Reconstruction of the human cerebral cortex robust to white matter lesions: method and validation.

Cortical atrophy has been reported in a number of diseases, such as multiple sclerosis and Alzheimer's disease, that are also associated with white matter (WM) lesions. However, most cortical reconstruction techniques do not account for these pathologies, thereby requiring additional processing to correct for the effect of WM lesions. In this work, we introduce CRUISE(+), an automated process for cortical reconstruction from magnetic resonance brain images with WM lesions. The process extends previously well validated methods to allow for multichannel input images and to accommodate for the presence of WM lesions. We provide new validation data and tools for measuring the accuracy of cortical reconstruction methods on healthy brains as well as brains with multiple sclerosis lesions. Using this data, we validate the accuracy of CRUISE(+) and compare it to another state-of-the-art cortical reconstruction tool. Our results demonstrate that CRUISE(+) has superior performance in the cortical regions near WM lesions, and similar performance in other regions.

Direct segmentation of the major white matter tracts in diffusion tensor images.

Diffusion-weighted images of the human brain are acquired more and more routinely in clinical research settings, yet segmenting and labeling white matter tracts in these images is still challenging. We present in this paper a fully automated method to extract many anatomical tracts at once on diffusion tensor images, based on a Markov random field model and anatomical priors. The approach provides a direct voxel labeling, models explicitly fiber crossings and can handle white matter lesions. Experiments on simulations and repeatability studies show robustness to noise and reproducibility of the algorithm, which has been made publicly available.

A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions.

We describe a new fully automatic method for the segmentation of brain images that contain multiple sclerosis white matter lesions. Multichannel magnetic resonance images are used to delineate multiple sclerosis lesions while segmenting the brain into its major structures. The method is an atlas-based segmentation technique employing a topological atlas as well as a statistical atlas. An advantage of this approach is that all segmented structures are topologically constrained, thereby allowing subsequent processing such as cortical unfolding or diffeomorphic shape analysis techniques. Evaluation with both simulated and real data sets demonstrates that the method has an accuracy competitive with state-of-the-art MS lesion segmentation methods, while simultaneously segmenting the whole brain.

Corrigendum to "Statistical normalization techniques for magnetic resonance imaging" [NeuroImage: Clinical 6 (2014) 9-19].

[This corrects the article DOI: 10.1016/j.nicl.2014.08.008.].

Statistical normalization techniques for magnetic resonance imaging.

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer's disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers.

LONGITUDINAL INTENSITY NORMALIZATION IN THE PRESENCE OF MULTIPLE SCLEROSIS LESIONS.

This paper proposes a longitudinal intensity normalization algorithm for T1-weighted magnetic resonance images of human brains in the presence of multiple sclerosis lesions, aiming towards stable and consistent longitudinal segmentations. Unlike previous longitudinal segmentation methods, we propose a 4D intensity normalization that can be used as a preprocessing step to any segmentation method. The variability in intensities arising from the relapsing and remitting nature of the multiple sclerosis lesions is modeled into an otherwise smooth intensity transform based on first order autoregressive models, resulting in smooth changes in segmentation statistics of normal tissues, while keeping the lesion information unaffected. We validated our method on both simulated and real longitudinal normal subjects and on multiple sclerosis subjects.

Tract-specific quantitative MRI better correlates with disability than conventional MRI in multiple sclerosis.

Although diffusion tensor imaging (DTI) and the magnetization transfer ratio (MTR) have been extensively studied in multiple sclerosis (MS), it is still unclear if they are more effective biomarkers of disability than conventional MRI. MRI scans were performed on 117 participants with MS in addition to 26 healthy volunteers. Mean values were obtained for DTI indices and MTR for supratentorial brain and three white matter tracts of interest. DTI and MTR values were tested for correlations with measures of atrophy and lesion volume and were compared with these more conventional indices for prediction of disability. All DTI and MTR values correlated to an equivalent degree with lesion volume and cerebral volume fraction (CVF). Thalamic volumes correlated with all indices in the optic radiations and with mean and perpendicular diffusivity in the corpus callosum. Nested model regression analysis demonstrated that, compared with CVF, DTI indices in the optic radiations were more strongly correlated with Expanded Disability Status Scale and were also more strongly correlated than both CVF and lesion volume with low-contrast visual acuity. Abnormalities in DTI and MTR are equivalently linked with brain atrophy and inflammatory lesion burden, suggesting that for practical purposes they are markers of multiple aspects of MS pathology. Our findings that some DTI and MTR indices are more strongly linked with disability than conventional MRI measures justifies their potential use as targeted, functional system-specific clinical trial outcomes in MS.

OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI.

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78%]) and the radiologist 52% (95% CI: [38%, 66%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images.

System for integrated neuroimaging analysis and processing of structure.

Mapping brain structure in relation to neurological development, function, plasticity, and disease is widely considered to be one of the most essential challenges for opening new lines of neuro-scientific inquiry. Recent developments with MRI analysis of structural connectivity, anatomical brain segmentation, cortical surface parcellation, and functional imaging have yielded fantastic advances in our ability to probe the neurological structure-function relationship in vivo. To date, the image analysis efforts in each of these areas have typically focused on a single modality. Here, we extend the cortical reconstruction using implicit surface evolution (CRUISE) methodology to perform efficient, consistent, and topologically correct analyses in a natively multi-parametric manner. This effort combines and extends state-of-the-art techniques to simultaneously consider and analyze structural and diffusion information alongside quantitative and functional imaging data. Robust and consistent estimates of the cortical surface extraction, cortical labeling, diffusion-inferred contrasts, diffusion tractography, and subcortical parcellation are demonstrated in a scan-rescan paradigm. Accompanying this demonstration, we present a fully automated software system complete with validation data.

The effect of daclizumab on brain atrophy in relapsing-remitting multiple sclerosis.

Daclizumab is a monoclonal antibody that reduces inflammation in multiple sclerosis (MS). Through a retrospective analysis, our objective was to determine whether daclizumab treatment reduces the rate of brain structure atrophy in comparison to a mixture of other disease-modifying therapies (mainly different interferon ß preparations). We analyzed MRI examinations (1332 scans from 70 MS cases) obtained between 2000 and 2011 in a single center and processed with an automated brain segmentation method. We used mixed-effects multivariable linear regression models to determine whether a median of 4.3 years of daclizumab therapy in 26 patients altered rates of brain-volume change, controlling for variations in MRI protocol. The control group consisted of 44 patients not treated with daclizumab. We found that supratentorial brain volume declined by 5.17 ml per year (95% confidence limits: 3.58-6.77) off daclizumab therapy. On daclizumab, the annual rate of volume loss decreased to 3.72 ml (p=0.01). The rate of ventricular enlargement decreased from 1.26 to 0.42 ml per year (p<0.001). Focused analysis suggests that reduction in gray matter atrophy rate most likely underlies these results. In summary, in this retrospective analysis, daclizumab therapy substantially decreased the rate of brain atrophy in relapsing-remitting MS in comparison to other disease-modifying therapies, predominantly interferon ß.

Relationships between retinal axonal and neuronal measures and global central nervous system pathology in multiple sclerosis.

OBJECTIVE: To determine the relationships between conventional and segmentation-derived optical coherence tomography (OCT) retinal layer thickness measures with intracranial volume (a surrogate of head size) and brain substructure volumes in multiple sclerosis (MS). DESIGN: Cross-sectional study. SETTING: Johns Hopkins University, Baltimore, Maryland. PARTICIPANTS: A total of 84 patients with MS and 24 healthy control subjects. MAIN OUTCOME MEASURES: High-definition spectral-domain OCT conventional and automated segmentation-derived discrete retinal layer thicknesses and 3-T magnetic resonance imaging brain substructure volumes. RESULTS: Peripapillary retinal nerve fiber layer as well as composite ganglion cell layer+inner plexiform layer thicknesses in the eyes of patients with MS without a history of optic neuritis were associated with cortical gray matter (P=.01 and P=.04, respectively) and caudate (P=.04 and P=.03, respectively) volumes. Inner nuclear layer thickness, also in eyes without a history of optic neuritis, was associated with fluid-attenuated inversion recovery lesion volume (P=.007) and inversely associated with normal-appearing white matter volume (P=.005) in relapsing-remitting MS. As intracranial volume was found to be related with several of the OCT measures in patients with MS and healthy control subjects and is already known to be associated with brain substructure volumes, all OCT-brain substructure relationships were adjusted for intracranial volume. CONCLUSIONS Retinal measures reflect global central nervous system pathology in multiple sclerosis, with thicknesses of discrete retinal layers each appearing to be associated with distinct central nervous system processes. Moreover, OCT measures appear to correlate with intracranial volume in patients with MS and healthy control subjects, an important unexpected factor unaccounted for in prior studies examining the relationships between peripapillary retinal nerve fiber layer thickness and brain substructure volumes.

Decreased microglial activation in MS patients treated with glatiramer acetate.

