analysis demonstrated the lowest baseline BPF quartile (OR 1.99;p= 0.0203) and the highest T2 lesion volume quartile (OR 2.19;p= 0.0130) were associated with progression in EDSS. Covariate specific disability curves demonstrated the effect of BPF and T2 lesion volume on short-term progression. In subjects with a 6-month EDSS of 2, the probability of a sustained progression of an EDSS of 3 within 3 years was 0.277 for a subject with low BPF and a high T2 lesion volume vs 0.055 for a subject with high BPF and a low T2 lesion volume.Conclusions:Markov transitional models allow for the comparison of covariate specific short-term disability changes among groups of patients with multiple sclerosis.

Advanced magnetic resonance imaging (MRI) studies often require the transformation of large numbers of images into a common space. Calculating transformations that relate each image to every other and applying them to the images on demand are theoretically possible; however, these can be computationally prohibitive. Therefore, relating each image to only one other image, then linking those transforms together to relate any two images in the database, may be an efficient alternative. Evaluated were the feasibility and validity of image registration to bring intraindividual MR images into mutual correspondence for longitudinal analysis through the concatenation of precomputed transforms. A longitudinal data set of 10 multiple sclerosis patients with nine serial dual-echo spin-echo, 1.5-T MRI scans was used. Intrasubject registrations were performed stepwise between consecutive images and direct from each time point to the baseline. Consecutive transforms were concatenated and evaluated against direct registrations by comparing the resulting transformed images (using Pearson correlation coefficient). Confounding variables such as time between scans, brain atrophy, and change in lesion load were evaluated. We found the images resampled with the direct and the concatenated transforms to be highly correlated, and there was no significant difference between methods. Differences in brain parenchymal fraction (a measure of brain atrophy) showed significant inverse correlation with the correspondence of the resampled images. Results indicate that concatenating multiple transforms that link two images together produces near-identical results to that of direct registration; thus, this method is both useful and valid.

A significant skewing of the peripheral T cell repertoire has been shown in relapsing-remitting multiple sclerosis (MS). Most of the studies already performed in this field are cross-sectional and therefore, little is known of the T cell repertoire evolution over time in MS and the correlation of T cell repertoire variation with clinical and MRI parameters. This study was performed on serially harvested frozen PBMC from nine untreated MS patients (27 samples) and 14 healthy individuals. The blood T cell repertoire of each patient was analysed at the complementarity determining region 3 (CDR3) level and compared with a monthly MRI scan performed over a six month period with assessment of T2 lesion load and gadolinium enhancing lesions. A highly significant blood T cell repertoire skewing was observed in MS patients as compared with healthy controls (p0.01). In addition, the number of altered Vbeta families correlated significantly with both the T2 lesion volume and the number of gadolinium enhancing lesions as assessed by MRI (Spearman correlation tests, r=0.51 and r=0.44, p0.01 and p0.05 respectively). Furthermore, the variation of the number of altered Vbeta families over time also correlated with the appearance of new gadolinium enhancing lesions (r=0.36, p=0.05). These findings which need confirmation on larger serial cohorts, suggest an association between the magnitude of TCRBV CDR3 length distribution alterations in the peripheral blood of MS patients and the disease process.

Purpose.: To automatically segment multiple sclerosis (MS) lesions into three subtypes (i.e., enhancing lesions, T1 "black holes", T2 hyperintense lesions). Materials and methods.: Proton density-, T2- and contrast-enhanced T1-weighted brain images of 12 MR scans were pre-processed through intracranial cavity (IC) extraction, inhomogeneity correction and intensity normalization. Intensity-based statistical k-nearest neighbor (k-NN) classification was combined with template-driven segmentation and partial volume artifact correction (TDS+) for segmentation of MS lesions subtypes and brain tissue compartments. Operator-supervised tissue sampling and parameter calibration were performed on 2 randomly selected scans and were applied automatically to the remaining 10 scans. Results from this three-channel TDS+ (3ch-TDS+) were compared to those from a previously validated two-channel TDS+ (2ch-TDS+) method. The results of both the 3ch-TDS+ and 2ch-TDS+ were also compared to manual segmentation performed by experts. Results.: Intra-class correlation coefficients (ICC) of 3ch-TDS+ for all three subtypes of lesions were higher (ICC between 0.95 and 0.96) than that of 2ch-TDS+ for T2 lesions (ICC = 0.82). The 3ch-TDS+ also identified the three lesion subtypes with high specificity (98.7-99.9%) and accuracy (98.5-99.9%). Sensitivity of 3ch-TDS+ for T2 lesions was 16% higher than with 2ch-TDS+. Enhancing lesions were segmented with the best sensitivity (81.9%). "Black holes" were segmented with the least sensitivity (62.3%). Conclusion.: 3ch-TDS+ is a promising method for automated segmentation of MS lesion subtypes. © 2006 Elsevier Inc. All rights reserved.

