MR imaging intensity modeling of damage and repair in multiple sclerosis: relationship of short-term lesion recovery to progression and disability

Lesion Volume Quantification Using Two Convolutional Neural Networks in MRIs of Multiple Sclerosis Patients

Background: Multiple sclerosis (MS) is a neurologic disease of the central nervous system which affects almost three million people worldwide. MS is characterized by a demyelination process that leads to brain lesions, allowing these affected areas to be visualized with magnetic resonance imaging (MRI). Deep learning techniques, especially computational algorithms based on convolutional neural networks (CNNs), have become a frequently used algorithm that performs feature self-learning and enables segmentation of structures in the image useful for quantitative analysis of MRIs, including quantitative analysis of MS. To obtain quantitative information about lesion volume, it is important to perform proper image preprocessing and accurate segmentation. Therefore, we propose a method for volumetric quantification of lesions on MRIs of MS patients using automatic segmentation of the brain and lesions by two CNNs. Methods: We used CNNs at two different moments: the first to perform brain extraction, and the second for lesion segmentation. This study includes four independent MRI datasets: one for training the brain segmentation models, two for training the lesion segmentation model, and one for testing. Results: The proposed brain detection architecture using binary cross-entropy as the loss function achieved a 0.9786 Dice coefficient, 0.9969 accuracy, 0.9851 precision, 0.9851 sensitivity, and 0.9985 specificity. In the second proposed framework for brain lesion segmentation, we obtained a 0.8893 Dice coefficient, 0.9996 accuracy, 0.9376 precision, 0.8609 sensitivity, and 0.9999 specificity. After quantifying the lesion volume of all patients from the test group using our proposed method, we obtained a mean value of 17,582 mm3. Conclusions: We concluded that the proposed algorithm achieved accurate lesion detection and segmentation with reproducibility corresponding to state-of-the-art software tools and manual segmentation. We believe that this quantification method can add value to treatment monitoring and routine clinical evaluation of MS patients.

Assessment of the Characteristics of Different Kinds of MS Lesions Using Multi-Parametric MRI

Background: Studying different pathological aspects of lesions in multiple sclerosis (MS) patients could be useful to modify the diagnosis and treatment of this neurological disorder. Magnetic resonance imaging (MRI) modalities have the potential to investigate variations in brain tissue because of inflammatory and neurodegenerative processes in various types of MS-related lesions. Objectives: This study was done to investigate the quantitative changes in MRI-based parameters, like perfusion and magnetization transfer ratio (MTR) of different types of brain lesions, to demonstrate the ability of MRI to detect structural and pathological differences in MS lesions. Methods: Quantitative MRI modalities were performed on 18 patients with five different kinds of lesions (T1 holes, acute and chronic white matter (WM), and acute and chronic gray matter (GM) lesions) using a 3 T MRI scanner. The following protocols were used to characterize the pathology of lesions: (I) fluid-attenuated inversion recovery (FLAIR); (II) pre- and post-contrast T1-weighted; (III) dynamic contrast-enhanced (DCE); and (IV) MTR imaging. Quantitative comparison of Ktrans, cerebral blood volume (CBV), cerebral blood flow (CBF), and MTR was done to find the best parameter to distinguish different lesions. Finally, a multivariate classifier was applied to introduce the best parameter to indicate differences in lesions. Results: Five lesions were characterized by perfusion and MTR parameters. The pathological changes were measured, including: (I) the highest value of parameters in both acute WM and GM lesions; (II) the lowest value of four parameters in both chronic WM and GM lesions; (III) MTR had the highest rank among parameters using the classifier. Conclusions: The degree of pathological alterations due to inflammatory and neurodegenerative processes in MS-related lesions was indicated through the used parameters in different kinds of lesions. Inflammation was the dominant process in acute lesions, while neurodegeneration and tissue loss were observed mostly in chronic lesions. Both inflammation and neurodegeneration were detected in T1 holes. Perfusion parameters and MTR were reasonable parameters to describe differences in brain lesions. Thus, it could be confirmed that magnetization transfer imaging (MTI) and DCE-MRI are high-sensitivity methods to detect microstructural changes in the brain and subtle changes in the blood-brain-barrier. Classification of the parameters indicated that MTR was the best biomarker than others to show variations in lesions pathology.

