Automated model-based bias field correction of MR images of the brain

Automatic segmentation of neonatal images using convex optimization and coupled level sets

Accurate segmentation of neonatal brain MR images remains challenging mainly due to their poor spatial resolution, inverted contrast between white matter and gray matter, and high intensity inhomogeneity. Most existing methods for neonatal brain segmentation are atlas-based and voxel-wise. Although active contour/surface models with geometric information constraint have been successfully applied to adult brain segmentation, they are not fully explored in the neonatal image segmentation. In this paper, we propose a novel neonatal image segmentation method by combining local intensity information, atlas spatial prior, and cortical thickness constraint in a single level-set framework. Besides, we also provide a robust and reliable tissue surface initialization for the proposed method by using a convex optimization technique. Thus, tissue segmentation, as well as inner and outer cortical surface reconstruction, can be obtained simultaneously. The proposed method has been tested on a large neonatal dataset, and the validation on 10 neonatal brain images (with manual segmentations) shows very promising results.

Early brain overgrowth in autism associated with an increase in cortical surface area before age 2 years

CONTEXT

Brain enlargement has been observed in 2-year-old children with autism, but the underlying mechanisms are unknown.

OBJECTIVE

To investigate early growth trajectories in brain volume and cortical thickness.

DESIGN

Longitudinal magnetic resonance imaging study.

SETTING

Academic medical centers.

PARTICIPANTS

Fifty-nine children with autism spectrum disorder (ASD) and 38 control children.

INTERVENTION

Children were examined at approximately 2 years of age. Magnetic resonance imaging was repeated approximately 24 months later (when aged 4-5 years; 38 children with ASD; 21 controls).

MAIN OUTCOME MEASURES

Cerebral gray and white matter volumes and cortical thickness.

RESULTS

We observed generalized cerebral cortical enlargement in individuals with ASD at both 2 and 4 to 5 years of age. Rate of cerebral cortical growth across multiple brain regions and tissue compartments in children with ASD was parallel to that seen in the controls, indicating that there was no increase in rate of cerebral cortical growth during this interval. No cerebellar differences were observed in children with ASD. After controlling for total brain volume, a disproportionate enlargement in temporal lobe white matter was observed in the ASD group. We found no significant differences in cortical thickness but observed an increase in an estimate of surface area in the ASD group compared with controls for all cortical regions measured (temporal, frontal, and parieto-occipital lobes).

CONCLUSIONS

Our longitudinal magnetic resonance imaging study found generalized cerebral cortical enlargement in children with ASD, with a disproportionate enlargement in temporal lobe white matter. There was no significant difference from controls in the rate of brain growth for this age interval, indicating that brain enlargement in ASD results from an increased rate of brain growth before age 2 years. The presence of increased cortical volume, but not cortical thickness, suggests that early brain enlargement may be associated with increased cortical surface area. Cortical surface area overgrowth in ASD may underlie brain enlargement and implicates a distinct set of pathogenic mechanisms.

A general system for automatic biomedical image segmentation using intensity neighborhoods

Image segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before being used in a different application. We describe an approach that, with few modifications, can be used in a variety of image segmentation problems. The approach is based on a supervised learning strategy that utilizes intensity neighborhoods to assign each pixel in a test image its correct class based on training data. We describe methods for modeling rotations and variations in scales as well as a subset selection for training the classifiers. We show that the performance of our approach in tissue segmentation tasks in magnetic resonance and histopathology microscopy images, as well as nuclei segmentation from fluorescence microscopy images, is similar to or better than several algorithms specifically designed for each of these applications.

MRI study of minor physical anomaly in childhood autism implicates aberrant neurodevelopment in infancy

BACKGROUND

MPAs (minor physical anomalies) frequently occur in neurodevelopmental disorders because both face and brain are derived from neuroectoderm in the first trimester. Conventionally, MPAs are measured by evaluation of external appearance. Using MRI can help overcome inherent observer bias, facilitate multi-centre data acquisition, and explore how MPAs relate to brain dysmorphology in the same individual. Optical MPAs exhibit a tightly synchronized trajectory through fetal, postnatal and adult life. As head size enlarges with age, inter-orbital distance increases, and is mostly completed before age 3 years. We hypothesized that optical MPAs might afford a retrospective 'window' to early neurodevelopment; specifically, inter-orbital distance increase may represent a biomarker for early brain dysmaturation in autism.

METHODS

We recruited 91 children aged 7-16; 36 with an autism spectrum disorder and 55 age- and gender-matched typically developing controls. All children had normal IQ. Inter-orbital distance was measured on T1-weighted MRI scans. This value was entered into a voxel-by-voxel linear regression analysis with grey matter segmented from a bimodal MRI data-set. Age and total brain tissue volume were entered as covariates.

RESULTS

Intra-class coefficient for measurement of the inter-orbital distance was 0.95. Inter-orbital distance was significantly increased in the autism group (p = 0.03, 2-tailed). The autism group showed a significant relationship between inter-orbital distance grey matter volume of bilateral amygdalae extending to the unci and inferior temporal poles.

CONCLUSIONS

Greater inter-orbital distance in the autism group compared with healthy controls is consistent with infant head size expansion in autism. Inter-orbital distance positively correlated with volume of medial temporal lobe structures, suggesting a link to "social brain" dysmorphology in the autism group. We suggest these data support the role of optical MPAs as a "fossil record" of early aberrant neurodevelopment, and potential biomarker for brain dysmaturation in autism.

On the convergence of EM-like algorithms for image segmentation using Markov random fields

Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.