Magnetic resonance imaging (MRI) has become a central tool for patient management, as well as research, in multiple sclerosis (MS). Measurements of disease burden and activity derived from MRI through quantitative image analysis techniques are increasingly being used. There are many complexities and challenges in building computerized processing pipelines to ensure efficiency, reproducibility, and quality control for MRI scans from MS patients. Such paradigms require advanced image processing and analysis technologies, as well as integrated database management systems to ensure the most utility for clinical and research purposes. This article reviews pipelines available for quantitative clinical MRI research in MS, including image segmentation, registration, time-series analysis, performance validation, visualization techniques, and advanced medical imaging software packages. To address the complex demands of the sequential processes, the authors developed a workflow management system that uses a centralized database and distributed computing system for image processing and analysis. The implementation of their system includes a web-form-based Oracle database application for information management and event dispatching, and multiple modules for image processing and analysis. The seamless integration of processing pipelines with the database makes it more efficient for users to navigate complex, multistep analysis protocols, reduces the user's learning curve, reduces the time needed for combining and activating different computing modules, and allows for close monitoring for quality-control purposes. The authors' system can be extended to general applications in clinical trials and to routine processing for image-based clinical research.

A mathematical model was applied to new lesion formation in multiple sclerosis, as apparent on frequent T2-weighted MRI. The pathophysiologically motivated two-process model comprises two opposing nonlinear self-limiting processes, intended to represent degenerative and reparatory processes, respectively, investigating T2 activity from a dynamic/temporal rather than a spatial/static perspective. Parametric maps were obtained from the model to characterize the MRI dynamics of lesion development, answering the questions of how long new T2 lesion activity persists, how much residual damage/hyperintensity remains and how the T2 dynamics compare to those of contrast-enhancing MRI indicating active inflammation. 997 MRI examinations were analyzed, acquired weekly to monthly from 45 patients over a 1-year period. The model was applied to all pixels within 332 new lesions, capturing the time profiles with excellent fidelity (r = 0.89 ± 0.03 average correlation between model and image data). From this modeling perspective, the observed dynamics in new T2 lesions are in agreement with two opposing processes of longitudinal intensity change, such as inflammation and degeneration versus resorbtion and repair. On average, about one third of a new lesion consisted of transient signal change with little or no residual hyperintensity and activity of 10 weeks or less. Global lesion burden as MRI surrogate of disease activity may therefore be confounded by large amounts of transient hyperintensity. T2 activity also persisted significantly beyond the period of contrast enhancement, thereby defining MRI sensitivity toward a subacute phase of lesion development beyond blood–brain barrier patency. Concentric patterns of dynamic properties within a lesion were observed, consistent with concentric histological appearance of resulting MS plaques.

Background: T cell receptor (TCR) peptide vaccination is a novel approach to treating multiple sclerosis (MS). The low immunogenicity of previous vaccines has hindered the development of TCR peptide vaccination for MS. Objective: To compare the immunogenicity of intramuscular injections of TCR BV5S2, BV6S5 and BV13S1 CDR2 peptides in incomplete Freund’s adjuvant (IFA) with intradermal injections of the same peptides without IFA. Methods: MS subjects were randomized to receive TCR peptides/IFA, TCR peptides/saline or IFA alone. Subjects were on study for 24 weeks. Results: The TCR peptides/IFA vaccine induced vigorous T cell responses in 100% of subjects completing the 24-week study (9/9) compared with only 20% (2/10) of those receiving the TCR peptides/saline vaccine (P =0.001). IFA alone induced a weak response in only one of five subjects. Aside from injection site reactions, there were no significant adverse events attributable to the treatment. Conclusions: The trivalent TCR peptide in IFA vaccine represents a significant improvement in immunogenicity over previous TCR peptide vaccines and warrants investigation of its ability to treat MS.

Our knowledge about the process of aging has continued to evolve as the methods used to study this process become more sophisticated. As more becomes known about the diagnostic criteria for dementia, the population of subjects taking part in aging studies has become more carefully screened minimizing the role of dementia as a confounding variable. Furthermore, advances in imaging techniques now allow us to view the anatomy of the brain in vivo better than any time in the past paving the way for longitudinal studies of the brain. It should not be surprising given the changes seen in studies of aging that our conventional wisdom of the aging process is being called into question.

This article reports test-retest reproducibility of functional MRI (fMRI) measurement on brain activation elicited by auditory-cued sequential finger tapping. Eight right-handed volunteers participated in nine fMRI sessions, approximately eight weeks apart, for the duration of more than a year. The first scan session was repeated within a day to examine the intra-session reproducibility. The frequency of activation for neural substrates relevant to the task was constructed across the subjects and sessions. The spatial reproducibility was measured as the ratio of the size of the volume as well as its overlaps with respect to the first scan session from regions-of-interest in the selected motor circuitry. Consistent activation patterns between sessions and across subjects were observed in the sensorimotor areas such as the left primary, supplementary, and premotor areas, as well as in the right cerebellar areas without evidence of session-dependent trends. Quantitative analysis showed that the reproducibility measures varied within the range obtained from studies on fMRI reproducibility covering much shorter terms. Intra-session fMRI scans yielded slightly better reproducibility measures compared to the results obtained from other scan sessions. The findings suggest that the reproducible fMRI measurement can be obtained for long-term monitoring of brain function.