Experimental design and sample size considerations in longitudinal magnetic resonance imaging-based biomarker detection for multiple sclerosis

Several modeling approaches have been developed to quantify differences in multiple sclerosis lesion evolution on magnetic resonance imaging to identify the effect of treatment on disease progression. These studies have limited clinical applicability due to onerous scan frequency and lengthy study duration. Efficient methods are needed to reduce the required sample size, study duration, and sampling frequency in longitudinal magnetic resonance imaging studies. We develop a data-driven approach to identify parameters of study design for evaluation of longitudinal magnetic resonance imaging biomarkers of multiple sclerosis lesion evolution. Our design strategies are considerably shorter than those described in previous studies, thus having the potential to lower costs of clinical trials. From a dataset of 36 multiple sclerosis patients with at least six monthly magnetic resonance imagings, we extracted new lesions and performed principal component analysis to estimate a biomarker that recapitulated lesion recovery. We tested the effect of multiple sclerosis disease modifying therapy on the lesion evolution index in three experimental designs and calculated sample sizes needed to appropriately power studies. Our proposed methods can be used to calculate required sample size and scan frequency in observational studies of multiple sclerosis disease progression as well as in designing clinical trials to find effects of treatment on multiple sclerosis lesion evolution.

Matrix decomposition for modeling lesion development processes in multiple sclerosis

Our main goal is to study and quantify the evolution of multiple sclerosis lesions observed longitudinally over many years in multi-sequence structural magnetic resonance imaging (sMRI). To achieve that, we propose a class of functional models for capturing the temporal dynamics and spatial distribution of the voxel-specific intensity trajectories in all sMRI sequences. To accommodate the hierarchical data structure (observations nested within voxels, which are nested within lesions, which, in turn, are nested within study participants), we use structured functional principal component analysis. We propose and evaluate the finite sample properties of hypothesis tests of therapeutic intervention effects on lesion evolution while accounting for the multilevel structure of the data. Using this novel testing strategy, we found statistically significant differences in lesion evolution between treatment groups.

Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images

We present the application of limited one-time sampling irregularity map (LOTS-IM): a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), for quantitatively assessing white matter hyperintensities (WMH) of presumed vascular origin, and multiple sclerosis (MS) lesions and their progression. LOTS-IM generates an irregularity map (IM) that represents all voxels as irregularity values with respect to the ones considered "normal". Unlike probability values, IM represents both regular and irregular regions in the brain based on the original MRI's texture information. We evaluated and compared the use of IM for WMH and MS lesions segmentation on T2-FLAIR MRI with the state-of-the-art unsupervised lesions' segmentation method, Lesion Growth Algorithm from the public toolbox Lesion Segmentation Toolbox (LST-LGA), with several well established conventional supervised machine learning schemes and with state-of-the-art supervised deep learning methods for WMH segmentation. In our experiments, LOTS-IM outperformed unsupervised method LST-LGA on WMH segmentation, both in performance and processing speed, thanks to the limited one-time sampling scheme and its implementation on GPU. Our method also outperformed supervised conventional machine learning algorithms (i.e., support vector machine (SVM) and random forest (RF)) and deep learning algorithms (i.e., deep Boltzmann machine (DBM) and convolutional encoder network (CEN)), while yielding comparable results to the convolutional neural network schemes that rank top of the algorithms developed up to date for this purpose (i.e., UResNet and UNet). LOTS-IM also performed well on MS lesions segmentation, performing similar to LST-LGA. On the other hand, the high sensitivity of IM on depicting signal change deems suitable for assessing MS progression, although care must be taken with signal changes not reflective of a true pathology.

Developing therapeutic strategies to promote myelin repair in multiple sclerosis

Introduction: Approved disease-modifying therapies for multiple sclerosis (MS) lessen inflammatory disease activity that causes relapses and MRI lesions. However, chronic inflammation and demyelination lead to axonal degeneration and neuronal loss, for which there currently is no effective treatment. There has been increasing interest in developing repair-promoting strategies, but there are important unanswered questions regarding the mechanisms and appropriate methods to evaluate these treatments. Areas covered: The rationale for remyelinating agents in MS is discussed, with an overview of both myelin physiology and endogenous repair mechanisms. This is followed by a discussion of the identification and development of potential remyelinating drugs. Potential biomarkers of remyelination are reviewed, including considerations regarding measuring remyelination in clinical trials. Information and data were obtained from a search of recent literature through PubMed. Peer-reviewed original articles and review articles were included. Expert opinion: There are several obstacles to the translation of potential remyelinating agents to clinical trials, particularly uncertainty regarding the most appropriate study population and method to monitor remyelination. Refinements in clinical trial design and outcome measurement, potentially via advanced imaging techniques, are needed to optimize detection of repair in patients with MS.

