Exploring the discrimination power of the time domain for segmentation and characterization of active lesions in serial MR data

An automated methodology for whole-body, multimodality tracking of individual cancer lesions

Objective. Manual analysis of individual cancer lesions to assess disease response is clinically impractical and requires automated lesion tracking methodologies. However, no methodology has been developed for whole-body individual lesion tracking, across an arbitrary number of scans, and acquired with various imaging modalities.Approach. This study introduces a lesion tracking methodology and benchmarked it using 2368Ga-DOTATATE PET/CT and PET/MR images of eight neuroendocrine tumor patients. The methodology consists of six steps: (1) alignment of multiple scans via image registration, (2) body-part labeling, (3) automatic lesion-wise dilation, (4) clustering of lesions based on local lesion shape metrics, (5) assignment of lesion tracks, and (6) output of a lesion graph. Registration performance was evaluated via landmark distance, lesion matching accuracy was evaluated between each image pair, and lesion tracking accuracy was evaluated via identical track ratio. Sensitivity studies were performed to evaluate the impact of lesion dilation (fixed versus automatic dilation), anatomic location, image modalities (inter- versus intra-modality), registration mode (direct versus indirect registration), and track size (number of time-points and lesions) on lesion matching and tracking performance.Main results. Manual contouring yielded 956 lesions, 1570 lesion-matching decisions, and 493 lesion tracks. The median residual registration error was 2.5 mm. The automatic lesion dilation led to 0.90 overall lesion matching accuracy, and an 88% identical track ratio. The methodology is robust regarding anatomic locations, image modalities, and registration modes. The number of scans had a moderate negative impact on the identical track ratio (94% for 2 scans, 91% for 3 scans, and 81% for 4 scans). The number of lesions substantially impacted the identical track ratio (93% for 2 nodes versus 54% for ≥5 nodes).Significance. The developed methodology resulted in high lesion-matching accuracy and enables automated lesion tracking in PET/CT and PET/MR.

An open-source tool for longitudinal whole-brain and white matter lesion segmentation

In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that can handle multi-contrast data and robustly analyze images with white matter lesions. This method is here extended with subject-specific latent variables that encourage temporal consistency between its segmentation results, enabling it to better track subtle morphological changes in dozens of neuroanatomical structures and white matter lesions. We validate the proposed method on multiple datasets of control subjects and patients suffering from Alzheimer's disease and multiple sclerosis, and compare its results against those obtained with its original cross-sectional formulation and two benchmark longitudinal methods. The results indicate that the method attains a higher test-retest reliability, while being more sensitive to longitudinal disease effect differences between patient groups. An implementation is publicly available as part of the open-source neuroimaging package FreeSurfer.

Two Time Point MS Lesion Segmentation in Brain MRI: An Expectation-Maximization Framework

Purpose: Lesion volume is a meaningful measure in multiple sclerosis (MS) prognosis. Manual lesion segmentation for computing volume in a single or multiple time points is time consuming and suffers from intra and inter-observer variability. Methods: In this paper, we present MSmetrix-long: a joint expectation-maximization (EM) framework for two time point white matter (WM) lesion segmentation. MSmetrix-long takes as input a 3D T1-weighted and a 3D FLAIR MR image and segments lesions in three steps: (1) cross-sectional lesion segmentation of the two time points; (2) creation of difference image, which is used to model the lesion evolution; (3) a joint EM lesion segmentation framework that uses output of step (1) and step (2) to provide the final lesion segmentation. The accuracy (Dice score) and reproducibility (absolute lesion volume difference) of MSmetrix-long is evaluated using two datasets. Results: On the first dataset, the median Dice score between MSmetrix-long and expert lesion segmentation was 0.63 and the Pearson correlation coefficient (PCC) was equal to 0.96. On the second dataset, the median absolute volume difference was 0.11 ml. Conclusions: MSmetrix-long is accurate and consistent in segmenting MS lesions. Also, MSmetrix-long compares favorably with the publicly available longitudinal MS lesion segmentation algorithm of Lesion Segmentation Toolbox.