A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction

We describe a fully automated method for tissue classification, which is the segmentation into cerebral gray matter (GM), cerebral white matter (WM), and cerebral spinal fluid (CSF), and intensity non-uniformity (INU) correction in brain magnetic resonance imaging (MRI) volumes. It combines supervised MRI modality-specific discriminative modeling and unsupervised statistical expectation maximization (EM) segmentation into an integrated Bayesian framework. While both the parametric observation models and the non-parametrically modeled INUs are estimated via EM during segmentation itself, a Markov random field (MRF) prior model regularizes segmentation and parameter estimation. Firstly, the regularization takes into account knowledge about spatial and appearance-related homogeneity of segments in terms of pairwise clique potentials of adjacent voxels. Secondly and more importantly, patient-specific knowledge about the global spatial distribution of brain tissue is incorporated into the segmentation process via unary clique potentials. They are based on a strong discriminative model provided by a probabilistic boosting tree (PBT) for classifying image voxels. It relies on the surrounding context and alignment-based features derived from a probabilistic anatomical atlas. The context considered is encoded by 3D Haar-like features of reduced INU sensitivity. Alignment is carried out fully automatically by means of an affine registration algorithm minimizing cross-correlation. Both types of features do not immediately use the observed intensities provided by the MRI modality but instead rely on specifically transformed features, which are less sensitive to MRI artifacts. Detailed quantitative evaluations on standard phantom scans and standard real-world data show the accuracy and robustness of the proposed method. They also demonstrate relative superiority in comparison to other state-of-the-art approaches to this kind of computational task: our method achieves average Dice coefficients of 0.93 ± 0.03 (WM) and 0.90 ± 0.05 (GM) on simulated mono-spectral and 0.94 ± 0.02 (WM) and 0.92 ± 0.04 (GM) on simulated multi-spectral data from the BrainWeb repository. The scores are 0.81 ± 0.09 (WM) and 0.82 ± 0.06 (GM) and 0.87 ± 0.05 (WM) and 0.83 ± 0.12 (GM) for the two collections of real-world data sets-consisting of 20 and 18 volumes, respectively-provided by the Internet Brain Segmentation Repository.

Non-iterative relative bias correction for 3D reconstruction of in utero fetal brain MR imaging

The slice intersection motion correction (SIMC) method is a powerful tool to compensate for motion that occurs during in utero acquisition of the multislice magnetic resonance (MR) images of the human fetal brain. The SIMC method makes use of the slice intersection intensity profiles of orthogonally planned slice pairs to simultaneously correct for the relative motion occurring between all the acquired slices. This approach is based on the assumption that the bias field is consistent between slices. However, for some clinical studies where there is a strong bias field combined with significant fetal motion relative to the coils, this assumption is broken and the resulting motion estimate and the reconstruction to a 3D volume can both contain errors. In this work, we propose a method to correct for the relative differences in bias field between all slice pairs. For this, we define the energy function as the mean square difference of the intersection profiles, that is then minimized with respect to the bias field parameters of the slices. A non iterative method which considers the relative bias between each slice simultaneously is used to efficiently remove inconsistencies. The method, when tested on synthetic simulations and actual clinical imaging studies where bias was an issue, brought a significant improvement to the final reconstructed image.

Different structural correlates for verbal memory impairment in temporal lobe epilepsy with and without mesial temporal lobe sclerosis

OBJECTIVES

Memory impairment is one of the most prominent cognitive deficits in temporal lobe epilepsy (TLE). The overall goal of this study was to explore the contribution of cortical and hippocampal (subfield) damage to impairment of auditory immediate recall (AIMrecall), auditory delayed recall (ADMrecall), and auditory delayed recognition (ADMrecog) of the Wechsler Memory Scale III (WMS-III) in TLE with (TLE-MTS) and without hippocampal sclerosis (TLE-no). It was hypothesized that volume loss in different subfields determines memory impairment in TLE-MTS and temporal neocortical thinning in TLE-no.

METHODS

T1 whole brain and T2-weighted hippocampal magnetic resonance imaging and WMS-III were acquired in 22 controls, 18 TLE-MTS, and 25 TLE-no. Hippocampal subfields were determined on the T2 image. Free surfer was used to obtain cortical thickness averages of temporal, frontal, and parietal cortical regions of interest (ROI). MANOVA and stepwise regression analysis were used to identify hippocampal subfields and cortical ROI significantly contributing to AIMrecall, ADMrecall, and ADMrecog.

RESULTS

In TLE-MTS, AIMrecall was associated with cornu ammonis 3 (CA3) and dentate (CA3&DG) and pars opercularis, ADMrecall with CA1 and pars triangularis, and ADMrecog with CA1. In TLE-no, AIMrecall was associated with CA3&DG and fusiform gyrus (FUSI), and ADMrecall and ADMrecog were associated with FUSI.

CONCLUSION

The study provided the evidence for different structural correlates of the verbal memory impairment in TLE-MTS and TLE-no. In TLE-MTS, the memory impairment was mainly associated by subfield-specific hippocampal and inferior frontal cortical damage. In TLE-no, the impairment was associated by mesial-temporal cortical and to a lesser degree hippocampal damage.

LoAd: a locally adaptive cortical segmentation algorithm

Thickness measurements of the cerebral cortex can aid diagnosis and provide valuable information about the temporal evolution of diseases such as Alzheimer's, Huntington's, and schizophrenia. Methods that measure the thickness of the cerebral cortex from in-vivo magnetic resonance (MR) images rely on an accurate segmentation of the MR data. However, segmenting the cortex in a robust and accurate way still poses a challenge due to the presence of noise, intensity non-uniformity, partial volume effects, the limited resolution of MRI and the highly convoluted shape of the cortical folds. Beginning with a well-established probabilistic segmentation model with anatomical tissue priors, we propose three post-processing refinements: a novel modification of the prior information to reduce segmentation bias; introduction of explicit partial volume classes; and a locally varying MRF-based model for enhancement of sulci and gyri. Experiments performed on a new digital phantom, on BrainWeb data and on data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) show statistically significant improvements in Dice scores and PV estimation (p<10(-3)) and also increased thickness estimation accuracy when compared to three well established techniques.