Fluid-Attenuated Inversion Recovery Signal Intensity as a Predictor of Gadolinium Enhancement in Relapsing-Remitting Multiple Sclerosis

Background

Magnetic resonance imaging (MRI) is used to diagnose and monitor disease activity in relapsing-remitting multiple sclerosis (RRMS). The objective of this study was to explore the association of "ultrabright" axial fluid-attenuated inversion recovery (FLAIR) lesions with gadolinium enhancement in patients with RRMS using qualitative and quantitative approaches.

Methods

MRIs from patients with RRMS from 2010 to 2015 were reviewed. Two radiologists independently identified ultrabright lesions on axial FLAIR sequences. The contrast-to-noise ratio (CNR) was measured for ultrabright and control lesions.

Results

Of 301 lesions included in the study, 77 (26%) were identified by both radiologists as ultrabright. Interrater agreement was moderate (κ = 0.77, P < .001). Lesions identified by both radiologists as ultrabright demonstrated an association with gadolinium enhancement (χ²₁ = 30.8, P < .001) but were not associated with MRI magnet strength (χ²₁ = 0.24, P = .65). Higher CNR values were associated with gadolinium enhancement for 1.5-T studies (OR, 1.05; 95% CI, 1.02-1.07; P = .001) and 3-T studies (OR, 1.02; 95% CI, 1.02-1.03; P < .001). Diagnostic accuracy of the quantitative model was good for 1.5-T studies (area under the curve, 0.79; 95% CI, 0.68-0.9; P < .001) and 3-T studies (area under the curve, 0.78; 95% CI, 0.73-0.84; P < .001). Positive predictive value of 100% was obtained for CNR values of 92 for 1.5-T and 184 for 3-T studies.

Conclusions

Qualitatively and quantitatively identified ultrabright axial FLAIR lesions are significantly associated with gadolinium enhancement.

Imaging of Central Nervous System Demyelinating Disorders

PURPOSE OF REVIEW

This article focuses on neuroimaging in multiple sclerosis (MS), the most common central nervous system (CNS) demyelinating disorder encountered by practicing neurologists. Less common adult demyelinating disorders and incidental subclinical white matter abnormalities that are often considered in the differential diagnosis of MS are also reviewed.

RECENT FINDINGS

Advancements in neuroimaging techniques, eg, the application of ultrahigh-field MRI, are rapidly expanding the use of neuroimaging in CNS demyelinating disorders. Probably the most important recent findings include the detection of cortical lesions and CNS atrophy even in early stages of MS. The key development for practicing neurologists is the growing impact of MRI on the diagnostic criteria for MS and neuromyelitis optica (NMO) spectrum disorders.

SUMMARY

MRI serves as an important component of the diagnostic criteria for MS and other major CNS demyelinating disorders, and it has been established as a reliable and sensitive indicator of disease activity and progression. In addition, rapidly advancing neuroimaging techniques are helping to improve our understanding of disease pathogenesis.

Assessing Repair in Multiple Sclerosis: Outcomes for Phase II Clinical Trials

Multiple Sclerosis (MS) pathology is complex and includes inflammatory processes, neurodegeneration, and demyelination. While multiple drugs have been developed to tackle MS-related inflammation, to date there is scant evidence regarding which therapeutic approach, if any, could be used to reverse demyelination, foster tissue repair, and thus positively impact on chronic disability. Here, we reviewed the current structural and functional markers (magnetic resonance imaging, positron emission tomography, optical coherence tomography, and visual evoked potentials) which could be used in phase II clinical trials of new compounds aimed to foster tissue repair in MS. Magnetic transfer ratio recovery in newly formed lesions currently represents the most widely used biomarker of tissue repair in MS, even if other markers, such as optical coherence tomography and positron emission tomography hold great promise to complement magnetic transfer ratio in tissue repair clinical trials. Future studies are needed to better characterize the different possible biomarkers to study tissue repair in MS, especially regarding their pathological specificity, sensitivity to change, and their relationship with disease activity.