White matter signal abnormality (WMSA) is often present in the MRIs of older persons with mobility impairment. We examined the relationship between impaired mobility and the progressive accrual of WMSA. Mobility was assessed with the Short Physical Performance Battery (SPPB) and quantitative measures of gait and balance. Fourteen subjects had baseline and follow-up MRI scans performed 20 months apart. WMSA was detected and quantified using automated computer algorithms. In the control subjects, WMSA volume increased by 0.02+/-0.05% ICCV (percent intracranial cavity volume)/year while the WMSA of mobility impaired subjects increased five-times faster (0.10+/-0.10 ICCV/year, p=0.03). WMSA volume was related to some of the mobility measures and was sensitive to change which was not true of the other MRI variables. The study demonstrates the sensitivity of longitudinal automated volumetric analysis of WMSA to differentiate differences in the accrual rate of WMSA in groups selected on the basis of mobility. Based on these results, we propose that a subset of subjects with mobility impairment have accelerated, disease related WMSA accrual, thus explaining the rapid progression of mobility impairment in some older persons without apparent cause. This study demonstrates that quantitative MRI and performance measures can provide valuable insight into the rate of progression and pathophysiologic abnormalities underlying mobility impairment.

Changes in mean magnetic resonance imaging (MRI)-derived measurements between patient groups are often used to determine outcomes in therapeutic trials and other longitudinal studies of multiple sclerosis (MS). However, in day-to-day clinical practice the changes withinindividual patients may also be of interest. In this paper, we estimated the measurement error of an automated brain tissue quantification algorithm and determined the thresholds for statistically significant change of MRI-derived T2 lesion volume and brain atrophy in individual patients. Twenty patients with MS were scanned twice within 30 min. Brain tissue volumes were measured using the computer algorithm. Brain atrophy was estimated by calculation of brain parenchymal fraction. The threshold of change between repeated scans that represented statistically significant change beyond measurement error with 95% certainty was 0.65 mL for T2 lesion burden and 0.0056 for brain parenchymal fraction. Changes in lesion burden and brain atrophy below these thresholds can be safely (with 95% certainty) explained by measurement variability alone. These values provide clinical neurologists with a useful reference to interpret MRI-derived measures in individual patients.

Due to histological evidence of the fundamental role of the cerebral vessels in white matter abnormalities, recently there has been an increased interest in analyzing the relationship between brain white matter lesions in multiple sclerosis (MS) and brain vasculature. We developed a method for visualization and measurement of geometrical relationships between MS lesions and the brain vessels imaged with magnetic resonance (MR) imaging techniques. Using MR images we create surface models of lesions and vessels that constitute a base for quantitative analysis. In this work we analyze correlation between basic lesion geometrical characteristics and two features: 1) distances to vessels, and 2) vessel caliber. For the former, we compute a distance map from the vessel structure, such that each voxel stores its distance vector to the closest vessel. This allows the measurements of Euclidean distances to the closest vessels. For the latter, we compute a radius map in which each voxel stores the radius of its closest vessel. It is used to measure distribution of lesions with respect to the vessel caliber. We compute and analyze relations between the basic geometrical characteristics of lesions and the closest vessels locations and calibers. To demonstrate the feasibility of the developed technique we present results from the study of 3 MS cases.

Background Natalizumab (NAT) discontinuation leads to multiple sclerosis reactivation. Objective To explore the concept of natalizumab de-escalation to interferon beta-1b (IFNB-1b) compared to continued treatment with NAT. Methods One year, randomized, rater-blinded, parallel-group, pilot study. RRMS patients on NAT for >12 months and clinically stable (free from relapses and disability progression for >6 months, without enhancing lesions [CEL] at baseline MRI) were randomized to NAT or IFNB-1b. Primary endpoint was time to first on-study relapse. Secondary endpoints were frequency of relapse free patients, number of relapses per patient, number of new/enlarging T2 (NT2) lesions and CEL at months 3, 6, 9 and 12 vs baseline (BL). Analyses are based on the intention to treat population. Results Nineteen patients were included (NAT n = 10; IFNB-1b n = 9) having comparable baseline characteristics. Time to first on study relapse showed a trend favouring NAT without reaching significance (p = 0.125, log-rank test). Among secondary clinical and radiological outcomes, the majority showed a trend favouring NAT without reaching significance except for number of NT2 lesions at month 6 being significantly higher in the IFNB-1b group (p = 0.043, U-Mann Whitney). Adverse events were within the expected range. Conclusions Our results suggest no pronounced inflammatory activity after de-escalation of NAT to IFNB-1b and indicate that the concept should be evaluated further.

To suggest a quantitative method for assessing the temporal changes in the geometry of individual multiple sclerosis (MS) lesions in follow-up studies of MS patients.Computer simulated and in vivo magnetic resonance (MR) imaged MS lesions were studied. Ten in vivo MS lesions were identified from sets of axial MR images acquired from a patient scanned consecutively for 24 times during a one-year period. Each of the lesions was segmented and its three-dimensional surface approximated using spherical harmonics (SH). From the obtained SH polynomial coefficients, indices of shape were defined, and analysis of the temporal changes in each lesion's geometry throughout the year was performed by determining the mean discrete total variation of the shape indices.The results demonstrate that most of the studied lesions undergo notable geometrical changes with time. These changes are not necessarily associated with similar changes in size/volume. Furthermore, it was found that indices corresponding to changes in lesion shape could be 1.4 to 8.0 times higher than those corresponding to changes in the lesion size/volume.Quantitative three-dimensional shape analysis can serve as a new tool for monitoring MS lesion activity and study patterns of MS lesion evolution over time.