Atlas-based segmentation of developing tissues in the human brain with quantitative validation in young fetuses

Imaging of the human fetus using magnetic resonance (MR) is an essential tool for quantitative studies of normal as well as abnormal brain development in utero. However, because of fundamental differences in tissue types, tissue properties and tissue distribution between the fetal and adult brain, automated tissue segmentation techniques developed for adult brain anatomy are unsuitable for this data. In this paper, we describe methodology for automatic atlas-based segmentation of individual tissue types in motion-corrected 3D volumes reconstructed from clinical MR scans of the fetal brain. To generate anatomically correct automatic segmentations, we create a set of accurate manual delineations and build an in utero 3D statistical atlas of tissue distribution incorporating developing gray and white matter as well as transient tissue types such as the germinal matrix. The probabilistic atlas is associated with an unbiased average shape and intensity template for registration of new subject images to the space of the atlas. Quantitative whole brain 3D validation of tissue labeling performed on a set of 14 fetal MR scans (20.57-22.86 weeks gestational age) demonstrates that this atlas-based EM segmentation approach achieves consistently high DSC performance for the main tissue types in the fetal brain. This work indicates that reliable measures of brain development can be automatically derived from clinical MR imaging and opens up possibility of further 3D volumetric and morphometric studies with multiple fetal subjects.

Joint restoration of bi-contrast MRI data for spatial intensity non-uniformities

The reconstruction of MRI data assumes a uniform radiofrequency field. However, in practice the radio-frequency field is inhomogeneous and leads to non-biological intensity non-uniformities across an image. This artifact can complicate further automated analysis of the data. In general, an acquisition protocol provides images of the same anatomic region with multiple contrasts representing similar underlying information, but suffering from different intensity non-uniformities. A method is presented for the joint intensity uniformity restoration of two such images. The effect of the intensity distortion on the auto-co-occurrence statistics of each of the two images as well as in their joint-co-occurrence statistics is modeled and used for their restoration with Wiener filtering. Several regularity constrains for the anatomy and for the non-uniformity are also imposed. Moreover, the method considers an inevitable difference between the signal regions of the two images. The joint treatment of the images can improve the accuracy and the efficiency of the restoration as well as decrease the requirements for additional calibration scans. The effectiveness of the method has been demonstrated extensively with both phantom and real brain anatomic data as well as with real DIXON pairs of fat and water abdominal data.

Segmentation priors from local image properties: without using bias field correction, location-based templates, or registration

We present a novel approach for generating information about a voxel's tissue class membership based on its signature--a collection of local image textures estimated over a range of neighborhood sizes. The approach produces a form of tissue class priors that can be used to initialize and regularize image segmentation. The signature-based approach is a departure from current location-based methods, which derive tissue class likelihoods based on a voxel's location in standard template space. To use location-based priors, one needs to register the volume in question to the template space, and estimate the image intensity bias field. Two optimizations, over more than a thousand parameters, are needed when high order nonlinear registration is employed. In contrast, the signature-based approach is independent of volume orientation, voxel position, and largely insensitive to bias fields. For these reasons, the approach does not require the use of population derived templates. The prior information is generated from variations in image texture statistics as a function of spatial scale, and an SVM approach is used to associate signatures with tissue types. With the signature-based approach, optimization is needed only during the training phase for the parameter estimation stages of the SVM hyperplanes, and associated PDFs; a training process separate from the segmentation step. We found that signature-based priors were superior to location-based ones aligned under favorable conditions, and that signature-based priors result in improved segmentation when replacing location-based ones in FAST (Zhang et al., 2001), a widely used segmentation program. The software implementation of this work is freely available as part of AFNI http://afni.nimh.nih.gov.

Widespread extrahippocampal NAA/(Cr+Cho) abnormalities in TLE with and without mesial temporal sclerosis

MR spectroscopy has demonstrated extrahippocampal NAA/(Cr+Cho) reductions in medial temporal lobe epilepsy with (TLE-MTS) and without (TLE-no) mesial temporal sclerosis. Because of the limited brain coverage of those previous studies, it was, however, not possible to assess differences in the distribution and extent of these abnormalities between TLE-MTS and TLE-no. This study used a 3D whole brain echoplanar spectroscopic imaging (EPSI) sequence to address the following questions: (1) Do TLE-MTS and TLE-no differ regarding severity and distribution of extrahippocampal NAA/(Cr+Cho) reductions? (2) Do extrahippocampal NAA/(Cr+Cho) reductions provide additional information for focus lateralization? Forty-three subjects (12 TLE-MTS, 13 TLE-no, 18 controls) were studied with 3D EPSI. Statistical parametric mapping (SPM2) was used to identify regions of significantly decreased NAA/(Cr+Cho) in TLE groups and in individual patients. TLE-MTS and TLE-no had widespread extrahippocampal NAA/(Cr+Cho) reductions. NAA/(Cr+Cho) reductions had a bilateral fronto-temporal distribution in TLE-MTS and a more diffuse, less well defined distribution in TLE-no. Extrahippocampal NAA/(Cr+Cho) decreases in the single subject analysis showed a large inter-individual variability and did not provide additional focus lateralizing information. Extrahippocampal NAA/(Cr+Cho) reductions in TLE-MTS and TLE-no are neither focal nor homogeneous. This reduces their value for focus lateralization and suggests a heterogeneous etiology of extrahippocampal spectroscopic metabolic abnormalities in TLE.

A generative model for image segmentation based on label fusion

We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject. Such label fusion methods have been shown to yield accurate segmentation, since the use of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures. To the best of our knowledge, this manuscript presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multiatlas segmentation algorithms are interpreted as special cases of our framework. We conduct two sets of experiments to validate the proposed methods. In the first set of experiments, we use 39 brain MRI scans-with manually segmented white matter, cerebral cortex, ventricles and subcortical structures-to compare different label fusion algorithms and the widely-used FreeSurfer whole-brain segmentation tool. Our results indicate that the proposed framework yields more accurate segmentation than FreeSurfer and previous label fusion algorithms. In a second experiment, we use brain MRI scans of 282 subjects to demonstrate that the proposed segmentation tool is sufficiently sensitive to robustly detect hippocampal volume changes in a study of aging and Alzheimer's Disease.

