Quantitative follow-up of patients with multiple sclerosis using MRI: reproducibility

A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in multiple sclerosis

Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients. The method integrates a novel model for white matter lesions into a previously validated generative model for whole-brain segmentation. By using separate models for the shape of anatomical structures and their appearance in MRI, the algorithm can adapt to data acquired with different scanners and imaging protocols without retraining. We validate the method using four disparate datasets, showing robust performance in white matter lesion segmentation while simultaneously segmenting dozens of other brain structures. We further demonstrate that the contrast-adaptive method can also be safely applied to MRI scans of healthy controls, and replicate previously documented atrophy patterns in deep gray matter structures in MS. The algorithm is publicly available as part of the open-source neuroimaging package FreeSurfer.

Longitudinal analysis of white matter and cortical lesions in multiple sclerosis

Purpose

The goals of this study were to assess the performance of a novel lesion segmentation tool for longitudinal analyses, as well as to validate the generated lesion progression map between two time points using conventional and non-conventional MR sequences.

Material and methods

The lesion segmentation approach was evaluated with (LeMan-PV) and without (LeMan) the partial volume framework using “conventional” and “non-conventional” MR imaging in a two-year follow-up prospective study of 32 early RRMS patients. Manual segmentations of new, enlarged, shrunken, and stable lesions were used to evaluate the performance of the method variants. The true positive rate was estimated for those lesion evolutions in both white matter and cortex. The number of false positives was compared with two strategies for longitudinal analyses. New lesion tissue volume estimation was evaluated using Bland-Altman plots. Wilcoxon signed-rank test was used to evaluate the different setups.

Results

The best median of the true positive rate was obtained using LeMan-PV with non-conventional sequences (P < .05): 87%, 87%, 100%, 83%, for new, enlarged, shrunken, and stable WM lesions, and 50%, 60%, 50%, 80%, for new, enlarged, shrunken, and stable cortical lesions, respectively. Most of the missed lesions were below the mean lesion size in each category. Lesion progression maps presented a median of 0 false positives (range:0–9) and the partial volume framework improved the volume estimation of new lesion tissue.

Conclusion

LeMan-PV exhibited the best performance in the detection of new, enlarged, shrunken and stable WM lesions. The method showed lower performance in the detection of cortical lesions, likely due to their low occurrence, small size and low contrast with respect to surrounding tissues. The proposed lesion progression map might be useful in clinical trials or clinical routine.

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DIR and MP2RAGE sequences improve automated longitudinal assessment of MS lesions.

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Partial volume estimation improves lesion segmentation in a longitudinal scenario.

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Lesion progression maps simplify the assessment of disease activity and treatment response.

[Development of a computer-aided diagnostic system for detecting multiple sclerosis using magnetic resonance images]

It is of key importance to be able to evaluate the temporal changes seen in multiple sclerosis (MS) lesions in terms of location, shape, and area for estimating MS progression. The purpose of our study was to develop an automated method for detecting potential MS regions based on three types of brain magnetic resonance (MR) images: T1- and T2-weighted images, and fluid attenuated inversion-recovery (FLAIR) images. The brain regions were segmented based on a tri-linear interpolation technique and k-mean clustering technique. True positive regions and false positive regions were classified from three types of MR images using a support vector machine (SVM). We applied our proposed method to 60 slices of 20 MS cases. As a result, the sensitivity for detection of MS regions was 81.8%, with 14.1% false positives per true positive. This method should prove useful for the diagnosis of multiple sclerosis.

Central nervous system imaging in diabetic cerebrovascular diseases and white matter hyperintensities

Diabetes mellitus is an important vascular risk factor for cerebrovascular disease. This occurs through pathophysiologic changes to the microcirculation as arteriolosclerosis and to the macrocirculation as large artery atherosclerosis. Imaging techniques can provide detailed visualization of the cerebrovasculature using CT (computed tomography) angiography and MR (magnetic resonance) angiography. Newer techniques focused on advanced parenchymal imaging include CT perfusion, quantitative MRI, and diffusion tensor imaging; each identifies brain lesion burden due to diabetes mellitus. These imaging approaches have provided insights into the diabetes mellitus brain and cerebral circulation pathophysiology. Imaging has taught us that diabetics develop cerebral atrophy, silent infarcts, and white matter disease more rapidly than other patient populations. Longitudinal studies are needed to quantify the rate and extent of such structural brain and blood vessel changes and how they relate to cognitive decline. Diabetes prevention and treatment strategies will then be possible to slow the development of such changes.