Although functional MRI (fMRI) has shown to be a tool with great potential to study the normal and diseased human brain, the large variability in the detected hemodynamic responses across sessions and across subjects hinders a wider application. To investigate the long-term reproducibility of fMRI activation of verbal working memory (WM), eight normal subjects performed an auditory version of the 2-back verbal WM task while fMRI images were acquired. The experiment was repeated nine times with the same settings for image acquisition and fMRI task. Data were analyzed using SPM99 program. Single-session activation maps and multi-subject session-specific activation maps were generated. Regions of interest (ROIs) associated to specific components of verbal WM were defined based on the voxels' coordinates in Talairach space. Visual observation of the multi-subject activation maps showed similar activation patterns, and quantitative analysis showed small coefficients of variance of activation within ROIs over time, suggesting small longitudinal variability of activation. Visual observation of the activation maps of individual sessions demonstrated striking variation of activation across sessions and across subjects, and quantitative analysis demonstrated larger contribution from between-subject variation to overall variation than that from within-subject variation. We concluded that by multi-subject analysis of data from a relatively small number of subjects, reasonably reproducible activation for the 2-back verbal WM paradigm can be achieved. The level of reproducibility encourages the application of this fMRI paradigm to the evaluation of cognitive changes in future investigations. The quantitative estimation of the proportions of within-subject and between-subject variabilities in the overall variability may be helpful for the design of future studies.

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.

In progressive neurological disorders, such as multiple sclerosis (MS), magnetic resonance imaging (MRI) follow-up is used to monitor disease activity and progression and to understand the underlying pathogenic mechanisms. This article presents image postprocessing methods and validation for integrating multiple serial MRI scans into a spatiotemporal volume for direct quantitative evaluation of the temporal intensity profiles. This temporal intensity signal and its dynamics have thus far not been exploited in the study of MS pathogenesis and the search for MRI surrogates of disease activity and progression. The integration into a four-dimensional data set comprises stages of tissue classification, followed by spatial and intensity normalization and partial volume filtering. Spatial normalization corrects for variations in head positioning and distortion artifacts via fully automated intensity-based registration algorithms, both rigid and nonrigid. Intensity normalization includes separate stages of correcting intra- and interscan variations based on the prior tissue class segmentation. Different approaches to image registration, partial volume correction, and intensity normalization were validated and compared. Validation included a scan-rescan experiment as well as a natural-history study on MS patients, imaged in weekly to monthly intervals over a 1-year follow-up. Significant error reduction was observed by applying tissue-specific intensity normalization and partial volume filtering. Example temporal profiles within evolving multiple sclerosis lesions are presented. An overall residual signal variance of 1.4% +/- 0.5% was observed across multiple subjects and time points, indicating an overall sensitivity of 3% (for axial dual echo images with 3-mm slice thickness) for longitudinal study of signal dynamics from serial brain MRI.

In order to achieve its main goal of maximal tumor removal while avoiding postoperative neurologic deficits, neuro-oncological surgery is strongly dependent on image guidance. Among all currently available imaging modalities, MRI provides the best anatomic detail and is highly sensitive for intracranial pathology. However, conventional MRI does not detect the exact location of white matter tracts or areas of cortical activation. This essential information can be obtained non-invasively by means of diffusion tensor MRI (DT-MRI) and functional MRI (fMRI) respectively. Here we present our initial experience with fMRI and DT-MRI for surgical planning and guidance in ten brain tumor cases.

Purpose: To assess the reproducibility and accuracy compared to radiologists of three automated segmentation pipelines for quantitative magnetic resonance imaging (MRI) measurement of brain white matter signal abnormalities (WMSA). Materials and Methods: WMSA segmentation was performed on pairs of whole brain scans from 20 patients with multiple sclerosis (MS) and 10 older subjects who were positioned and imaged twice within 30 minutes. Radiologist outlines of WMSA on 20 sections from 16 patients were compared with the corresponding results of each segmentation method. Results: The segmentation method combining expectationmaximization (EM) tissue segmentation, template-driven segmentation (TDS), and partial volume effect correction (PVEC) demonstrated the highest accuracy (the absolute value of the Z-score was 0.99 for both groups of subjects), as well as high interscan reproducibility (repeatability coefficient was 0.68 mL in MS patients and 1.49 mL in aging subjects). Conclusion: The addition of TDS to the EM segmentation and PVEC algorithms significantly improved the accuracy of WMSA volume measurements, while also improving measurement reproducibility.

We present a completely automatic method to identify abnormal anatomical configurations of the brain resulting from various pathologies. The statistical framework developed here is applied to identify regions that significant differ from normal anatomy in two groups of patients, namely subjects who subsequently converted to Alzheimer's Disease (AD) and subjects with mild AD. The regions identified are consistent with post-mortem pathological findings in AD.