A spatiotemporal atlas of MR intensity, tissue probability and shape of the fetal brain with application to segmentation

Modeling and analysis of MR images of the developing human brain is a challenge due to rapid changes in brain morphology and morphometry. We present an approach to the construction of a spatiotemporal atlas of the fetal brain with temporal models of MR intensity, tissue probability and shape changes. This spatiotemporal model is created from a set of reconstructed MR images of fetal subjects with different gestational ages. Groupwise registration of manual segmentations and voxelwise nonlinear modeling allow us to capture the appearance, disappearance and spatial variation of brain structures over time. Applying this model to atlas-based segmentation, we generate age-specific MR templates and tissue probability maps and use them to initialize automatic tissue delineation in new MR images. The choice of model parameters and the final performance are evaluated using clinical MR scans of young fetuses with gestational ages ranging from 20.57 to 24.71 weeks. Experimental results indicate that quadratic temporal models can correctly capture growth-related changes in the fetal brain anatomy and provide improvement in accuracy of atlas-based tissue segmentation.

An Automated Method for Segmenting White Matter Lesions through Multi-Level Morphometric Feature Classification with Application to Lupus

We demonstrate an automated, multi-level method to segment white matter brain lesions and apply it to lupus. The method makes use of local morphometric features based on multiple MR sequences, including T1-weighted, T2-weighted, and fluid attenuated inversion recovery. After preprocessing, including co-registration, brain extraction, bias correction, and intensity standardization, 49 features are calculated for each brain voxel based on local morphometry. At each level of segmentation a supervised classifier takes advantage of a different subset of the features to conservatively segment lesion voxels, passing on more difficult voxels to the next classifier. This multi-level approach allows for a fast lesion classification method with tunable trade-offs between sensitivity and specificity producing accuracy comparable to a human rater.

Joint analysis of structural and perfusion MRI for cognitive assessment and classification of Alzheimer's disease and normal aging

Structural magnetic resonance imaging (MRI) of brain tissue loss and physiological imaging of regional cerebral blood flow (rCBF) can provide complimentary information for the characterization of brain disorders, such as Alzheimer's disease (AD) but studies into gains in classification power for AD using these image modalities jointly have been limited. Our aim in this study was to determine the joint contribution of structural and perfusion-weighted imaging for the classification of AD in a cross-sectional study using an integrated multimodality MRI processing framework and a cortical surface-based analysis approach. We used logistic regression analysis to determine sequentially the value of cortical thickness, rCBF, and cortical thickness and rCBF jointly for classification for diagnosis of AD compared to controls. We further tested the extent to which cortical thinning and reduced rCBF explain individually or together variability in dementia severity. Separate analysis of structural MRI and perfusion-weighted MRI data yielded the well-established pattern of cortical thinning and rCBF reduction in AD, affecting predominantly temporo-parietal brain regions. Using structural MRI and perfusion-weighted MRI jointly indicated that cortical thinning dominated the classification of AD and controls without significant contributions from rCBF. However there was also a positive interaction between reduced rCBF and cortical thinning in the right superior temporal sulcus, implying that structural and physiological brain alterations in AD can be complementary. Compared to reduced rCBF, regional cortical thinning better explained the variability in dementia severity. In conclusion, structural brain alterations compared to physiological variations are the dominant features of MRI in AD.

The effects of healthy aging on intracerebral blood vessels visualized by magnetic resonance angiography

Histological and magnetic resonance imaging studies have demonstrated that age-associated alterations of the human brain may be at least partially related to vascular alterations. Relatively little information has been published on vascular changes associated with healthy aging, however. The study presented in this paper examined vessels segmented from standardized, high-resolution, magnetic resonance angiograms (MRAs) of 100 healthy volunteers (50 males, 50 females), aged 18-74, without hypertension or other disease likely to affect the vasculature. The subject sample was divided into 5 age groups (n=20/group) with gender equally distributed per group. The anterior cerebral, both middle cerebral, and the posterior circulations were examined for vessel number, vessel radius, and vessel tortuosity. Males exhibited larger vessel radii regardless of age and across all anatomical regions. Both males and females displayed a lower number of MRA-discernible vessels with age, most marked in the posterior circulation. Age-associated tortuosity increases were relatively mild. Our multi-modal image database has been made publicly available for use by other investigators.

Topology-corrected segmentation and local intensity estimates for improved partial volume classification of brain cortex in MRI

In magnetic resonance imaging (MRI), accuracy and precision with which brain structures may be quantified are frequently affected by the partial volume (PV) effect. PV is due to the limited spatial resolution of MRI compared to the size of anatomical structures. Accurate classification of mixed voxels and correct estimation of the proportion of each pure tissue (fractional content) may help to increase the precision of cortical thickness estimation in regions where this measure is particularly difficult, such as deep sulci. The contribution of this work is twofold: on the one hand, we propose a new method to label voxels and compute tissue fractional content, integrating a mechanism for detecting sulci with topology preserving operators. On the other hand, we improve the computation of the fractional content of mixed voxels using local estimation of pure tissue intensity means. Accuracy and precision were assessed using simulated and real MR data and comparison with other existing approaches demonstrated the benefits of our method. Significant improvements in gray matter (GM) classification and cortical thickness estimation were brought by the topology correction. The fractional content root mean squared error diminished by 6.3% (p<0.01) on simulated data. The reproducibility error decreased by 8.8% (p<0.001) and the Jaccard similarity measure increased by 3.5% on real data. Furthermore, compared with manually guided expert segmentations, the similarity measure was improved by 12.0% (p<0.001). Thickness estimation with the proposed method showed a higher reproducibility compared with the measure performed after partial volume classification using other methods.