Reproducibility of scan prescription in follow-up brain MRI: manual versus automatic determination

In follow-up brain magnetic resonance imaging (MRI), precise reproducibility of the scan prescription is important so that over- or underestimating changes in volumes of clinical interest is prevented. (The scan prescription is defined as the location and orientation of the head with respect to the scan planes of the three-dimensional MRI matrix.) In this study, the misregistration between the original and a second scan was calculated in the case of both manual positioning and automatic positioning. These calculations were carried out both for a healthy volunteer scanned repeatedly and, in a retrospective study, for 225 patients who had an original and at least one follow-up scan. The effects of the scan operator being the same for both scans or being different were also examined. A commercially available 1.5 Tesla MRI system and a six-element head-array coil were employed in all of the imaging. The reproducibility of the scan prescription was determined by the registration of the original scan image to the follow-up scan image by use of the Fourier phase correlation method. Our results showed that (1) the reproducibility by automatic positioning was superior to that by manual positioning (p < 0.05), and (2) there was no significant difference in the results between when the operator was the same or different (p > 0.05). We conclude that, in follow-up brain MRI, automatic positioning should be used, because manual positioning decreases the reproducibility of the scan prescription even if the same operator performs the second scan.

Current status and future perspectives of magnetic resonance high-field imaging: a summary

There are several magnetic resonance (MR) imaging techniques that benefit from high-field MR imaging. This article describes a range of novel techniques that are currently being used clinically or will be used in the future for clinical purposes as they gain popularity. These techniques include functional MR imaging, diffusion tensor imaging, cortical thickness assessment, arterial spin labeling perfusion, white matter hyperintensity lesion assessment, and advanced MR angiography.

Magnetic Resonance Image Analysis for Brain CAD Systems with Machine Learning

This chapter describes the image analysis for brain Computer-Aided Diagnosis (CAD) systems with machine learning techniques, which could assist radiologists in the detection of such brain diseases as asymptomatic unruptured aneurysms, Alzheimer’s Disease (AD), vascular dementia, and Multiple Sclerosis (MS) by magnetic resonance imaging. Image analysis in CAD systems consists of image enhancement, initial detection, and image feature extraction, including segmentation. In addition, the authors review the classification of true and false positives using machine learning techniques, as well as the evaluation methods and development cycle for CAD systems.

REGION SIMILARITY RELATIONSHIP BETWEEN WATERSHED AND PENALIZED FUZZY HOPFIELD NEURAL NETWORK ALGORITHMS FOR BRAIN IMAGE SEGMENTATION

A robust image segmentation method that combines the watershed segmentation and penalized fuzzy Hopfield neural network (PFHNN) algorithms to minimize undesirable over-segmentation is described in this paper. This method incorporates spatial graph representation derived from the watershed segmented regions and cluster analysis information obtained from the PFHNN algorithm to achieve efficient image segmentation. The proposed scheme employs the Markov random field (MRF) model on the region adjacency graph (RAG) to improve the quality of watershed segmentation. Here, the fusion criterion is according to the correlation coefficient, which uses inter-region similarities to determine the merging of regions. Analysis of the performance of the proposed technique is presented through quantitative and qualitative validation experiments on benchmark images, and significant and promising segmentation results are presented using brain phantom simulated data.

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.

Regional white matter atrophy--based classification of multiple sclerosis in cross-sectional and longitudinal data

BACKGROUND AND PURPOSE

The different clinical subtypes of multiple sclerosis (MS) may reflect underlying differences in affected neuroanatomic regions. Our aim was to analyze the effectiveness of jointly using the inferior subolivary medulla oblongata volume (MOV) and the cross-sectional area of the corpus callosum in distinguishing patients with relapsing-remitting multiple sclerosis (RRMS), secondary-progressive multiple sclerosis (SPMS), and primary-progressive multiple sclerosis (PPMS).

MATERIALS AND METHODS

We analyzed a cross-sectional dataset of 64 patients (30 RRMS, 14 SPMS, 20 PPMS) and a separate longitudinal dataset of 25 patients (114 MR imaging examinations). Twelve patients in the longitudinal dataset had converted from RRMS to SPMS. For all images, the MOV and corpus callosum were delineated manually and the corpus callosum was parcellated into 5 segments. Patients from the cross-sectional dataset were classified as RRMS, SPMS, or PPMS by using a decision tree algorithm with the following input features: brain parenchymal fraction, age, disease duration, MOV, total corpus callosum area and areas of 5 segments of the corpus callosum. To test the robustness of the classification technique, we applied the results derived from the cross-sectional analysis to the longitudinal dataset.