Longitudinal Patch-Based Segmentation of Multiple Sclerosis White Matter Lesions

Segmenting T2-hyperintense white matter lesions from longitudinal MR images is essential in understanding progression of multiple sclerosis. Most lesion segmentation techniques find lesions independently at each time point, even though there are different noise and image contrast variations at each point in the time series. In this paper, we present a patch based 4D lesion segmentation method that takes advantage of the temporal component of longitudinal data. For each subject with multiple time-points, 4D patches are constructed from the T₁-w and FLAIR scans of all time-points. For every 4D patch from a subject, a few relevant matching 4D patches are found from a reference, such that their convex combination reconstructs the subject's 4D patch. Then corresponding manual segmentation patches of the reference are combined in a similar manner to generate a 4D membership of lesions of the subject patch. We compare our 4D patch-based segmentation with independent 3D voxel-based and patch-based lesion segmentation algorithms. Based on ground truth segmentations from 30 data sets, we show that the mean Dice coefficients between manual and automated segmentations improve after using the 4D approach compared to two state-of-the-art 3D segmentation algorithms.

Temporally consistent probabilistic detection of new multiple sclerosis lesions in brain MRI

Detection of new Multiple Sclerosis (MS) lesions on magnetic resonance imaging (MRI) is important as a marker of disease activity and as a potential surrogate for relapses. We propose an approach where sequential scans are jointly segmented, to provide a temporally consistent tissue segmentation while remaining sensitive to newly appearing lesions. The method uses a two-stage classification process: 1) a Bayesian classifier provides a probabilistic brain tissue classification at each voxel of reference and follow-up scans, and 2) a random-forest based lesion-level classification provides a final identification of new lesions. Generative models are learned based on 364 scans from 95 subjects from a multi-center clinical trial. The method is evaluated on sequential brain MRI of 160 subjects from a separate multi-center clinical trial, and is compared to 1) semi-automatically generated ground truth segmentations and 2) fully manual identification of new lesions generated independently by nine expert raters on a subset of 60 subjects. For new lesions greater than 0.15 cc in size, the classifier has near perfect performance (99% sensitivity, 2% false detection rate), as compared to ground truth. The proposed method was also shown to exceed the performance of any one of the nine expert manual identifications.

Recommendations to improve imaging and analysis of brain lesion load and atrophy in longitudinal studies of multiple sclerosis

Focal lesions and brain atrophy are the most extensively studied aspects of multiple sclerosis (MS), but the image acquisition and analysis techniques used can be further improved, especially those for studying within-patient changes of lesion load and atrophy longitudinally. Improved accuracy and sensitivity will reduce the numbers of patients required to detect a given treatment effect in a trial, and ultimately, will allow reliable characterization of individual patients for personalized treatment. Based on open issues in the field of MS research, and the current state of the art in magnetic resonance image analysis methods for assessing brain lesion load and atrophy, this paper makes recommendations to improve these measures for longitudinal studies of MS. Briefly, they are (1) images should be acquired using 3D pulse sequences, with near-isotropic spatial resolution and multiple image contrasts to allow more comprehensive analyses of lesion load and atrophy, across timepoints. Image artifacts need special attention given their effects on image analysis results. (2) Automated image segmentation methods integrating the assessment of lesion load and atrophy are desirable. (3) A standard dataset with benchmark results should be set up to facilitate development, calibration, and objective evaluation of image analysis methods for 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.

Supervised automatic procedure to identify new lesions in brain MR longitudinal studies of patients with multiple sclerosis

PURPOSE

Identification of new enhancing lesions is a major endpoint of longitudinal brain magnetic resonance (MR) studies of multiple sclerosis (MS). To date, this is a visual, time-consuming procedure. We present here a supervised automated procedure (SAP) aimed at reducing the time needed to identify new MS enhancing lesions.