Brain MRI tissue classification based on local Markov random fields

A new method for tissue classification of brain magnetic resonance images (MRI) of the brain is proposed. The method is based on local image models where each models the image content in a subset of the image domain. With this local modeling approach, the assumption that tissue types have the same characteristics over the brain needs not to be evoked. This is important because tissue type characteristics, such as T1 and T2 relaxation times and proton density, vary across the individual brain and the proposed method offers improved protection against intensity non-uniformity artifacts that can hamper automatic tissue classification methods in brain MRI. A framework in which local models for tissue intensities and Markov Random Field (MRF) priors are combined into a global probabilistic image model is introduced. This global model will be an inhomogeneous MRF and it can be solved by standard algorithms such as iterative conditional modes. The division of the whole image domain into local brain regions possibly having different intensity statistics is realized via sub-volume probabilistic atlases. Finally, the parameters for the local intensity models are obtained without supervision by maximizing the weighted likelihood of a certain finite mixture model. For the maximization task, a novel genetic algorithm almost free of initialization dependency is applied. The algorithm is tested on both simulated and real brain MR images. The experiments confirm that the new method offers a useful improvement of the tissue classification accuracy when the basic tissue characteristics vary across the brain and the noise level of the images is reasonable. The method also offers better protection against intensity non-uniformity artifact than the corresponding method based on a global (whole image) modeling scheme.

Hippocampal malrotation in pediatric patients with epilepsy associated with complex prefrontal dysfunction

PURPOSE

The cognitive consequences of hippocampal malrotation (HIMAL) were investigated in a matched control study of children with epilepsy.

METHODS

Seven children with HIMAL were compared on a range of memory and attention tasks with 21 control children with epilepsy without temporal role pathology and 7 children with epilepsy and magnetic resonance imaging (MRI)-documented hippocampal sclerosis. In addition, in a statistical morphometric analysis, MRI studies from four children with HIMAL were compared to similar images of 20 age-matched typically developing control children.

RESULTS

Although the task battery was sensitive to the memory deficit of the children with hippocampal sclerosis, it did not reveal memory impairment in the patients with HIMAL. In contrast, the patients with HIMAL were impaired on the attentionally more demanding dual tasks, compared to both the control and the hippocampal sclerosis group. The structural MRI analysis revealed morphometric abnormalities in the tail of the affected hippocampus, the adjacent neocortex, and the ipsilateral medial thalamus. The basal forebrain was bilaterally affected. Abnormalities in remote cortex were found in the ipsilateral temporal lobe, the contralateral anterior cingulate gyrus, and bilateral in the dorsolateral and lateral-orbitofrontal prefrontal cortex.

DISCUSSION

Because the prefrontal cortical regions have been shown to be active during dual-task performance, the MRI results converge with the neuropsychological findings of impairment on these tasks. We conclude that HIMAL had no direct memory repercussions, but was secondary to subtle but widespread neurologic abnormalities that also affected morphology and functioning of the prefrontal cortex.

Involvement of the thalamocortical network in TLE with and without mesiotemporal sclerosis

PURPOSE

The thalamus plays an important role in seizure propagation in temporal lobe epilepsy (TLE). This study investigated how structural abnormalities in the focus, ipsilateral thalamus and extrafocal cortical structures relate to each other in TLE with mesiotemporal sclerosis (TLE-MTS) and without hippocampal sclerosis (TLE-no).

METHODS

T₁ and high-resolution T₂ images were acquired on a 4T magnet in 29 controls, 15 TLE-MTS cases, and 14 TLE-no. Thalamus volumes were obtained by warping a labeled atlas onto each subject's brain. Deformation-based morphometry was used to identify regions of thalamic volume loss and FreeSurfer for cortical thickness measurements. CA1 volumes were obtained from high-resolution T₂ images. Multiple regression analysis and correlation analyses for voxel- and vertex-based analyses were performed in SPM2 and FreeSurfer.

RESULTS

TLE-MTS had bilateral volume loss in the anterior thalamus, which was correlated with CA1 volume and cortical thinning in the mesiotemporal lobe. TLE-no had less severe volume loss in the dorsal lateral nucleus, which was correlated with thinning in the mesiotemporal region but not with extratemporal thinning.

DISCUSSION

The findings suggest that seizure propagation from the presumed epileptogenic focus or regions close to it into the thalamus occurs in TLE-MTS and TLE-no and results in circumscribed neuronal loss in the thalamus. However, seizure spread beyond the thalamus seems not to be responsible for the extensive extratemporal cortical abnormalities in TLE.

A naturalistic study of grey matter volume increase after early treatment in anti-psychotic naïve, newly diagnosed schizophrenia

BACKGROUND

Anti-psychotic treatment appears to be associated with striatal volume increase, but how early this change occurs is still unknown.

METHODS

A single prospective cohort of 20 anti-psychotic-naïve patients, newly diagnosed with schizophrenia, underwent magnetic resonance imaging brain scan at baseline. This was repeated following up to 8 weeks of anti-psychotic treatment. Ten patients had repeat scan within only 3 weeks. The choice of anti-psychotic medication was naturalistic, i.e., clinician-led. Well-matched healthy individuals were also scanned to control for non-specific changes over a 3-week period.

RESULTS

After 3 weeks of anti-psychotic treatment, significant grey matter volume increase in the right caudate, superior and inferior frontal gyrus, precentral gyrus, and left inferior parietal lobule was noted. However, after 8 weeks of anti-psychotic treatment, volume increase in the right thalamus and bilateral cerebellum was observed. Significant grey matter reduction was detected in the left medial frontal gyrus at both 3- and 8-week intervals.

CONCLUSIONS

Early increase in striatal volume change occurs as early as 3 weeks after anti-psychotic treatment, whilst thalamic volume increase is apparent later, by 8 weeks of treatment. We speculate that drug-mediated neuroplasticity may provide a biomarker for clinical recovery.