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.

Imaging of noninfectious inflammatory disorders of the spinal cord

Myelitis, or inflammation of the spinal cord, produces a characteristic clinical syndrome. Among the many causes of myelitis are the prototypical demyelinating diseases multiple sclerosis and neuromyelitis optica, each of which has distinct clinical, pathologic, and radiographic features. Less distinct are the myelitides associated with systemic autoimmune conditions like sarcoidosis and lupus. Nondemyelinating conditions such as arachnoiditis, dural arteriovenous fistula, and tumor infiltration may also produce inflammation of the spinal cord. The objective of this review is to aid the clinician in the radiographic diagnosis of noninfectious inflammatory diseases of the spinal cord.

Multicontrast MRI Quantification of Focal Inflammation and Degeneration in Multiple Sclerosis

INTRODUCTION

Local microstructural pathology in multiple sclerosis patients might influence their clinical performance. This study applied multicontrast MRI to quantify inflammation and neurodegeneration in MS lesions. We explored the impact of MRI-based lesion pathology in cognition and disability.

METHODS

36 relapsing-remitting MS subjects and 18 healthy controls underwent neurological, cognitive, behavioural examinations and 3 T MRI including (i) fluid attenuated inversion recovery, double inversion recovery, and magnetization-prepared gradient echo for lesion count; (ii) T1, T2, and T2(*) relaxometry and magnetisation transfer imaging for lesion tissue characterization. Lesions were classified according to the extent of inflammation/neurodegeneration. A generalized linear model assessed the contribution of lesion groups to clinical performances.

RESULTS

Four lesion groups were identified and characterized by (1) absence of significant alterations, (2) prevalent inflammation, (3) concomitant inflammation and microdegeneration, and (4) prevalent tissue loss. Groups 1, 3, 4 correlated with general disability (Adj-R (2) = 0.6; P = 0.0005), executive function (Adj-R (2) = 0.5; P = 0.004), verbal memory (Adj-R (2) = 0.4; P = 0.02), and attention (Adj-R (2) = 0.5; P = 0.002).

CONCLUSION

Multicontrast MRI provides a new approach to infer in vivo histopathology of plaques. Our results support evidence that neurodegeneration is the major determinant of patients' disability and cognitive dysfunction.

Cerebral lesions of multiple sclerosis: is gadolinium always irreplaceable in assessing lesion activity?

PURPOSE

We aimed to identify imaging characteristics on conventional magnetic resonance imaging that could predict multiple sclerosis (MS) brain lesion activity without contrast media administration.

MATERIALS AND METHODS

Magnetic resonance data sets of forty-two patients with relapsing-remitting MS who presented symptoms or signs suggestive of new disease activity were retrospectively reviewed. We classified the MS lesions into three types according to different patterns present on T2-weighted images and evaluated their relationship with the contrast uptake. Evolving aspects of each type of lesion were observed in 18 patients during a follow-up period ranging from nine to 36 months.

RESULTS

On T2-weighted images, only the pattern consisting of a thin border of decreased intensity compared with the lesion's center and perifocal edema (Type II) reached diagnostic accuracy in terms of its relationship with gadolinium enhancement (P = 0.006). The sensitivity was 0.461, and the specificity was 0.698. In contrast, enhancement was not significantly related to the pattern consisting of a lesion center that was homogeneously brighter than its periphery (Type I) or less-hyperintense T2 focal lesions with either homogeneous or inhomogeneous center (Type III) (P > 0.05 for both).

CONCLUSION

The assessment of MS lesion activity should include a careful evaluation of T2-weighted images in addition to contrast enhancement assessment. The presence of an accompanying peripheral thin rim of hypointensity on T2-weighted images related best with contrast enhancement and subsequent lesion activity and may represent an additional pattern for disease activity assessment when gadolinium examination is contraindicated or influenced by prior therapy.

Automated identification of brain new lesions in multiple sclerosis using subtraction images

PURPOSE

To propose and evaluate a new automated method for the identification of new/enlarging multiple sclerosis (MS) lesions on subtracted images (SI). The subtraction of serially acquired images has shown great potential in assessing new/enlarging brain magnetic resonance imaging (MRI) lesions in MS patients. However, this approach relies on the manual definition of lesions, which is labor-intensive and subject to operator-dependent variability.