RESULTS

MOV and central corpus callosum segment area were the 2 features retained by the decision tree. Patients with MOV >0.94 cm(3) were classified as having RRMS. Patients with progressive MS were further subclassified as having SPMS if the central corpus callosum segment area was <55.12 mm(2), and as having PPMS otherwise. In the cross-sectional dataset, 51/64 (80%) patients were correctly classified. For the longitudinal dataset, 88/114 (77%) patient time points were correctly classified as RRMS or SPMS.

CONCLUSIONS

Classification techniques revealed differences in affected neuroanatomic regions in subtypes of multiple sclerosis. The combination of central corpus callosum segment area and MOV provides good discrimination among patients with RRMS, SPMS, and PPMS.

An MRI study of age-related white and gray matter volume changes in the rhesus monkey

We applied the automated MRI segmentation technique Template Driven Segmentation (TDS) to dual-echo spin echo (DE SE) images of eight young (5-12 years), six middle-aged (16-19 years) and eight old (24-30 years) rhesus monkeys. We analyzed standardized mean volumes for 18 anatomically defined regions of interest (ROI's) and found an overall decrease from young to old age in the total forebrain (5.01%), forebrain parenchyma (5.24%), forebrain white matter (11.53%), forebrain gray matter (2.08%), caudate nucleus (11.79%) and globus pallidus (18.26%). Corresponding behavioral data for five of the young, five of the middle-aged and seven of the old subjects on the Delayed Non-matching to Sample (DNMS) task, the Delayed-recognition Span Task (DRST) and the Cognitive Impairment Index (CII) were also analyzed. We found that none of the cognitive measures were related to ROI volume changes in our sample size of monkeys.

Medulla oblongata volume: a biomarker of spinal cord damage and disability in multiple sclerosis

BACKGROUND AND PURPOSE

While brain MR imaging is routinely performed, the MR imaging assessment of spinal cord pathology in multiple sclerosis (MS) is less frequent in clinical practice. The purpose of this study was to determine whether measurements of medulla oblongata volume (MOV) on routine brain MR imaging could serve as a biomarker of spinal cord damage and disability in MS.

MATERIALS AND METHODS

We identified 45 patients with MS with both head and cervical spinal cord MR imaging and 29 age-matched and sex-matched healthy control subjects with head MR imaging. Disability was assessed by the expanded disability status scale (EDSS) and ambulation index (AI). MOV and upper cervical cord volume (UCCV) were manually segmented; semiautomated segmentation was used for brain parenchymal fraction (BPF). These measures were compared between groups, and linear regression models were built to predict disability.

RESULTS

In the patients, MOV correlated significantly with UCCV (r = 0.67), BPF (r = 0.45), disease duration (r = -0.64), age (r = -0.47), EDSS score (r = -0.49) and AI (r = -0.52). Volume loss of the medulla oblongata was -0.008 cm(3)/year of age in patients with MS, but no significant linear relationship with age was found for healthy control subjects. The patients had a smaller MOV (mean +/- SD, 1.02 +/- 0.17 cm(3)) than healthy control subjects (1.15 +/- 0.15 cm(3)), though BPF was unable to distinguish between these 2 groups. MOV was smaller in patients with progressive MS (secondary- progressive MS, 0.88 +/- 0.19 cm(3) and primary-progressive MS, 0.95 +/- 0.30 cm(3)) than in patients with relapsing-remitting MS (1.08 +/- 0.15 cm(3)). A model including both MOV and BPF better predicted AI than BPF alone (P = .04). Good reproducibility in MOV measurements was demonstrated for intrarater (intraclass correlation coefficient, 0.97), interrater (0.79), and scan rescan data (0.81).

CONCLUSION

MOV is associated with disability in MS and can serve as a biomarker of spinal cord damage.

A rhesus monkey reference label atlas for template driven segmentation

BACKGROUND

We have acquired dual-echo spin-echo (DE SE) MRI data of the rhesus monkey brain since 1994 as part of an ongoing study of normal aging. To analyze these legacy data for regional volume changes, we have created a reference label atlas for the Template Driven Segmentation (TDS) algorithm.

METHODS

The atlas was manually created from DE SE legacy MRI data of one behaviorally normal, young, male rhesus monkey and consisted of 14 regions of interest (ROI's). We analyzed the reproducibility and validity of the TDS algorithm using the atlas relative to manual segmentation.

RESULTS

ROI volumes were comparable between the two segmentation methodologies, except TDS overestimated the volume of basal ganglia regions. Both methodologies were highly reproducible, but TDS had lower sensitivity and comparable specificity.