MATERIALS AND METHODS

The SAP uses an algorithm including Cartesian coordinates of the lesions to be compared, their area and a constant (k). The procedure was validated for enhancing lesions on T1-weighted spin-echo images after intravenous administration of 0.1 mmol/kg of paramagnetic contrast agent, randomly selected from a dataset of a longitudinal MR study on ten relapsing-remitting MS patients followed for 2-5 years. During the validation session, two readers decided by consensus whether two lesions, present on the same slice of two examinations performed on subsequent dates, were the same or not. In this way, k was calibrated to obtain the same result from both visual inspection and automatic algorithm output.

RESULTS

After evaluating of 25+/-5 (mean+/-standard deviation) lesions in each of ten different sessions with correction of k value, the k value became a stable value (0.45+/-0.05).

CONCLUSIONS

Once the suitable value of k was found, SAP was able to identify new enhancing lesions, avoiding visual inspection, which is usually a lengthy procedure.

Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine

RATIONALE AND OBJECTIVES

Brain lesions, especially white matter lesions (WMLs), are associated with cardiac and vascular disease, but also with normal aging. Quantitative analysis of WML in large clinical trials is becoming more and more important.

MATERIALS AND METHODS

In this article, we present a computer-assisted WML segmentation method, based on local features extracted from multiparametric magnetic resonance imaging (MRI) sequences (ie, T1-weighted, T2-weighted, proton density-weighted, and fluid attenuation inversion recovery MRI scans). A support vector machine classifier is first trained on expert-defined WMLs, and is then used to classify new scans.

RESULTS

Postprocessing analysis further reduces false positives by using anatomic knowledge and measures of distance from the training set.

CONCLUSIONS

Cross-validation on a population of 35 patients from three different imaging sites with WMLs of varying sizes, shapes, and locations tests the robustness and accuracy of the proposed segmentation method, compared with the manual segmentation results from two experienced neuroradiologists.

Population based analysis of directional information in serial deformation tensor morphometry

Deformation morphometry provides a sensitive approach to detecting and mapping subtle volume changes in the brain. Population based analyses of this data have been used successfully to detect characteristic changes in different neurodegenerative conditions. However, most studies have been limited to statistical mapping of the scalar volume change at each point in the brain, by evaluating the determinant of the Jacobian of the deformation field. In this paper we describe an approach to spatial normalisation and analysis of the full deformation tensor. The approach employs a spatial relocation and reorientation of tensors of each subject. Using the assumption of small changes, we use a linear modeling of effects of clinical variables on each deformation tensor component across a population. We illustrate the use of this approach by examining the pattern of significance and orientation of the volume change effects in recovery from alcohol abuse. Results show new local structure which was not apparent in the analysis of scalar volume changes.

Whole brain voxel-wise analysis of single-subject serial DTI by permutation testing

Diffusion tensor MRI (DTI) has been widely used to investigate brain microstructural changes in pathological conditions as well as for normal development and aging. In particular, longitudinal changes are vital to the understanding of progression but these studies are typically designed for specific regions of interest. To analyze changes in these regions traditional statistical methods are often employed to elucidate group differences which are measured against the variability found in a control cohort. However, in some cases, rather than collecting multiple subjects into two groups, it is necessary and more informative to analyze the data for individual subjects. There is also a need for understanding changes in a single subject without prior information regarding the spatial distribution of the pathology, but no formal statistical framework exists for these voxel-wise analyses of DTI. In this study, we present PERVADE (permutation voxel-wise analysis of diffusion estimates), a whole brain analysis method for detecting localized FA changes between two separate points in time of any given subject, without any prior hypothesis about where changes might occur. Exploiting the nature of DTI that it is calculated from multiple diffusion-weighted images of each region, permutation testing, a non-parametric hypothesis testing technique, was modified for the analysis of serial DTI data and implemented for voxel-wise hypothesis tests of diffusion metric changes, as well as for suprathreshold cluster analysis to correct for multiple comparisons. We describe PERVADE in detail and present results from Monte Carlo simulation supporting the validity of the technique as well as illustrative examples from a healthy subject and patients in the early stages of multiple sclerosis.