Intensity Standardization Simplifies Brain MR Image Segmentation

Typically, brain MR images present significant intensity variation across patients and scanners. Consequently, training a classifier on a set of images and using it subsequently for brain segmentation may yield poor results. Adaptive iterative methods usually need to be employed to account for the variations of the particular scan. These methods are complicated, difficult to implement and often involve significant computational costs. In this paper, a simple, non-iterative method is proposed for brain MR image segmentation. Two preprocessing techniques, namely intensity inhomogeneity correction, and more importantly MR image intensity standardization, used prior to segmentation, play a vital role in making the MR image intensities have a tissue-specific numeric meaning, which leads us to a very simple brain tissue segmentation strategy.Vectorial scale-based fuzzy connectedness and certain morphological operations are utilized first to generate the brain intracranial mask. The fuzzy membership value of each voxel within the intracranial mask for each brain tissue is then estimated. Finally, a maximum likelihood criterion with spatial constraints taken into account is utilized in classifying all voxels in the intracranial mask into different brain tissue groups. A set of inhomogeneity corrected and intensity standardized images is utilized as a training data set. We introduce two methods to estimate fuzzy membership values. In the first method, called SMG (for simple membership based on a gaussian model), the fuzzy membership value is estimated by fitting a multivariate Gaussian model to the intensity distribution of each brain tissue whose mean intensity vector and covariance matrix are estimated and fixed from the training data sets. The second method, called SMH (for simple membership based on a histogram), estimates fuzzy membership value directly via the intensity distribution of each brain tissue obtained from the training data sets. We present several studies to evaluate the performance of these two methods based on 10 clinical MR images of normal subjects and 10 clinical MR images of Multiple Sclerosis (MS) patients. A quantitative comparison indicates that both methods have overall better accuracy than the k-nearest neighbors (kNN) method, and have much better efficiency than the Finite Mixture (FM) model based Expectation-Maximization (EM) method. Accuracy is similar for our methods and EM method for the normal subject data sets, but much better for our methods for the patient data sets.

Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI

Recent developments in MRI data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. However, a fundamental bottleneck in MRI studies of the hippocampus at the subfield level is that they currently depend on manual segmentation, a laborious process that severely limits the amount of data that can be analyzed. In this article, we present a computational method for segmenting the hippocampal subfields in ultra-high resolution MRI data in a fully automated fashion. Using Bayesian inference, we use a statistical model of image formation around the hippocampal area to obtain automated segmentations. We validate the proposed technique by comparing its segmentations to corresponding manual delineations in ultra-high resolution MRI scans of 10 individuals, and show that automated volume measurements of the larger subfields correlate well with manual volume estimates. Unlike manual segmentations, our automated technique is fully reproducible, and fast enough to enable routine analysis of the hippocampal subfields in large imaging studies.

Neonatal brain image segmentation in longitudinal MRI studies

In the study of early brain development, tissue segmentation of neonatal brain MR images remains challenging because of the insufficient image quality due to the properties of developing tissues. Among various brain tissue segmentation algorithms, atlas-based brain image segmentation can potentially achieve good segmentation results on neonatal brain images. However, their performances rely on both the quality of the atlas and the spatial correspondence between the atlas and the to-be-segmented image. Moreover, it is difficult to build a population atlas for neonates due to the requirement of a large set of tissue-segmented neonatal brain images. To combat these obstacles, we present a longitudinal neonatal brain image segmentation framework by taking advantage of the longitudinal data acquired at late time-point to build a subject-specific tissue probabilistic atlas. Specifically, tissue segmentation of the neonatal brain is formulated as two iterative steps of bias correction and probabilistic-atlas-based tissue segmentation, along with the longitudinal atlas reconstructed by the late time image of the same subject. The proposed method has been evaluated qualitatively through visual inspection and quantitatively by comparing with manual delineations and two population-atlas-based segmentation methods. Experimental results show that the utilization of a subject-specific probabilistic atlas can substantially improve tissue segmentation of neonatal brain images.

Distributed local MRF models for tissue and structure brain segmentation

Accurate tissue and structure segmentation of magnetic resonance (MR) brain scans is critical in several applications. In most approaches this task is handled through two sequential steps. We propose to carry out cooperatively both tissue and subcortical structure segmentation by distributing a set of local and cooperative Markov random field (MRF) models Tissue segmentation is performed by partitioning the volume into subvolumes where local MRFs are estimated in cooperation with their neighbors to ensure consistency. Local estimation fits precisely to the local intensity distribution and thus handles nonuniformity of intensity without any bias field modelization. Similarly, subcortical structure segmentation is performed via local MRF models that integrate localization constraints provided by a priori fuzzy description of brain anatomy. Subcortical structure segmentation is not reduced to a subsequent processing step but joined with tissue segmentation: the two procedures cooperate to gradually and conjointly improve model accuracy. We propose a framework to implement this distributed modeling integrating cooperation, coordination, and local model checking in an efficient way. Its evaluation was performed using both phantoms and real 3 T brain scans, showing good results and in particular robustness to nonuniformity and noise with a low computational cost. This original combination of local MRF models, including anatomical knowledge, appears as a powerful and promising approach for MR brain scan segmentation.

Automated voxel-based 3D cortical thickness measurement in a combined Lagrangian-Eulerian PDE approach using partial volume maps

Accurate cortical thickness estimation is important for the study of many neurodegenerative diseases. Many approaches have been previously proposed, which can be broadly categorised as mesh-based and voxel-based. While the mesh-based approaches can potentially achieve subvoxel resolution, they usually lack the computational efficiency needed for clinical applications and large database studies. In contrast, voxel-based approaches, are computationally efficient, but lack accuracy. The aim of this paper is to propose a novel voxel-based method based upon the Laplacian definition of thickness that is both accurate and computationally efficient. A framework was developed to estimate and integrate the partial volume information within the thickness estimation process. Firstly, in a Lagrangian step, the boundaries are initialized using the partial volume information. Subsequently, in an Eulerian step, a pair of partial differential equations are solved on the remaining voxels to finally compute the thickness. Using partial volume information significantly improved the accuracy of the thickness estimation on synthetic phantoms, and improved reproducibility on real data. Significant differences in the hippocampus and temporal lobe between healthy controls (NC), mild cognitive impaired (MCI) and Alzheimer's disease (AD) patients were found on clinical data from the ADNI database. We compared our method in terms of precision, computational speed and statistical power against the Eulerian approach. With a slight increase in computation time, accuracy and precision were greatly improved. Power analysis demonstrated the ability of our method to yield statistically significant results when comparing AD and NC. Overall, with our method the number of samples is reduced by 25% to find significant differences between the two groups.