CONCLUSIONS

TDS segmentation calculates accurate volumes for most ROI's. Sensitivity will be improved in future studies through the acquisition of higher quality data.

Segmentation of subtraction images for the measurement of lesion change in multiple sclerosis

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.

Lesion identification using unified segmentation-normalisation models and fuzzy clustering

In this paper, we propose a new automated procedure for lesion identification from single images based on the detection of outlier voxels. We demonstrate the utility of this procedure using artificial and real lesions. The scheme rests on two innovations: First, we augment the generative model used for combined segmentation and normalization of images, with an empirical prior for an atypical tissue class, which can be optimised iteratively. Second, we adopt a fuzzy clustering procedure to identify outlier voxels in normalised gray and white matter segments. These two advances suppress misclassification of voxels and restrict lesion identification to gray/white matter lesions respectively. Our analyses show a high sensitivity for detecting and delineating brain lesions with different sizes, locations, and textures. Our approach has important implications for the generation of lesion overlap maps of a given population and the assessment of lesion-deficit mappings. From a clinical perspective, our method should help to compute the total volume of lesion or to trace precisely lesion boundaries that might be pertinent for surgical or diagnostic purposes.

Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and Markov random field model

In this paper, an approach is proposed for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. The proposed approach, based on a Bayesian classifier, utilizes the adaptive mixtures method (AMM) and Markov random field (MRF) model to obtain and upgrade the class conditional probability density function (CCPDF) and the a priori probability of each class. To compare the performance of the proposed approach with those of previous approaches including manual segmentation, the similarity criteria of different slices related to 20 MS patients were calculated. Also, volumetric comparison of lesions volume between the fully automated segmentation and the gold standard was performed using correlation coefficient (CC). The results showed a better performance for the proposed approach, compared to those of previous works.

Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI

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.

Multiple sclerosis medical image analysis and information management

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.

Current concepts of analysis of cerebral white matter hyperintensities on magnetic resonance imaging

Cerebrovascular disease is common and associated with cognitive deficits and increased risk for dementia. Until recently, only limited attention has focused on advances in imaging techniques to better define and quantify the spectrum of asymptomatic cerebrovascular disease commonly seen on magnetic resonance imaging, such as abnormal white matter signals. Abnormal signals in cerebral white matter, although nonspecific, are increased in prevalence and severity in association with aging and cerebrovascular risk factors among older individuals. The ubiquitous occurrence of these abnormal white matter signals commonly referred to as white matter hyperintensities (WMHs) and the association with cerebrovascular risk and cognitive impairment among older individuals make scientific evaluation of WMHs an important and much needed avenue of research. In this section, we review current methods of WMH analysis. Strengths and limitation of both quantitative and qualitative methods are discussed initially, followed by a brief review of current magnetic resonance imaging segmentation and mapping techniques that make it possible to assess the anatomical location of WMHs. We conclude by discussing future analytic methods designed to better understand the pathophysiology and cognitive consequences of WMHs.

A randomized blinded trial of combination therapy with cyclophosphamide in patients-with active multiple sclerosis on interferon beta

OBJECTIVE

To evaluate the efficacy and safety of combination therapy with pulse cyclophosphamide given with methylprednisolone (MP) and interferon beta (IFNbeta)-Ia in multiple sclerosis (MS) patients with active disease during IFNbeta monotherapy.

METHODS

This was a randomized, single-blind, parallel-group, multicenter trial in MS patients with a history of active disease during IFNbeta treatment. Patients were randomized to either cyclophosphamide 800 mg/m2 plus methylprednisolone 1 g IV (CY/MP) or methylprednisolone once a month for six months and then followed for an additional 18 months. All patients received three days of methylprednisolone 1 g IV at screening and 30 mcg IFNbeta-Ia IM weekly for the entire 24 months. The primary endpoint was change from baseline in the mean number of gadolinium-enhancing (Gd+) lesions. Secondary clinical endpoints included time to treatment failure.

RESULTS

Fifty-nine patients were randomized to treatment: 30 to CY/MP and 29 to MP Change from baseline in the number of Gd+ lesions was significantly different between treatment groups at three (P =0.01), six (P =0.04) and 12 months (P =0.02), with fewer lesions in the CY/MP group. The cumulative rate of treatment failure was significantly lower in the CY/MP group compared with the MP group (rate ratio =0.30; 95% confidence interval, 0.12-0.75; P =0.011). CY/MP treatment was well tolerated.

CONCLUSION

Combination therapy with CY/MP and IFNbeta-Ia decreased the number of Gd+ lesions and slowed clinical activity in patients with previously active disease on IFNbeta alone.