MATERIALS AND METHODS

An overestimated mask of candidate SI lesions was created and then these hyperintense voxel clusters were filtered using specific constraints for extent, shape, and intensity. The method was tested on normal and pathological MRI datasets.

RESULTS

The automated method did not detect hyperintense voxels on SI of healthy controls. SI lesions were identified manually and automatically in a multicenter MS dataset of 19 patients with paired MRI over 36 weeks. Sensitivity of the method was high (0.91) and in agreement with the results of manually defined SI lesions (Cohen's k=0.82,95% confidence interval [CI]: 0.77–0.87). On a second multicenter MS dataset of 103 patients with paired MRI over 76 weeks, the number of SI lesions detected automatically correlated with the number of gadolinium-enhancing lesions(r=0.74).

CONCLUSION

The proposed method is robust, accurate,and sensitive and may be used with confidence in Phase II MS trials.

Imaging small vessel-associated white matter changes in aging

Alterations in cerebrovascular structure and function may underlie the most common age-associated cognitive, psychiatric, and neurological conditions presented by older adults. Although much remains to understand, existing research suggests several age-associated detrimental conditions may be mediated through sometimes subtle small vessel-induced damage to the cerebral white matter. Here we review a selected portion of the vast work that demonstrates links between changes in vascular and neural health as a function of advancing age, and how even changes in low-to-moderate risk individuals, potentially beginning early in the adult age-span, may have an important impact on functional status in late life.

Quantitative MRI analysis in children with multiple sclerosis: a multicenter feasibility pilot study

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 mm3. 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.

Magnetic resonance monitoring of lesion evolution in multiple sclerosis

Disease activity in multiple sclerosis (MS) is strongly linked to the formation of new lesions, which involves a complex sequence of inflammatory, degenerative, and reparative processes. Conventional magnetic resonance imaging (MRI) techniques, such as T2-weighted and gadolinium-enhanced T1-weighted sequences, are highly sensitive in demonstrating the spatial and temporal dissemination of demyelinating plaques in the brain and spinal cord. Hence, these techniques can provide quantitative assessment of disease activity in patients with MS, and they are commonly used in monitoring treatment efficacy in clinical trials and in individual cases. However, the correlation between conventional MRI measures of disease activity and the clinical manifestations of the disease, particularly irreversible disability, is weak. This has been explained by a process of exhaustion of both structural and functional redundancies that increasingly prevents repair and recovery, and by the fact that these imaging techniques do not suffice to explain the entire spectrum of the disease process and lesion development. Nonconventional MRI techniques, such as magnetization transfer imaging, diffusion-weighted imaging, and proton magnetic resonance spectroscopy, which can selectively measure the more destructive aspects of MS pathology and monitor the reparative mechanisms of this disease, are increasingly being used for serial analysis of new lesion formation and provide a better approximation of the pathological substrate of MS plaques. These nonconventional MRI-based measures better assess the serial changes in newly forming lesions and improve our understanding of the relationship between the damaging and reparative mechanisms that occur in MS.

Reliability of longitudinal brain volume loss measurements between 2 sites in patients with multiple sclerosis: comparison of 7 quantification techniques

BACKGROUND AND PURPOSE

Brain volume loss is currently a MR imaging marker of neurodegeneration in MS. Available quantification algorithms perform either direct (segmentation-based techniques) or indirect (registration-based techniques) measurements. Because there is no reference standard technique, the assessment of their accuracy and reliability remains a difficult goal. Therefore, the purpose of this work was to assess the robustness of 7 different postprocessing algorithms applied to images acquired from different MR imaging systems.

MATERIALS AND METHODS

Nine patients with MS were followed longitudinally over 1 year (3 time points) on two 1.5T MR imaging systems. Brain volume change measures were assessed using 7 segmentation algorithms: a segmentation-classification algorithm, FreeSurfer, BBSI, KN-BSI, SIENA, SIENAX, and JI algorithm.