Grey matter (GM) damage is a clinically relevant feature of multiple sclerosis (MS) that has been previously assessed with diffusion tensor imaging (DTI). Fractional anisotropy (FA) of the basal ganglia and thalamus might be increased in MS patients, and correlates with disability scores. Despite the established role of the striatum and thalamus in motor control, mood and cognition, the impact of DTI changes within these structures on motor and neuropsychological performance has not yet been specifically addressed in MS. We investigated DTI metrics of deep GM nuclei and their potential association with mobility and neuropsychological function. DTI metrics from 3T MRI were assessed in the caudate, putamen, and thalamus of 30 MS patients and 10 controls. Sixteen of the patients underwent neuropsychological testing. FA of the caudate and putamen was higher in MS patients compared to controls. Caudate FA correlated with Expanded Disability Status Scale score, Ambulation Index, and severity of depressive symptomatology. Putamen and thalamus FA correlated with deficits in memory tests. In contrast, cerebral white matter (WM) lesion burden showed no significant correlation with any of the disability, mobility and psychometric parameters. Our findings support evidence of FA changes in the basal ganglia in MS patients, as well as deep GM involvement in disabling features of MS, including mobility and cognitive impairment. Deep GM FA appears to be a more sensitive correlate of disability than WM lesion burden.

Background: De-escalating natalizumab (NTZ) to interferon beta 1b (IFN B 1B) is a possible treatment option in multiple sclerosis (MS) patients interrupting NTZ because of increased risk of progressive multifocal leukoencephalopathy (PML). The aim of this study was to evaluate satisfaction and adherence to treatment, behavioral and fatigue changes in patients switched to IFN B 1B compared to continued NTZ treatment. Methods: A 1 year, prospective, randomized, rater-blinded, parallel-group study. Nineteen relapsing remitting (RR) MS patients, randomly assigned to undergo either NTZ (n = 10) or IFN B 1B (n = 9) treatment, who had previously received NTZ for at least 12 months with disease stability and fearing or at risk for PML were included. Patients underwent behavioral and treatment assessments at baseline, after 24-week and 1 year follow-up. Behavioral assessment included measures of cognition, fatigue and quality of life. Treatment assessment included measures of satisfaction, persistence and adherence to treatment. Clinical-radiological disease activity and safety were also assessed. Results: Baseline characteristics of patients were similar between groups except for Euro Quality Visual Analogue Scale, being higher in the NTZ group (p = 0.04). Within-group comparisons at the three time points, as well as interaction analysis of treatment effect over time did not show any statistically significant differences in behavioral or treatment assessments, but a coherent trend favoring NTZ over IFN B 1B. Conclusions: De-escalating NTZ to IFN B 1B is feasible and associated with overall good patient related outcome and persistently stable behavioral measures.

Objectives To determine if callosal atrophy and interhemispheric dysfunction can be detected in the early stages of relapsing-remitting multiple sclerosis (MS) and to evaluate their progression in relation to the disability and evolution of lesions seen on magnetic resonance imaging during a 5-year period. Methods We compared 30 patients who had clinically definite early-onset replasing-remitting MS and mild disability with control subjects. Regional and segmental callosal size and extent of white matter abnormalities on magnetic resonance imaging, as well as performance on tasks exploring interhemispheric transfer of motor, auditory, and sensory information were assessed. Patients with MS were evaluated at baseline and after 5 years. Physical disability was determined at both times using the Expanded Disability Status Scale score. Results Patients with MS were seen with significant callosal atrophy and functional impairment of interhemispheric transfer at baseline that worsened during the 5-year study. A significant correlation was found between the magnitude of disability and the severity of morphological and functional callosal involvement at baseline. This association persisted at year 5. Baseline clinical characteristics such as age and prestudy relapse rate were unrelated to callosal size or interhemispheric performance. However, the number of baseline T2-weighted lesions was correlated with callosal involvement and this relation persisted at year 5. Conclusion Patients who had relapsing-remitting MS in the early stages of the disease and mild disability had significant callosal involvement that progressed over time. The relationship between disability, T2-weighted lesions load, and degree of morphological and functional callosal impairment confirm the potential value of using callosal dysfunction as a surrogate marker of disease progression in MS.

A generic algorithm is presented for the segmentation of three-dimensional multispectral magnetic resonance images. The algorithm is unsupervised and adaptive, does not require initialization, classifies the data in any number of tissue classes and suggests an optimal number of classes. It uses a statistical model including Bayesian distributions for brain tissues intensities and Gibbs Random Fields (GRF)-based spatial contiguity constraints. The classification is unsupervised, that is to say the intensity-based signatures of brain tissues and the spatial hyperparameters of the underlying GRF are derived from the data. Adaptivity is achieved through the variation of the size of the neighborhoods used for the estimation of the intensity characteristics. This allows slow variations of signal intensity in space to account for MRI intensity nonuniformity. Segmentation results with proton density, T2 and T1-weighted data are provided. The algorithm can be used as an independent segmentation module within a brain MRI data processing pipeline.

In this paper we describe a new algorithm for nonrigid registration of brain images based on an elastically deformable model. The use of registration methods has become an important tool for computer-assisted diagnosis and surgery. Our goal was to improve analysis in various applications of neurology and neurosurgery by improving nonrigid registration.A local gray level similarity measure is used to make an initial sparse displacement field estimate. The field is initially estimated at locations determined by local features, and then a linear elastic model is used to infer the volumetric deformation across the image. The associated partial differential equation is solved by a finite element approach. A model of empirically observed variability of the brain was created from a dataset of 154 young adults. Both homogeneous and inhomogeneous elasticity models were compared. The algorithm has been applied to medical applications including intraoperative images of neurosurgery showing brain shift and a study of gait and balance disorder.