Probabilistic segmentation of brain tissue in MR imaging

A new method has been developed for probabilistic segmentation of five different types of brain structures: white matter, gray matter, cerebro-spinal fluid without ventricles, ventricles and white matter lesion in cranial MR imaging. The algorithm is based on information from T1-weighted (T1-w), inversion recovery (IR), proton density-weighted (PD), T2-weighted (T2-w) and fluid attenuation inversion recovery (FLAIR) scans. It uses the K-Nearest Neighbor classification technique that builds a feature space from spatial information and voxel intensities. The technique generates for each tissue type an image representing the probability per voxel being part of it. By application of thresholds on these probability maps, binary segmentations can be obtained. A similarity index (SI) and a probabilistic SI (PSI) were calculated for quantitative evaluation of the results. The influence of each image type on the performance was investigated by alternately leaving out one of the five scan types. This procedure showed that the incorporation of the T1-w, PD or T2-w did not significantly improve the segmentation results. Further investigation indicated that the combination of IR and FLAIR was optimal for segmentation of the five brain tissue types. Evaluation with respect to the gold standard showed that the SI-values for all tissues exceeded 0.8 and all PSI-values exceeded 0.7, implying an excellent agreement.

Fully automatic segmentation of white matter hyperintensities in MR images of the elderly

The role of quantitative image analysis in large clinical trials is continuously increasing. Several methods are available for performing white matter hyperintensity (WMH) volume quantification. They vary in the amount of the human interaction involved. In this paper, we describe a fully automatic segmentation that was used to quantify WMHs in a large clinical trial on elderly subjects. Our segmentation method combines information from 3 different MR images: proton density (PD), T2-weighted and fluid-attenuated inversion recovery (FLAIR) images; our method uses an established artificial intelligent technique (fuzzy inference system) and does not require extensive computations. The reproducibility of the segmentation was evaluated in 9 patients who underwent scan-rescan with repositioning; an inter-class correlation coefficient (ICC) of 0.91 was obtained. The effect of differences in image resolution was tested in 44 patients, scanned with 6- and 3-mm slice thickness FLAIR images; we obtained an ICC value of 0.99. The accuracy of the segmentation was evaluated on 100 patients for whom manual delineation of WMHs was available; the obtained ICC was 0.98 and the similarity index was 0.75. Besides the fact that the approach demonstrated very high volumetric and spatial agreement with expert delineation, the software did not require more than 2 min per patient (from loading the images to saving the results) on a Pentium-4 processor (512 MB RAM).

Accrual of MRI white matter abnormalities in elderly with normal and impaired mobility

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.

Quantification of multiple sclerosis lesion load and brain tissue volumetry using multiparameter MRI: methodology and reproducibility

Quantitative characterization of multiple sclerosis (MS) lesion load is of considerable interest to clinical follow-up studies. Based on fuzzy clustering of multiparameter magnetic resonance images, we have developed a computer-assisted system for volumetric quantification of brain tissue. Tests on patient data show that the system is very efficient, and volumetric measurements characterized are highly reproducible. The high reproducibility and efficiency offer the potential of routine laboratory and clinical use for quantification of MS lesion load.

Has your patient's multiple sclerosis lesion burden or brain atrophy actually changed?

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 within individual 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.

Preliminary analysis of a trial of pulse cyclophosphamide in IFN-beta-resistant active MS

This was a randomized, single-blind, parallel-group, multicenter trial in MS patients with a history of active disease during IFN-beta treatment. Patients were randomized to either cyclophosphamide 800 mg/m2 plus methylprednisolone 1 g IV (CY/MP) or methylprednisolone (MP) once monthly for 6 months then followed for an additional 18 months. All patients received IFN-beta1a for the 24-month study period. The primary endpoint was mean change from baseline in the number of gadolinium-enhancing (Gd+) lesions. Secondary endpoints included the percentage of patients with Gd+ lesions, change in T2 lesion burden, change in brain parenchymal fraction (BPF), time to treatment failure, and cumulative probability of relapse. Safety was assessed by the incidence of adverse events and the results of blood and urine testing. A higher number of patients completed the study in the pulse cyclophosphamide group because approximately half as many of these patients became treatment failures (26% vs. 52%, p = 0.03). During the infusion phase, the mean number of Gd+ lesions declined 70-80% from baseline in the CY group vs. a small increase in MP (p = 0.02 and 0.04 at 3 and 6 months). We conclude that pulse cyclophosphamide appears to be well tolerated in combination with IFN-beta1a. Pulse cyclophosphamide decreases the number of Gd+ lesions in patients with active disease on IFN-beta compared to pulse methylprednisolone alone. Six doses of pulse cyclophosphamide in combination with IFN-beta1a both prevent and delay clinical disease activity in patients with previously active disease on IFN-beta alone. Pulse cyclophosphamide is a therapeutic option as rescue therapy for patients thought to be interferon non-responders.