Asymmetrical lateral ventricular enlargement in Parkinson's disease

BACKGROUND

A recent case report suggested the presence of asymmetrical lateral ventricular enlargement associated with motor asymmetry in Parkinson's disease (PD). The current study explored these associations further.

METHODS

Magnetic resonance imaging (3T) scans were obtained on 17 PD and 15 healthy control subjects at baseline and 12-43 months later. Baseline and longitudinal lateral ventricular volumetric changes were compared between contralateral and ipsilateral ventricles in PD subjects relative to symptom onset side and in controls relative to their dominant hand. Correlations between changes in ventricular volume and United Parkinson's disease rating scale motor scores (UPDRS-III) whilst on medication were determined.

RESULTS

The lateral ventricle contralateral to symptom onset side displayed a faster rate of enlargement compared to the ipsilateral (P = 0.004) in PD subjects, with no such asymmetry detected (P = 0.312) in controls. There was a positive correlation between ventricular enlargement and worsening motor function assessed by UPDRS-III scores (r = 0.96, P < 0.001).

DISCUSSION

There is asymmetrical lateral ventricular enlargement that is associated with PD motor asymmetry and progression. Further studies are warranted to investigate the underlying mechanism(s), as well as the potential of using volumetric measurements as a marker for PD progression.

A structural MRI study of human brain development from birth to 2 years

Brain development in the first 2 years after birth is extremely dynamic and likely plays an important role in neurodevelopmental disorders, including autism and schizophrenia. Knowledge regarding this period is currently quite limited. We studied structural brain development in healthy subjects from birth to 2. Ninety-eight children received structural MRI scans on a Siemens head-only 3T scanner with magnetization prepared rapid gradient echo T1-weighted, and turbo spin echo, dual-echo (proton density and T2 weighted) sequences: 84 children at 2-4 weeks, 35 at 1 year and 26 at 2 years of age. Tissue segmentation was accomplished using a novel automated approach. Lateral ventricle, caudate, and hippocampal volumes were also determined. Total brain volume increased 101% in the first year, with a 15% increase in the second. The majority of hemispheric growth was accounted for by gray matter, which increased 149% in the first year; hemispheric white matter volume increased by only 11%. Cerebellum volume increased 240% in the first year. Lateral ventricle volume increased 280% in the first year, with a small decrease in the second. The caudate increased 19% and the hippocampus 13% from age 1 to age 2. There was robust growth of the human brain in the first two years of life, driven mainly by gray matter growth. In contrast, white matter growth was much slower. Cerebellum volume also increased substantially in the first year of life. These results suggest the structural underpinnings of cognitive and motor development in early childhood, as well as the potential pathogenesis of neurodevelopmental disorders.

Encoding probabilistic brain atlases using Bayesian inference

This paper addresses the problem of creating probabilistic brain atlases from manually labeled training data. Probabilistic atlases are typically constructed by counting the relative frequency of occurrence of labels in corresponding locations across the training images. However, such an "averaging" approach generalizes poorly to unseen cases when the number of training images is limited, and provides no principled way of aligning the training datasets using deformable registration. In this paper, we generalize the generative image model implicitly underlying standard "average" atlases, using mesh-based representations endowed with an explicit deformation model. Bayesian inference is used to infer the optimal model parameters from the training data, leading to a simultaneous group-wise registration and atlas estimation scheme that encompasses standard averaging as a special case. We also use Bayesian inference to compare alternative atlas models in light of the training data, and show how this leads to a data compression problem that is intuitive to interpret and computationally feasible. Using this technique, we automatically determine the optimal amount of spatial blurring, the best deformation field flexibility, and the most compact mesh representation. We demonstrate, using 2-D training datasets, that the resulting models are better at capturing the structure in the training data than conventional probabilistic atlases. We also present experiments of the proposed atlas construction technique in 3-D, and show the resulting atlases' potential in fully-automated, pulse sequence-adaptive segmentation of 36 neuroanatomical structures in brain MRI scans.

Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation

In this paper, we propose an improved region-based active contour model in a variational level set formulation. We define an energy functional with a local intensity fitting term, which induces a local force to attract the contour and stops it at object boundaries, and an auxiliary global intensity fitting term, which drives the motion of the contour far away from object boundaries. Therefore, the combination of these two forces allows for flexible initialization of the contours. This energy is then incorporated into a level set formulation with a level set regularization term that is necessary for accurate computation in the corresponding level set method. The proposed model is first presented as a two-phase level set formulation and then extended to a multi-phase formulation. Experimental results show the advantages of our method in terms of accuracy and robustness. In particular, our method has been applied to brain MR image segmentation with desirable results.

An improved level set method for brain MR images segmentation and bias correction

Intensity inhomogeneities cause considerable difficulty in the quantitative analysis of magnetic resonance (MR) images. Thus, bias field estimation is a necessary step before quantitative analysis of MR data can be undertaken. This paper presents a variational level set approach to bias correction and segmentation for images with intensity inhomogeneities. Our method is based on an observation that intensities in a relatively small local region are separable, despite of the inseparability of the intensities in the whole image caused by the overall intensity inhomogeneity. We first define a localized K-means-type clustering objective function for image intensities in a neighborhood around each point. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. The objective function is then integrated over the entire domain to define the data term into the level set framework. Our method is able to capture bias of quite general profiles. Moreover, it is robust to initialization, and thereby allows fully automated applications. The proposed method has been used for images of various modalities with promising results.