Ideal treatment planning for irradiation of brain tumors using BNCT involves a combination of temporal and spatial targeting. Spatial targeting, or target volume selection, is accomplished by CT and MR imaging during treatment planning. CT offers the highest quality geometric accuracy and serves as the geometric model for Monte Carlo simulations in our treatment planning system.1 Contrast enhanced MR offers the best description of the tumor and surrounding areas of edema. In addition, in the future, PET scanning may be employed to aid in identifying the target temporally.

To investigate the relationship between magnetic resonance imaging regional lesion burden and cognitive performance in multiple sclerosis (MS) over a 4-year follow-up period.Twenty-eight patients with MS underwent magnetic resonance imaging and took the Brief, Repeatable Battery of Neuropsychological Tests in Multiple Sclerosis at baseline, 1-year, and 4-year follow-up. An automated 3-dimensional lesion detection method was used to identify MS lesions within anatomical regions on proton density T2-weighted images. The relationship between magnetic resonance imaging regional lesion volumes and the Brief, Repeatable Battery of Neuropsychological Tests in Multiple Sclerosis results was examined using regression analyses.At all time points, frontal lesion volume represented the greatest proportion of total lesion volume, and the percentage of white matter classified as lesion was also highest in frontal and parietal regions. On neuropsychological testing, when compared with age- and educational level-matched control subjects, patients with MS showed significant impairment on tests of sustained attention, processing speed, and verbal memory (P.001). Performance on these measures was negatively correlated with MS lesion volume in frontal and parietal regions at baseline, 1-year, and 4-year follow-up (R = -0.55 to -0.73, P.001).Multiple sclerosis lesions show a propensity for frontal and parietal white matter. Lesion burden in these areas was strongly associated with performance on tasks requiring sustained complex attention and working verbal memory. This relationship was consistent over a 4-year period, suggesting that disruption of frontoparietal subcortical networks may underlie the pattern of neuropsychological impairment seen in many patients with MS.

Background We previously described a composite cerebral MRI scale combining T1 lesions, T2 lesions, and whole brain atrophy in multiple sclerosis (MS) patients: the Magnetic Resonance Disease Severity Scale (MRDSS). Objective Test the strength of the MRDSS vs. individual MRI measures for sensitivity to longitudinal change. Methods We prospectively studied 84 patients [age (mean ± SD) 42.6 ± 8.6 years, EDSS score 3.2 ± 2.1, 68% ( n = 57) relapsing–remitting (RR), 24% ( n = 20) secondary progressive (SP), and 8.3% ( n = 7) primary progressive] over a follow-up period of 3.2 ± 0.3 years. Baseline and follow-up brain MRI derived T2 lesion volume (T2LV), T1 hypointense lesion volume (T1LV), and brain parenchymal fraction (BPF). The ratio of T1LV to T2LV assessed lesion severity. MRDSS for each patient was the combination of T2LV, BPF, and T1/T2 ratio. Wilcoxon signed rank test assessed change in each measure over follow-up. Results Patients had higher (worse) MRDSS at follow-up (5.5 ± 2.3) vs. baseline (4.9 ± 2.4; p p p p > 0.5). Change in MRDSS was larger than change in individual MRI components. Although MRDSS showed a significant change in both RR (entry 4.4 ± 2.4 vs. exit 5.2 ± 2.3; p p p p > 0.05). Longitudinal change in MRDSS was significantly different between RR and SP groups ( p = 0.002); however, the change in the individual standardized and unstandardized MRI components did not differ between groups ( p > 0.1). Conclusions These results suggest the improved sensitivity to longitudinal change of MRDSS vs. individual MRI measures of lesions and atrophy in both RR and SP MS. Although MRDSS shows sensitivity in the progressive phase, it has particular sensitivity in the RR stage of disease. MRDSS shows potential as a tool to monitor disease evolution in MS. Support This study was supported by grants from the National Institutes of Health ( R01NS055083-01 ) and the National Multiple Sclerosis Society ( RG3705A1 and RG3798A2 ).

Background: Pediatric multiple sclerosis (MS) is a rare disorder with significant consequences. Quantitative MRI measurements may provide significant insights, however multicenter collaborative studies are needed given the small numbers of subjects. The goal of this study is to demonstrate feasibility and evaluate lesion volume (LV) characteristics in a multicenter cohort of children with MS. Methods: A common MRI-scanning guideline was implemented at six member sites of the U.S. Network of Pediatric MS Centers of Excellence. We included in this study the first ten scans performed at each site on patients meeting the following inclusion criteria: pediatric RRMS within 3 years of disease onset, examination within 1 month of MRI and no steroids 1 month prior to MRI. We quantified T2 number, T2-LV and individual lesion size in a total of 53 MRIs passing quality control procedures and assessed gadolinium-enhancing lesion number and LV in 55 scans. We studied MRI measures according to demographic features including age, race, ethnicity and disability scores, controlling for disease duration and treatment duration using negative binomial regression and linear regression. Results: The mean number of T2 lesions was 24.30 ± 19.68 (range:1–113) and mean gadolinium-enhancing lesion count was 1.85 ± 5.84, (range:0–32). Individual lesion size ranged from 14.31 to 55750.60 mm 3 . Non-white subjects had higher T2–LV (unadjusted pT2-LV= 0.028; adjusted pT2-LV= 0.044), and maximal individual T2-LV (unadjusted pMax = 0.007; adjusted pMax = 0.011) than white patients. We also found a trend toward larger mean lesion size in males than females (p = 0.07). Conclusion: Assessment of MRI lesion LV characteristics is feasible in a multicenter cohort of children with MS. Background Multiple sclerosis (MS) is an increasingly recognized disorder in children and adolescents. The onset of MS prior to the age of 18 occurs in 3-5% of the total MS population [1-3]. Children and adolescents with MS have higher relapse rates than adults with the disease [4], suggesting inflammation as a prominent feature. Children also demonstrate considerable cognitive disability early in the disease course [5-7] but relatively less