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

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

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.

Statistical validation of image segmentation quality based on a spatial overlap index

RATIONALE AND OBJECTIVES

To examine a statistical validation method based on the spatial overlap between two sets of segmentations of the same anatomy.

MATERIALS AND METHODS

The Dice similarity coefficient (DSC) was used as a statistical validation metric to evaluate the performance of both the reproducibility of manual segmentations and the spatial overlap accuracy of automated probabilistic fractional segmentation of MR images, illustrated on two clinical examples. Example 1: 10 consecutive cases of prostate brachytherapy patients underwent both preoperative 1.5T and intraoperative 0.5T MR imaging. For each case, 5 repeated manual segmentations of the prostate peripheral zone were performed separately on preoperative and on intraoperative images. Example 2: A semi-automated probabilistic fractional segmentation algorithm was applied to MR imaging of 9 cases with 3 types of brain tumors. DSC values were computed and logit-transformed values were compared in the mean with the analysis of variance (ANOVA).

RESULTS

Example 1: The mean DSCs of 0.883 (range, 0.876-0.893) with 1.5T preoperative MRI and 0.838 (range, 0.819-0.852) with 0.5T intraoperative MRI (P < .001) were within and at the margin of the range of good reproducibility, respectively. Example 2: Wide ranges of DSC were observed in brain tumor segmentations: Meningiomas (0.519-0.893), astrocytomas (0.487-0.972), and other mixed gliomas (0.490-0.899).

CONCLUSION

The DSC value is a simple and useful summary measure of spatial overlap, which can be applied to studies of reproducibility and accuracy in image segmentation. We observed generally satisfactory but variable validation results in two clinical applications. This metric may be adapted for similar validation tasks.

Probabilistic segmentation of white matter lesions in MR imaging

A new method has been developed for fully automated segmentation of white matter lesions (WMLs) in cranial MR imaging. The algorithm uses information from T1-weighted (T1-w), inversion recovery (IR), proton density-weighted (PD), T2-weighted (T2-w) and fluid attenuation inversion recovery (FLAIR) scans. It is based on the K-Nearest Neighbor (KNN) classification technique that builds a feature space from voxel intensities and spatial information. The technique generates images representing the probability per voxel being part of a WML. By application of thresholds on these probability maps, binary segmentations can be obtained. ROC curves show that the segmentations achieve both high sensitivity and specificity. A similarity index (SI), overlap fraction (OF) and extra fraction (EF) are calculated for additional quantitative analysis of the result. The SI is also used for determination of the optimal probability threshold for generation of the binary segmentation. Using probabilistic equivalents of the SI, OF and EF, the probability maps can be evaluated directly, providing a powerful tool for comparison of different classification results. This method for automated WML segmentation reaches an accuracy that is comparable to methods for multiple sclerosis (MS) lesion segmentation and is suitable for detection of WMLs in large and longitudinal population studies.

Interscan registration using navigator echoes

A common problem in clinical MRI is anatomic misalignment of imaging slices across successive examinations. This unnecessarily complicates the radiologic assessment of anatomic change over time on serial MRI studies. To address this problem, spherical navigator echoes, which can detect rigid body motion in all six degrees of freedom, were used to guide spatial location and orientation adjustments to an exam prescription to match the reference frame of images acquired in an earlier exam. An initial linear navigator echo is also necessary to effect coarse Z translation adjustments prior to fine six degrees of freedom adjustment with a spherical navigator echo. Results of this technique are presented for head image volumes of five volunteers. Each volunteer was imaged on two scanners. In all cases, the reference frame adjustments provided by the navigator echoes substantially improved the alignment of the latter exam and performed well compared to retrospective image-based registration.

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.

Three-dimensional analysis of the geometry of individual multiple sclerosis lesions: detection of shape changes over time using spherical harmonics

PURPOSE

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.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSION

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.

Quantitative image analysis: software systems in drug development trials

Multi-dimensional image analysis is being used increasingly to arrive at surrogate end-points for drug development trials. Various imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) and ultrasound are used to analyze treatments for diseases such as cancer, multiple sclerosis, osteoarthritis, and Alzheimer's disease. However, extracting information from images can be tedious and is prone to high user variability. The medical image analysis community is moving towards advanced software systems specifically designed for drug development trials. These systems can automatically identify the anatomy of interest in medical images (segmentation methods), can compare the anatomy over time or between patients (registration methods) and allow the quantitative extraction of anatomical features and the integration of the data and results into a database management system, automatically tracking the changes made to the data (audit trail generation). In this article, we present a case study using a prototype system that is used for quantifying multiple sclerosis lesions from multivariate MRI.