Activated microglia are thought to be an important contributor to tissue damage in multiple sclerosis (MS). The level of microglial activation can be measured non-invasively using [(11)C]-R-PK11195, a radiopharmaceutical for positron emission tomography (PET). Prior studies have identified abnormalities in the level of [(11)C]-R-PK11195 uptake in patients with MS, but treatment effects have not been evaluated. Nine previously untreated relapsing-remitting MS patients underwent PET and magnetic resonance imaging of the brain at baseline and after 1 year of treatment with glatiramer acetate. Parametric maps of [(11)C]-R-PK11195 uptake were obtained for baseline and post-treatment PET scans, and the change in [(11)C]-R-PK11195 uptake pre- to post-treatment was evaluated across the whole brain. Region-of-interest analysis was also applied to selected subregions. Whole brain [(11)C]-R-PK11195 binding potential per unit volume decreased 3.17% (95% CI: -0.74, -5.53%) between baseline and 1 year (p = 0.018). A significant decrease was noted in cortical gray matter and cerebral white matter, and a trend towards decreased uptake was seen in the putamen and thalamus. The results are consistent with a reduction in inflammation due to treatment with glatiramer acetate, though a larger controlled study would be required to prove that association. Future research will focus on whether the level of baseline microglial activation predicts future tissue damage in MS and whether [(11)C]-R-PK11195 uptake in cortical gray matter correlates with cortical lesion load.

Revisiting brain atrophy and its relationship to disability in multiple sclerosis.

BACKGROUND: Brain atrophy is a well-accepted imaging biomarker of multiple sclerosis (MS) that partially correlates with both physical disability and cognitive impairment. METHODOLOGY/PRINCIPAL FINDINGS: Based on MRI scans of 60 MS cases and 37 healthy volunteers, we measured the volumes of white matter (WM) lesions, cortical gray matter (GM), cerebral WM, caudate nucleus, putamen, thalamus, ventricles, and brainstem using a validated and completely automated segmentation method. We correlated these volumes with the Expanded Disability Status Scale (EDSS), MS Severity Scale (MSSS), MS Functional Composite (MSFC), and quantitative measures of ankle strength and toe sensation. Normalized volumes of both cortical and subcortical GM structures were abnormally low in the MS group, whereas no abnormality was found in the volume of the cerebral WM. High physical disability was associated with low cerebral WM, thalamus, and brainstem volumes (partial correlation coefficients ~0.3-0.4) but not with low cortical GM volume. Thalamus volumes were inversely correlated with lesion load (r = -0.36, p<0.005). CONCLUSION: The GM is atrophic in MS. Although lower WM volume is associated with greater disability, as might be expected, WM volume was on average in the normal range. This paradoxical result might be explained by the presence of coexisting pathological processes, such as tissue damage and repair, that cause both atrophy and hypertrophy and that underlie the observed disability.

Segmentation of brain images using adaptive atlases with application to ventriculomegaly.

Segmentation of brain images often requires a statistical atlas for providing prior information about the spatial position of different structures. A major limitation of atlas-based segmentation algorithms is their deficiency in analyzing brains that have a large deviation from the population used in the construction of the atlas. We present an expectation-maximization framework based on a Dirichlet distribution to adapt a statistical atlas to the underlying subject. Our model combines anatomical priors with the subject's own anatomy, resulting in a subject specific atlas which we call an "adaptive atlas". The generation of this adaptive atlas does not require the subject to have an anatomy similar to that of the atlas population, nor does it rely on the availability of an ensemble of similar images. The proposed method shows a significant improvement over current segmentation approaches when applied to subjects with severe ventriculomegaly, where the anatomy deviates significantly from the atlas population. Furthermore, high levels of accuracy are maintained when the method is applied to subjects with healthy anatomy.

MR CONTRAST SYNTHESIS FOR LESION SEGMENTATION.

The magnetic resonance contrast of a neuroimaging data set has strong impact on the utility of the data in image analysis tasks, such as registration and segmentation. Lengthy acquisition times often prevent routine acquisition of multiple MR contrast images, and opportunities for detailed analysis using these data would seem to be irrevocably lost. This paper describes an example based approach which uses patch matching from a multiple contrast atlas with the intended goal of generating an alternate MR contrast image, thus effectively simulating alternative pulse sequences from one another. In this paper, we deal specifically with Fluid Attenuated Inversion Recovery (FLAIR) sequence generation from T1 and T2 pulse sequences. The applicability of this synthetic FLAIR for estimating white matter lesions segmentation is demonstrated.