Part 1. Automated change detection and characterization in serial MR studies of brain-tumor patients

The goal of this study was to create an algorithm which would quantitatively compare serial magnetic resonance imaging studies of brain-tumor patients. A novel algorithm and a standard classify-subtract algorithm were constructed. The ability of both algorithms to detect and characterize changes was compared using a series of digital phantoms. The novel algorithm achieved a mean sensitivity of 0.87 (compared with 0.59 for classify-subtract) and a mean specificity of 0.98 (compared with 0.92 for classify-subtract) with regard to identification of voxels as changing or unchanging and classification of voxels into types of change. The novel algorithm achieved perfect specificity in seven of the nine experiments. The novel algorithm was additionally applied to a short series of clinical cases, where it was shown to identify visually subtle changes. Automated change detection and characterization could facilitate objective review and understanding of serial magnetic resonance imaging studies in brain-tumor patients.

Segmentation of brain tumors in 4D MR images using the hidden Markov model

Tumor size is an objective measure that is used to evaluate the effectiveness of anticancer agents. Responses to therapy are categorized as complete response, partial response, stable disease and progressive disease. Implicit in this scheme is the change in the tumor over time; however, most tumor segmentation algorithms do not use temporal information. Here we introduce an automated method using probabilistic reasoning over both space and time to segment brain tumors from 4D spatio-temporal MRI data. The 3D expectation-maximization method is extended using the hidden Markov model to infer tumor classification based on previous and subsequent segmentation results. Spatial coherence via a Markov Random Field was included in the 3D spatial model. Simulated images as well as patient images from three independent sources were used to validate this method. The sensitivity and specificity of tumor segmentation using this spatio-temporal model is improved over commonly used spatial or temporal models alone.

Deformation-based mapping of volume change from serial brain MRI in the presence of local tissue contrast change

This paper is motivated by the analysis of serial structural magnetic resonance imaging (MRI) data of the brain to map patterns of local tissue volume loss or gain over time, using registration-based deformation tensor morphometry. Specifically, we address the important confound of local tissue contrast changes which can be induced by neurodegenerative or neurodevelopmental processes. These not only modify apparent tissue volume, but also modify tissue integrity and its resulting MRI contrast parameters. In order to address this confound we derive an approach to the voxel-wise optimization of regional mutual information (RMI) and use this to drive a viscous fluid deformation model between images in a symmetric registration process. A quantitative evaluation of the method when compared to earlier approaches is included using both synthetic data and clinical imaging data. Results show a significant reduction in errors when tissue contrast changes locally between acquisitions. Finally, examples of applying the technique to map different patterns of atrophy rate in different neurodegenerative conditions is included.

An application of the Dempster-Shafer theory of evidence to the classification of knee function and detection of improvement due to total knee replacement surgery

This paper utilises a novel method for the classification of subjects with osteoarthritic and normal knee function. The classification method comprises a number of different components. Firstly, the method exploits the Dempster-Shafer theory of evidence allowing for a degree of ignorance in the subject's classification, i.e., a level of uncertainty as to whether a gait variable indicates osteoarthritis or not. Secondly, the inclusion of simplex plots allows both the classification of a subject, and the contribution of each associated gait variable to that classification, to be represented visually. As a result, the method is further able to highlight periodic changes in a subject's knee function due to total knee replacement surgery and subsequent recovery. The visual representation enables a simple clinical interpretation of the results from the quantitative analysis.