Accuracy and reproducibility of manual and semiautomated quantification of MS lesions by MRI

PURPOSE

To evaluate the accuracy, reproducibility, and speed of two semiautomated methods for quantifying total white matter lesion burden in multiple sclerosis (MS) patients with respect to manual tracing and to other methods presented in recent literature.

MATERIALS AND METHODS

Two methods involving the use of MRI for semiautomated quantification of total lesion burden in MS patients were examined. The first method, geometrically constrained region growth (GEORG), requires user specification of lesion location. The second technique, directed multispectral segmentation (DMSS), requires only the location of a single exemplar lesion. Test data sets included both clinical MS data and MS brain phantoms.

RESULTS

The mean processing times were 60 minutes for manual tracing, 10 minutes for region growth, and 3 minutes for directed segmentation. Intra- and interoperator coefficients of variation (CVs) were 5.1% and 16.5% for manual tracing, 1.4% and 2.3% for region growth, and 1.5% and 5.2% for directed segmentation. The average deviations from manual tracing were 9% for region growth and 5.7% for directed segmentation.

CONCLUSION

Both semiautomated methods were shown to have a significant advantage over manual tracing in terms of speed and precision. The accuracy of both methods was acceptable, given the high variability of the manual results.

MRI contrast uptake in new lesions in relapsing-remitting MS followed at weekly intervals

BACKGROUND

One of the diagnostic imaging hallmarks of MS is the uptake of IV administered contrast material in new lesions in the brain, signaling blood-brain barrier breakdown and active inflammation. Many clinical drug trials are designed based on the assumption that lesion enhancement on MRI remains visible on average for 1 month. For practical reasons, few serial MRI studies of patients with MS have been performed at intervals shorter than 4 weeks.

METHODS

The authors performed a year-long longitudinal study in 26 patients with relapsing-remitting MS (RRMS), which comprised an initial phase of MRI follow-up at weekly intervals for 8 weeks, followed by imaging every other week for another 16 weeks, and monthly thereafter. They present a quantitative analysis (using a supervised interactive thresholding procedure) of new enhancing lesions appearing during the first 6 weeks in this cohort and evaluated from the time of first detection until enhancement was no longer seen.

RESULTS

The average duration of Gd-DTPA enhancement in individual new lesions was 3.07 weeks (median, 2 weeks). Significant correlations were demonstrated between the duration of contrast enhancement or initial growth rates and lesion volumes. Different lesions in the same patient appeared to develop largely independent of each other and demonstrated a large range in the duration of enhancement during the acute phase of their evolution.

CONCLUSIONS

The average duration of blood-brain barrier impairment in RRMS is shorter than earlier estimates. Early lesion growth parameters may predict final lesion size. Within-patient heterogeneity of lesion evolution suggests that individual lesions develop independently.

Utility of simultaneous brain, CSF and hyperintensity quantification in dementia

Improved methods of quantifying MRI are needed to study brain-behavior relationships in dementia. Rating scales are variable; lesion-tracing approaches can be subjective and ignore atrophy; segmentation of MRI hyperintensities is complicated by partial volume effects; and hyperintense lesions in different anatomical areas may have different effects. The goal of this study was to extend existing segmentation approaches to include hyperintensities and to demonstrate the utility of simultaneously assessing atrophy and lesion compartments in dementia. A semi-automated method was applied to quantify brain and cerebrospinal fluid (CSF) compartments and to subclassify hyperintensities into periventricular, deep white matter, thalamic and basal ganglia compartments. Twenty MR scans from participants in an ongoing dementia study were used to generate intra- and inter-rater reliability estimates. High intra- and inter-class correlation coefficients (0.83-0.99) were obtained for all measures and the semi-automated measurements were highly correlated with traced volumes. Brain, CSF and specific lesion volumes were significantly correlated with neuropsychological functions. In models using only total hyperintensity volumes, the effects of lesion compartments (such as thalamic) were masked. Simultaneous quantification of atrophy and anatomically distinct hyperintensities is important for understanding cognitive impairments in dementia.