Mapping the effect of APOE epsilon4 on gray matter loss in Alzheimer's disease in vivo

Previous studies suggest that in Alzheimer's disease (AD) the Apolipoprotein E (APOE) epsilon4 allele is associated with greater vulnerability of medial temporal lobe structures. However, less is known about its effect on the whole cortical mantle. Here we aimed to identify APOE-related patterns of cortical atrophy in AD using an advanced computational anatomy technique. We studied 15 AD patients carriers (epsilon4+, age: 72+/-10 SD years, MMSE: 20+/-3 SD) and 14 non-carriers (epsilon4-, age: 69+/-9, MMSE: 20+/-5) of the epsilon4 allele and compared them to 29 age-and-sex matched controls (age: 70+/-9, MMSE: 28+/-1). Each subject underwent a clinical evaluation, a neuropsychological battery, and high-resolution MRI. UCLA's cortical pattern matching technique was used to identify regions of local cortical atrophy. epsilon4+ and epsilon4- patients showed similar performance on neuropsychological tests (p>.05, t-test). Diffuse cortical atrophy was detected for both epsilon4+ (p=.0001, permutation test) and epsilon4- patients (p=.0001, permutation test) relative to controls, and overall gray matter loss was about 15% in each patients group. Differences in gray matter loss between carriers and non-carriers mapped to the temporal cortex and right occipital pole (20% greater loss in carriers) and to the posterior cingulate, left orbitofrontal and dorsal fronto-parietal cortex (5-15% greater loss in non-carriers). APOE effect in AD was not significant (p>.74, ANOVA), but a significant APOE by region (temporal vs fronto-parietal cortex) interaction was detected (p=.002, ANOVA), in both early and late-onset patients (p<.05, ANOVA). We conclude that the epsilon4 allele modulates disease phenotype in AD, being associated with a pattern of differential temporal and fronto-parietal vulnerability.

Nosologic imaging of the brain: segmentation and classification using MRI and MRSI

A new technique is presented to create nosologic images of the brain based on magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging (MRSI). A nosologic image summarizes the presence of different tissues and lesions in a single image by color coding each voxel or pixel according to the histopathological class it is assigned to. The proposed technique applies advanced methods from image processing as well as pattern recognition to segment and classify brain tumors. First, a registered brain atlas and a subject-specific abnormal tissue prior, obtained from MRSI data, are used for the segmentation. Next, the detected abnormal tissue is classified based on supervised pattern recognition methods. Class probabilities are also calculated for the segmented abnormal region. Compared to previous approaches, the new framework is more flexible and able to better exploit spatial information leading to improved nosologic images. The combined scheme offers a new way to produce high-resolution nosologic images, representing tumor heterogeneity and class probabilities, which may help clinicians in decision making.

Teasing apart the heterogeneity of autism: Same behavior, different brains in toddlers with fragile X syndrome and autism

To examine brain volumes in substructures associated with the behavioral features of children with FXS compared to children with idiopathic autism and controls. A cross-sectional study of brain substructures was conducted at the first time-point as part of an ongoing longitudinal MRI study of brain development in FXS. The study included 52 boys between 18-42 months of age with FXS and 118 comparison children (boys with autism-non FXS, developmental-delay, and typical development). Children with FXS and autistic disorder had substantially enlarged caudate volume and smaller amygdala volume; whereas those children with autistic disorder without FXS (i.e., idiopathic autism) had only modest enlargement in their caudate nucleus volumes but more robust enlargement of their amygdala volumes. Although we observed this double dissociation among selected brain volumes, no significant differences in severity of autistic behavior between these groups were observed. This study offers a unique examination of early brain development in two disorders, FXS and idiopathic autism, with overlapping behavioral features, but two distinct patterns of brain morphology. We observed that despite almost a third of our FXS sample meeting criteria for autism, the profile of brain volume differences for children with FXS and autism differed from those with idiopathic autism. These findings underscore the importance of addressing heterogeneity in studies of autistic behavior.

Widespread neocortical abnormalities in temporal lobe epilepsy with and without mesial sclerosis

PURPOSE

Extrafocal structural abnormalities have been consistently described in temporal lobe epilepsy (TLE) with mesial temporal lobe sclerosis (TLE-MTS). In TLE without MTS (TLE-no) extrafocal abnormalities are more subtle and often require region of interest analyses for their detection. Cortical thickness measurements might be better suited to detect such subtle abnormalities than conventional whole brain volumetric techniques which are often negative in TLE-no. The aim of this study was to seek and characterize patterns of cortical thinning in TLE-MTS and TLE-no.

METHODS

T1 weighted whole brain images were acquired on a 4 T magnet in 66 subjects (35 controls, 15 TLE-MTS, 16 TLE-no). Cortical thickness measurements were obtained using the FreeSurfer software routine. Group comparisons and correlation analyses were done using the statistical routine of FreeSurfer (FDR, p=0.05).

RESULTS

TLE-MTS and TLE-no showed both widespread temporal and extratemporal cortical thinning. In TLE-MTS, the inferior medial and posterior temporal regions were most prominently affected while lateral temporal and opercular regions were more affected in TLE-no. The correlation analysis showed a significant correlation between the ipsilateral hippocampal volume and regions of thinning in TLE-MTS and between inferior temporal cortical thickness and thinning in extratemporal cortical regions in TLE-no.

CONCLUSION

The pattern of thinning in TLE-no was different from the pattern in TLE-MTS. This finding suggests that different epileptogenic networks could be involved in TLE-MTS and TLE and further supports the hypothesis that TLE-MTS and TLE-no might represent two distinct TLE syndromes.

Inhomogeneity compensation for MR brain image segmentation using a multi-stage FCM-based approach

Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for MR image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into clustering algorithms. This paper proposes a multiple stage fuzzy c-means (FCM) based algorithm for the estimation and compensation of the slowly varying additive or multiplicative noise, supported by a pre-filtering technique for Gaussian and impulse noise elimination. The slowly varying behavior of the bias or gain field is assured by a smoothening filter that performs a context dependent averaging, based on a morphological criterion. The experiments using 2-D synthetic phantoms and real MR images show, that the proposed method provides accurate segmentation. The produced segmentation and fuzzy membership values can serve as excellent support for 3-D registration and segmentation techniques.