Ranking the 'balance' of state long-term care systems: a comparative exposition of the SMARTER and CaRBS Techniques

The use of attribute sets to rank units of health provision (e.g., states, organizations) against policy goals is an essential task within decision-making and analysis. This paper elucidates and compares two techniques, SMARTER (Simple Multiattribute Rating Technique Exploiting Ranks) and CaRBS (Classification and Ranking Belief Simplex), within an expositional ranking of US states' long-term care (LTC) systems against the policy goal of providing a balance between (traditionally dominant) institutional care, and alternative home and community-based services (HCBS). While the (more established) SMARTER technique is used primarily for comparative purposes, greater emphasis is placed on elucidating CaRBS which is based on the Dempster-Shafer theory of evidence. It is shown that CaRBS offers four appealing features for health policy analysis: (1) the capacity to rank using either of two confidence measures (DST-related belief and plausibility values), (2) a systematic approach to managing missing data, (3) the production of stable rankings, and (4) the simplex plot method of data representation. In addition to discussing the LTC policy implications of the study findings, the issues of rank order stability and the management of missing data are discussed with respect to the two techniques employed.

A review of the automated detection of change in serial imaging studies of the brain

Serial imaging is frequently performed on patients with diseases of the brain, to track and observe changes. Magnetic resonance imaging provides very detailed and rich information, and is therefore used frequently for this application. The data provided by MR can be so plentiful; however, that it obfuscates the information the radiologist seeks. A system which could reduce the large quantity of primitive data to a smaller and more informative subset of data, emphasizing change, would be useful. This article discusses motivating factors for the production of an automated process to this effect, and reviews the approaches of previous authors. The discussion is focused on brain tumors and multiple sclerosis, but many of the ideas are applicable to other disease processes, as well.

Multispectral analysis of bone lesions in the hands of patients with rheumatoid arthritis

Quantitative measures of rheumatoid arthritis (RA) disease progression can provide valuable tools for evaluation of new treatments during clinical trials. In this study, a novel multispectral (MS) MRI analysis method is presented to quantify changes in bone lesion volume (DeltaBLV) in the hands of RA patients. Image registration and MS analysis were employed to identify MS tissue class transitions between two serial MRI exams. DeltaBLV was determined from MS class transitions between two time points. The following three classifiers were investigated: (a) multivariate Gaussian (MVG), (b) k-nearest neighbor (k-NN), and (c) K-means (KM). Unlike supervised classifiers (MVG, k-NN), KM, an unsupervised classifier, does not require labeled training data, resulting in potentially greater clinical utility. All MS estimates of DeltaBLV were linearly correlated (r(p)) with manual estimates. KM and k-NN estimates also exhibited a significant rank-order correlation (r(s)) with manual estimates. For KM, r(p) = 0.94 p < 0.0001, r(s) = 0.76 p = 0.002; for k-NN, r(p) = 0.86 p = 0.0001, r(s) = 0.69 p = 0.009; and for MVG, r(p) = 0.84 p = 0.0003, r(s) = 0.49 p = 0.09. Temporal classification rates were as follows: for KM, 90.1%; for MVG, 89.5%; and for k-NN, 86.7%. KM matched the performance of k-NN, offering strong potential for use in multicenter clinical trials. This study demonstrates that MS tissue class transitions provide a quantitative measure of DeltaBLV.

An object-based approach for detecting small brain lesions: application to Virchow-Robin spaces

This paper is concerned with the detection of multiple small brain lesions from magnetic resonance imaging (MRI) data. A model based on the marked point process framework is designed to detect Virchow-Robin spaces (VRSs). These tubular shaped spaces are due to retraction of the brain parenchyma from its supplying arteries. VRS are described by simple geometrical objects that are introduced as small tubular structures. Their radiometric properties are embedded in a data term. A prior model includes interactions describing the clustering property of VRS. A Reversible Jump Markov Chain Monte Carlo algorithm (RJMCMC) optimizes the proposed model, obtained by multiplying the prior and the data model. Example results are shown on T1-weighted MRI datasets of elderly subjects.

Time-series analysis of MRI intensity patterns in multiple sclerosis

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.