RESULTS

Intersite variability showed that segmentation-based techniques and SIENAX provided large and heterogeneous values of brain volume changes. A Bland-Altman analysis showed a mean difference of 1.8%, 0.07%, and 0.79% between the 2 sites, and a wide length agreement interval of 11.66%, 7.92%, and 11.94% for the segmentation-classification algorithm, FreeSurfer, and SIENAX, respectively. In contrast, registration-based algorithms showed better reproducibility, with a low mean difference of 0.45% for BBSI, KN-BSI and JI, and a mean length agreement interval of 1.55%. If SIENA obtained a lower mean difference of 0.12%, its agreement interval of 3.29% was wider.

CONCLUSIONS

If brain atrophy estimation remains an open issue, future investigations of the accuracy and reliability of the brain volume quantification algorithms are needed to measure the slow and small brain volume changes occurring in MS.

A new metric for detecting change in slowly evolving brain tumors: validation in meningioma patients

BACKGROUND

Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies.

OBJECTIVE

This article describes a semiautomatic monitoring approach using longitudinal medical images. We test the method on brain scans of patients with meningioma, which experts have found difficult to monitor because the tumor evolution is very slow and may be obscured by artifacts related to image acquisition.

METHODS

We describe a semiautomatic procedure targeted toward identifying difficult-to-detect changes in brain tumor imaging. The tool combines input from a medical expert with state-of-the-art technology. The software is easy to calibrate and, in less than 5 minutes, returns the total volume of tumor change in mm. We test the method on postgadolinium, T1-weighted magnetic resonance images of 10 patients with meningioma and compare our results with experts' findings. We also perform benchmark testing with synthetic data.

RESULTS

Our experiments indicated that experts' visual inspections are not sensitive enough to detect subtle growth. Measurements based on experts' manual segmentations were highly accurate but also labor intensive. The accuracy of our approach was comparable to the experts' results. However, our approach required far less user input and generated more consistent measurements.

CONCLUSION

The sensitivity of experts' visual inspection is often too low to detect subtle growth of meningiomas from longitudinal scans. Measurements based on experts' segmentation are highly accurate but generally too labor intensive for standard clinical settings. We described an alternative metric that provides accurate and robust measurements of subtle tumor changes while requiring a minimal amount of user input.

Seasonal prevalence of MS disease activity

OBJECTIVE

This observational cohort study investigated the seasonal prevalence of multiple sclerosis (MS) disease activity (likelihood and intensity), as reflected by new lesions from serial T2-weighted MRI, a sensitive marker of subclinical disease activity.

METHODS

Disease activity was assessed from the appearance of new T2 lesions on 939 separate brain MRI examinations in 44 untreated patients with MS. Likelihood functions for MS disease activity were derived, accounting for the temporal uncertainty of new lesion occurrence, individual levels of disease activity, and uneven examination intervals. Both likelihood and intensity of disease activity were compared with the time of year (season) and regional climate data (temperature, solar radiation, precipitation) and among relapsing and progressive disease phenotypes. Contrast-enhancing lesions and attack counts were also compared for seasonal effects.

RESULTS

Unlike contrast enhancement or attacks, new T2 activity revealed a likelihood 2-3 times higher in March-August than during the rest of the year, and correlated strongly with regional climate data, in particular solar radiation. In addition to the likelihood or prevalence, disease intensity was also elevated during the summer season. The elevated risk season appears to lessen for progressive MS and occur about 2 months earlier.

CONCLUSION

This study documents evidence of a strong seasonal pattern in subclinical MS activity based on noncontrast brain MRI. The observed seasonality in MS disease activity has implications for trial design and therapy assessment. The observed activity pattern is suggestive of a modulating role of seasonally changing environmental factors or season-dependent metabolic activity.

Novel MRI approaches to assess patients with multiple sclerosis

PURPOSE OF REVIEW

This review summarizes novel MRI approaches for the investigation of lesion burden and understanding of the pathophysiology of multiple sclerosis (MS).

RECENT FINDINGS

Recent technical advances are improving our ability to detect and define the nature of focal lesions and 'diffuse' tissue damage in MS as well as the functional consequences of such structural abnormalities. New contrast agents allow to monitor the pluriformity of MS inflammation. Double inversion recovery sequences enable us to detect and monitor the evolution of MS lesions in the cortex. High and ultra-high field scanners are improving imaging of MS-related abnormalities at an unprecedented resolution. Furthermore, this new generation of scanners has the potential to ameliorate structural and functional MR studies of the disease. All of this has contributed, and is likely to continue to contribute, to the definition of the factors associated with the development of irreversible disability in MS. Finally, new analysis methods have allowed to track regional disease-related changes and are resulting in an increased correlation between MRI and clinical deficits.