This paper presents a new method for the automatic segmentation and characterization of object changes in time series of three-dimensional data sets. The technique was inspired by procedures developed for analysis of functional MRI data sets. After precise registration of serial volume data sets to 4-D data, we applied a time series analysis taking into account the characteristic time function of variable lesions. The images were preprocessed with a correction of image field inhomogeneities and a normalization of the brightness over the whole time series. Thus, static regions remain unchanged over time, whereas changes in tissue characteristics produce typical intensity variations in the voxel’s time series. A set of features was derived from the time series, expressing probabilities for membership to the sought structures. These multiple sources of uncertain evidence were combined to a single evidence value using Dempster–Shafer’s theory. The project was driven by the objective of improving the segmentation and characterization of white matter lesions in serial MR data of multiple sclerosis patients. Pharmaceutical research and patient follow-up requires efficient and robust methods with a high degree of automation. The new approach replaces conventional segmentation of series of 3-D data sets by a 1-D processing of the temporal change at each voxel in the 4-D image data set. The new method has been applied to a total of 11 time series from different patient studies, covering time resolutions of 12 and 24 data sets over a period of about 1 year. The results demonstrate that time evolution is a highly sensitive feature for detection of fluctuating structures.

The development and optimization of spin-echo-based, single-slab, three-dimensional techniques for magnetic resonance imaging of the whole brain are described. T1-weighted and T2-weighted image sets with a volume resolution of 1 mm(3) and fluid-attenuated inversion-recovery image sets with a volume resolution of 3 mm(3) were obtained in acquisition times of less than 10 minutes per image set.

Objective: To study the change in serum levels of soluble intercellular adhesion molecule 1 (sICAM-1) and soluble tumor necrosis factor receptors (sTNF-Rs) in MS patients in relation to clinical disease activity and changes on brain MRI.Background: Circulating forms of adhesion molecules or soluble receptors may be released from cells as a consequence of activation and may be useful markers for inflammation.Methods: During a prospective longitudinal study over 1 year, 40 patients with MS underwent frequent imaging of the brain (22 MR images per patient) at the time of blood sampling as well as monthly neurologic examinations, and scoring on Kurtzke’s Expanded Disability Status Scale (EDSS) and ambulation index (AI).Results: Patients with relapsing–progressive disease had the highest levels of sICAM-1 whereas patients with progressive disease had the highest levels of sTNF-Rs. Fluctuations in sICAM-1 correlated with the occurrence of attacks in patients with relapsing and relapsing–progressive disease. In patients with relapsing–progressive MS, an increase in sICAM-1 level preceded the appearance of new gadolinium (Gd) enhancing lesions on MRI. In patients with progressive disease, an increase in sTNF-R p55 level preceded the appearance of new Gd enhancing lesions on MRI, whereas a decrease in sICAM-1 levels correlated with the appearance of new Gd enhancing lesions.Conclusions: These results demonstrate a linkage between sICAM-1 and sTNF-R levels and disease activity in MS. Furthermore, patients with progressive disease appear to have a different immunologic stage of disease in which immune changes are tightly linked with changes on MRI. The demonstration of a correlation in individual patients between immunologic events and changes in disease activity has implications for monitoring patients undergoing treatment and for monitoring disease progression.

Abnormalities in the white matter of the brain are common to subjects with multiple sclerosis and Alzheimer’s disease. They also develop in normal, asymptomatic, subjects and appear more frequently with age. Clinically, it is interesting to be able to differentiate between different disease states and to find markers which allow early diagnosis. Conventional spin echo (CSE) magnetic resonance imaging (MRI) is sensitive to these white matter changes and has frequently been applied to their study.

We present a new approach for the computation of the deformation field between three dimensional (3D) images. The deformation field minimizes the sum of the squared differences between the images to be matched and is constrained by the physical properties of the different objects represented by the image. The objects are modeled as elastic bodies. Compared to optical flow methods, this approach distinguishes itself by three main characteristics: it can account for the actual physical properties of the objects to be deformed, it can provide us with physical properties of the deformed objects (i.e. stress tensors), and computes a global solution to the deformation instead of a set of local solutions. This latter characteristic is achieved through a finite-element based scheme. The finite element approach requires the different objects in the images to be meshed. Therefore, a tetrahedral mesh generator using a pre-computed case table and specifically suited for segmented images has been developed. Preliminary experiments on simulated data as well as on medical data have been carried out successfully. Tested medical applications included muscle exercise imaging and ventricular deformation in multiple sclerosis.