Method for quantifying volumetric lesion change in interval liver CT examinations

We propose a method of using a relatively low degree of freedom (DOF) warping to accurately measure the interval change of lesions having homogeneous contrast. The setting presented here presupposes the use of interval computed tomography (CT) liver exams. After a 3 x 24 DOF warping of the later examination to match the liver's pose in the earlier exam of the interval pair is performed, the lesion's volume change is estimated using the computed difference volume of the two data sets via a novel method that counts partial volume contributions and is insensitive to slight misregistration. A mathematically generated phantom is used to quantify accuracy in the presence of noise. We also quantify the accuracy of our CT liver registrations using microcoils implanted for chemotherapy. A probabilistic liver atlas is used to support automatic masking and liver-focused registration.

Incorporating a measure of local scale in voxel-based 3-D image registration

We present a new class of approaches for rigid-body registration and their evaluation in studying multiple sclerosis (MS) via multiprotocol magnetic resonance imaging (MRI). Three pairs of rigid-body registration algorithms were implemented, using cross-correlation and mutual information (MI), operating on original gray-level images, and utilizing the intermediate images resulting from our new scale-based method. In the scale image, every voxel has the local "scale" value assigned to it, defined as the radius of the largest ball centered at the voxel with homogeneous intensities. Three-dimensional image data of the head were acquired from ten MS patients for each of six MRI protocols. Images in some of the protocols were acquired in registration. The registered pairs were used as ground truth. Accuracy and consistency of the six registration methods were measured within and between protocols for known amounts of misregistrations. Our analysis indicates that there is no "best" method. For medium misregistration, the method using MI, for small add large misregistration the method using normalized cross-correlation performs best. For high-resolution data the correlation method and for low-resolution data the MI method, both using the original gray-level images, are the most consistent. We have previously demonstrated the use of local scale information in fuzzy connectedness segmentation and image filtering. Scale may also have potential for image registration as suggested by this work.

Automatic detection and segmentation of evolving processes in 3D medical images: Application to multiple sclerosis

The study of temporal series of medical images can be helpful for physicians to perform pertinent diagnoses and to help them in the follow-up of a patient: in some diseases, lesions, tumors or anatomical structures vary over time in size, position, composition, etc., either because of a natural pathological process or under the effect of a drug or a therapy. It is a laborious and subjective task to visually and manually analyze such images. Thus the objective of this work was to automatically detect regions with apparent local volume variation with a vector field operator applied to the local displacement field obtained after a non-rigid registration between two successive temporal images. On the other hand, quantitative measurements, such as the volume variation of lesions or segmentation of evolving lesions, are important. By studying the information of apparent shrinking areas in the direct and reverse displacement fields between images, we are able to segment evolving lesions. Then we propose a method to segment lesions in a whole temporal series of images. In this article we apply this approach to automatically detect and segment multiple sclerosis lesions that evolve in time series of MRI scans of the brain. At this stage, we have only applied the approach to a few experimental cases to demonstrate its potential. A clinical validation remains to be done, which will require important additional work.

Magnetic resonance imaging of multiple sclerosis: new insights linking pathology to clinical evolution

Magnetic resonance imaging methods allow observation of pathological changes in vivo. Magnetic resonance-based studies have provided a number of important insights into the spatio-temporal evolution of the pathology of multiple sclerosis in vivo, particularly with respect to the relation between pathology and progression of disability. Magnetic resonance techniques have shown that this pathology is not restricted to the plaques that are evident at autopsy, but also involve the so-called normal-appearing white matter. Nonconventional magnetic resonance imaging strategies such as magnetization transfer imaging and spectroscopic imaging provide measures with higher pathological specificity for myelin and axonal injury. These and other advanced magnetic resonance techniques (such as the measurement of atrophy, lesion relaxation spectra, and lesion dynamics) are affording opportunities to use observations of patients to test biologically specific hypotheses. This should help us to better define new targets for drug therapy and to assess responses to new therapeutic agents.