SUMMARY

Novel MR approaches highlighted previously unrecognized or neglected aspects of MS pathophysiology, which are likely to improve our understanding of the heterogeneous clinical manifestations of this condition.

Disease modeling in multiple sclerosis: assessment and quantification of sources of variability in brain parenchymal fraction measurements

The measurement of brain atrophy from magnetic resonance imaging (MRI) has become an established method of estimating disease severity and progression in multiple sclerosis (MS). Most commonly reported in the form of brain parenchymal fraction (BPF), it is more sensitive to the degenerative component of the disease and shows progression more reliably than lesion burden. Typically, the reliability of BPF and other morphometric measurements is assessed by evaluating scan-rescan experiments. While these experiments provide good estimates of real-life error related to imperfect patient repositioning in the MRI scanner, measurement variance due to physiological and reversible pathological fluctuations in brain volume are not taken into account. In this work, we propose a new model for estimating variability in serial morphometry, particularly the BPF measurement. Specifically, we attempt to detect and explicitly model the remaining sources of error to more accurately describe the overall variability in BPF measurements. Our results show that sources of variability beyond subject repositioning error are important and cannot be ignored. We demonstrate that scan-rescan experiments only provide a lower bound on the true error in repeated measurements of patients' BPF. We have estimated the variance due to patient repositioning during scan-rescan (sigma(sr)(2) = 3.0e-06), variance assigned to physiological fluctuations (sigma(p)(2) = 5.74e-06) and the variance associated with lesion activity (sigma(les)(2) = 1.09e-05). These variance components can be used to determine the relative impact of their sources on sample size estimates for studies investigating change over time in MS patients. Our results demonstrate that sample size calculations based exclusively on scan-rescan variability (sigma(sr)) are likely to underestimate the number of patients required. If the physiological variability (sigma(p)) is incorporated in sample size calculations, the required sample size would increase by a factor of 5.69 based on standard t-test sample size calculation.

MRI in multiple sclerosis: current status and future prospects

Many promising MRI approaches for research or clinical management of multiple sclerosis (MS) have recently emerged, or are under development or refinement. Advanced MRI methods need to be assessed to determine whether they allow earlier diagnosis or better identification of phenotypes. Improved post-processing should allow more efficient and complete extraction of information from images. Magnetic resonance spectroscopy should improve in sensitivity and specificity with higher field strengths and should enable the detection of a wider array of metabolites. Diffusion imaging is moving closer to the goal of defining structural connectivity and, thereby, determining the functional significance of lesions at specific locations. Cell-specific imaging now seems feasible with new magnetic resonance contrast agents. The imaging of myelin water fraction brings the hope of providing a specific measure of myelin content. Ultra-high-field MRI increases sensitivity, but also presents new technical challenges. Here, we review these recent developments in MRI for MS, and also look forward to refinements in spinal-cord imaging, optic-nerve imaging, perfusion MRI, and functional MRI. Advances in MRI should improve our ability to diagnose, monitor, and understand the pathophysiology of MS.

Longitudinal imaging: change and causality

PURPOSE OF REVIEW

Longitudinal studies that use MRI scans performed over multiple time-points have been increasingly employed in the study of different neurological disorders, including degenerative dementia, multiple sclerosis, and epilepsy.

RECENT FINDINGS

Although it is well established that increased rates of brain atrophy occur in degenerative dementia and multiple sclerosis, recent data have further described these changes and demonstrated that they correlate with both cognitive and functional decline. Advanced voxel-level techniques have also provided detailed descriptions of regional patterns of change, and a few studies have started to investigate changes over multiple MRI enabling the trajectories of brain loss over time to be determined. Researchers have also started to more thoroughly investigate the underlying causes of brain atrophy. Correlations have been observed between rate of brain atrophy and the presence of abnormal protein deposits in the brain in dementia, and the lesion burden in multiple sclerosis. However, longitudinal studies on epilepsy have been inconsistent, with very little recent data.

SUMMARY

Recent data further support the suggestion that longitudinal MRI provides a good biomarker of disease progression in dementia and multiple sclerosis, though more work needs to be performed to define the role of longitudinal imaging in epilepsy.