Simplified MRI prediction of clinically definite multiple sclerosis: a stepping stone towards treatment criteria?

In patients with malignant astrocytomas or metastatic brain disease treated with high-dose radiotherapy, conventional imaging methods may not adequately distinguish recurrent tumor from radiation change. We used a fast spoiled gradient refocusing technique in the open-configuration intraoperative MR system to assess the rate of regional enhancement of the treated tumor bed and to localize specific sites for pathologic sampling to determine whether gadolinium uptake correlated with histologic data. Twenty-four patients were studied. Fourteen of 15 patients with areas of early enhancement had recurrent tumor present in histologic samples, and 8 of the remaining 9 patients had only reactive changes. Dynamic MRI was predictive of recurrent tumor (P < .0005, Fisher exact test and P < .002, Student t test). We conclude that dynamic MRI in the open-bore magnet is a promising method for localizing potential sites of active tumor growth in patients treated for malignant astrocytomas and metastatic brain lesions.

We evaluated brain tissue compartments in 72 healthy volunteers between the ages of 18 and 81 years with quantitative MRI. The intracranial fraction of white matter was significantly lower in the age categories above 59 years. The CSF fraction increased significantly with age, consistent with previous reports. The intracranial percentage of gray matter decreased somewhat with age, but there was no significant difference between the youngest subjects and the subjects above 59. A covariance adjustment for the volume of hyperintensities did not alter the foregoing results. The intracranial percentage of white matter volume was strongly correlated with the percentage volume of CSF. The finding of a highly significant decrease with age in white matter, in the absence of a substantial decrease in gray matter, is consistent with recent neuropathologic reports in humans and nonhuman primates.

This paper presents a new methodology for the automatic segmentation and characterization of object changes in time series of three-dimensional data sets. The purpose of the analysis is a detection and characterization of objects based on their dynamic changes. The technique was inspired by procedures developed for the analysis of functional MRI data sets. After precise registration of serial volume data sets to 4-D data, we applied a new time series analysis taking into account the characteristic time function of variable lesions. The images were preprocessed with a correction of image field inhomogeneities and a normalization of the brightness function over the whole time series. This leads to the hypothesis that static regions remain unchanged over time, whereas local changes in tissue characteristics cause typical functions in the voxel’s time series. A set of features are derived from the time series and their derivatives, expressing probabilities for membership to the sought structures. These multiple sources of uncertain evidence were combined to a single evidence value using Dempster Shafer’s theory. Individual processing of a series of 3-D data sets is therefore replaced by a fully 4-D processing. To explore the sensitivity of time information, active lesions are segmented solely based on time fluctuation, neglecting absolute intensity information.

We provide in this article a generic framework for pose estimation from geometric features. We propose more particularly two algorithms: a gradient descent on the Riemannian least squares distance and on the Mahalanobis distance. For each method, we provide a way to compute the uncertainty of the resulting transformation. The analysis and comparison of the algorithms show their advantages and drawbacks and point out the very good prediction on the transformation accuracy. An application in medical image analysis validates the uncertainty estimation on real data and demonstrates that, using adapted and rigorous tools, we can detect very small modifications in medical images. We believe that these algorithms could be easily embedded in many applications and provide a thorough basis for computing many image statistics.

Objective: To assess the correlation between cognitive dysfunction and disease burden in multiple sclerosis (MS) during a 1-year period. Design: The Brief, Repeatable Battery of Neuropsychological Tests in Multiple Sclerosis was performed at entrance and 1 year. Patients underwent at least 20 proton density (range, 20-24) and T2-weighted axial magnetic resonance imaging (MRI) brain scans except for stable patients who were scanned monthly. Magnetic resonance imaging was evaluated using computer-automated, 3-dimensional volumetric analysis. Setting: A research clinic of a university hospital. Patients: Forty-four patients with MS of the following disease categories: relapsing-remitting (14), relapsingremitting progressive (12), chronic progressive (13), and stable (5). Main Outcome Measures: The relationships between scores on the Brief, Repeatable Battery of Neuropsychological Tests in Multiple Sclerosis and 2 MRI measures (total lesion volume and brain to intracranial cavity volume ratio) were assessed using linear regression. These MRI measures were also compared with cognitive status at 1 year using analysis of variance. Results: Overall, there was no decline in mean cognitive test performance during 1 year. Significant correlations were found between baseline neuropsychological test scores of nonverbal memory, informationprocessing speed, and attention and both MRI measures. Patients with chronic progressive MS demonstrated the strongest correlations. At 1 year, change in information-processing speed and attention correlated with change in total lesion volume. The mean increase in total lesion volume was 5.7 mL for 4 patients whose cognitive status worsened compared with 0.4 mL for 19 patients who improved and 0.5 mL for 21 patients who remained stable. Conclusions: During a 1-year period mean cognitive performance did not worsen. Automated volumetric MRI measures of total lesion volume and brain to intracranial cavity volume ratio correlated with neuropsychological performance, especially in patients with chronic progressive MS. Worsening MRI lesion burden correlated with cognitive decline.