The male predisposition to pharyngeal collapse: importance of airway length

Obstructive sleep apnea is an important disorder because of both its prevalence and its cardiovascular and neurocognitive sequelae. Despite the fact that male sex is a major risk factor for this disorder, the mechanisms underlying this predisposition are unclear. To understand the pathophysiologic basis of the male predisposition for pharyngeal collapse, we performed a detailed analysis of the anatomic and physiologic features of the upper airway in a cohort of normal and near-normal subjects (equal number of men and women). Although no important physiologic (genioglossal electromyogram, airflow resistance) differences were observed between sexes, a number of anatomic differences were apparent. The pharyngeal airway length was substantially longer in men compared with women. There was also an increased cross-sectional area of the soft palate and an increased airway volume in men compared with women. Using signal-averaged anatomic data from male and female subjects, we developed representative male and female finite element airway models. This model demonstrated the male airway to be substantially more collapsible than the female airway, solely on the basis of anatomic differences. This study suggests that the male predisposition to pharyngeal collapse is anatomically based, primarily as the result of an increased length of vulnerable airway as well as increased soft palate size.

Estimating cerebral atrophy in multiple sclerosis patients from various MR pulse sequences

PURPOSE

The purpose of this study was to determine how measures reflecting cerebral atrophy (CA) are influenced by pulse sequence (PS) and segmentation algorithm (SA) used in multiple sclerosis (MS) patients and healthy control (HC)s.

METHODS

Magnetic resonance imaging (MRI) scans from 10 relapsing-remitting MS (RRMS) patients and five HCs were used to determine the change in brain fractional volume (BFV) over a two-year period. T1-weighted, fluid-attenuated inversion recovery (FLAIR), and proton density (PD)/T2-weighted sequences were analysed Image segmentation to determine brain volume was performed using the following a histogram SA, an adaptive fuzzy c-means algorithm (AFCM), and an adaptive Bayesian segmentation with a K-means clustering.

RESULTS

Combinations of the SA and PS in MS patents demonstrated significant differences in the per cent change in BFV from baseline. For the combination of PS and SA the per cent change in BFV for year one and year two varied from +2.05% to - 1.6% and +0.79% to -3.11%, respectively. Analysis of the HCs data revealed fluctuations in BFV varying from +0.26% to -0.29%.

CONCLUSIONS

MRI estimates of CA are dependent on both the type of PS and SA; therefore, the choice of SA technique and PS should be consistent during an MS treatment trial. The progression of CA in MS should only be used as a secondary or tertiary outcome measure in treatment trials until a better understanding of how this measurement is affected by the disease, the image acquisition and analysis techniques.

Estrogen replacement therapy is associated with less progression of subclinical structural brain disease in normal elderly women: a pilot study

BACKGROUND

Cortical atrophy, central atrophy, deep white-matter hyperintensities, and periventricular hyperintensities are reported in normal aging.

OBJECTIVES

We examined the effects of estrogen replacement therapy (ERT) on these forms of 'subclinical structural brain disease' (SSBD) in normal, postmenopausal women in a pilot, naturalistic, longitudinal study of 15 subjects.

METHODS

Two assessments were performed at least two years apart, with volumetric magnetic resonance imaging (MRI) and neuropsychological testing.

RESULTS

Women receiving open-label ERT showed significantly less progression of SSBD than those who did not.

CONCLUSIONS

The association between reduced SSBD progression and ERT suggests this intervention could help preserve normal brain structure in healthy elderly women.

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.

Quantitative MRI assessment of leukoencephalopathy

Quantitative MRI assessment of leukoencephalopathy is difficult because the MRI properties of leukoencephalopathy significantly overlap those of normal tissue. This report describes the use of an automated procedure for longitudinal measurement of tissue volume and relaxation times to quantify leukoencephalopathy. Images derived by using this procedure in patients undergoing therapy for acute lymphoblastic leukemia (ALL) are presented. Five examinations from each of five volunteers (25 examinations) were used to test the reproducibility of quantitated baseline and subsequent, normal-appearing images; the coefficients of variation were less than 2% for gray and white matter. Regions of leukoencephalopathy in patients were assessed by comparison with manual segmentation. Two radiologists manually segmented images from 15 randomly chosen MRI examinations that exhibited leukoencephalopathy. Kappa analyses showed that the two radiologists' interpretations were concordant (kappa = 0.70) and that each radiologist's interpretations agreed with the results of the automated procedure (kappa = 0.57 and 0.55). The clinical application of this method was illustrated by analysis of images from sequential MR examinations of two patients who developed leukoencephalopathy during treatment for ALL. The ultimate goal is to use these quantitative MR imaging measures to better understand therapy-induced neurotoxicity, which can be limited or even reversed with some combination of therapy adjustments and pharmacological and neurobehavioral interventions.