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

Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures

Healthy ageing coincides with a progressive decline of brain gray matter (GM) ultimately affecting the entire brain. For a long time, manual delineation-based volumetry within predefined regions of interest (ROI) has been the gold standard for assessing such degeneration. Voxel-Based Morphometry (VBM) offers an automated alternative approach that, however, relies critically on the segmentation and spatial normalization of a large collection of images from different subjects. This can be achieved via different algorithms, with SPM5/SPM8, DARTEL of SPM8 and FSL tools (FAST, FNIRT) being three of the most frequently used. We complemented these voxel based measurements with a ROI based approach, whereby the ROIs are defined by transforms of an atlas (containing different tissue probability maps as well as predefined anatomic labels) to the individual subject images in order to obtain volumetric information at the level of the whole brain or within separate ROIs. Comparing GM decline between 21 young subjects (mean age 23) and 18 elderly (mean age 66) revealed that volumetric measurements differed significantly between methods. The unified segmentation/normalization of SPM5/SPM8 revealed the largest age-related differences and DARTEL the smallest, with FSL being more similar to the DARTEL approach. Method specific differences were substantial after segmentation and most pronounced for the cortical structures in close vicinity to major sulci and fissures. Our findings suggest that algorithms that provide only limited degrees of freedom for local deformations (such as the unified segmentation and normalization of SPM5/SPM8) tend to overestimate between-group differences in VBM results when compared to methods providing more flexible warping. This difference seems to be most pronounced if the anatomy of one of the groups deviates from custom templates, a finding that is of particular importance when results are compared across studies using different VBM methods.

Cortical gamma-aminobutyric acid and glutamate in posttraumatic stress disorder and their relationships to self-reported sleep quality

STUDY OBJECTIVES

To test if posttraumatic stress disorder (PTSD) is associated with low brain gamma-aminobutyric acid (GABA) levels and if reduced GABA is mediated by poor sleep quality.

DESIGN

Laboratory study using in vivo proton magnetic resonance spectroscopy (1H MRS) and behavioral testing.

SETTING

VA Medical Center Research Service, Psychiatry and Radiology.

PATIENTS OR PARTICIPANTS

Twenty-seven patients with PTSD (PTSD+) and 18 trauma-exposed controls without PTSD (PTSD-), recruited from United States Army reservists, Army National Guard, and mental health clinics.

INTERVENTIONS

None.

MEASUREMENTS AND RESULTS

1H MRS at 4 Tesla yielded spectra from three cortical brain regions. In parieto-occipital and temporal cortices, PTSD+ had lower GABA concentrations than PTSD-. As expected, PTSD+ had higher depressive and anxiety symptom scores and a higher Insomnia Severity Index (ISI) score. Higher ISI correlated with lower GABA and higher glutamate levels in parieto-occipital cortex and tended to correlate with lower GABA in the anterior cingulate. The relationship between parieto-occipital GABA and PTSD diagnosis was fully mediated through insomnia severity. Lower N-acetylaspartate and glutamate concentrations in the anterior cingulate cortex correlated with higher arousal scores, whereas depressive and anxiety symptoms did generally not influence metabolite concentrations.

CONCLUSIONS

Low brain gamma-aminobutyric acid (GABA) concentration in posttraumatic stress disorder (PTSD) is consistent with most findings in panic and social anxiety disorders. Low GABA associated with poor sleep quality is consistent with the hyperarousal theory of both primary insomnia and PTSD. Our data demonstrate that poor sleep quality mediates low parieto-occipital GABA in PTSD. The findings have implications for PTSD treatment approaches.

Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation

This paper proposes a new energy minimization method called multiplicative intrinsic component optimization (MICO) for joint bias field estimation and segmentation of magnetic resonance (MR) images. The proposed method takes full advantage of the decomposition of MR images into two multiplicative components, namely, the true image that characterizes a physical property of the tissues and the bias field that accounts for the intensity inhomogeneity, and their respective spatial properties. Bias field estimation and tissue segmentation are simultaneously achieved by an energy minimization process aimed to optimize the estimates of the two multiplicative components of an MR image. The bias field is iteratively optimized by using efficient matrix computations, which are verified to be numerically stable by matrix analysis. More importantly, the energy in our formulation is convex in each of its variables, which leads to the robustness of the proposed energy minimization algorithm. The MICO formulation can be naturally extended to 3D/4D tissue segmentation with spatial/sptatiotemporal regularization. Quantitative evaluations and comparisons with some popular softwares have demonstrated superior performance of MICO in terms of robustness and accuracy.

Testing for association of the monoamine oxidase A promoter polymorphism with brain structure volumes in both autism and the fragile X syndrome

BACKGROUND

Autism and the fragile X syndrome (FXS) are related to each other genetically and symptomatically. A cardinal biological feature of both disorders is abnormalities of cerebral cortical brain volumes. We have previously shown that the monoamine oxidase A (MAOA) promoter polymorphism is associated with cerebral cortical volumes in children with autism, and we now sought to determine whether the association was also present in children with FXS.

METHODS

Participants included 47 2-year-old Caucasian boys with FXS, some of whom also had autism, as well as 34 2-year-old boys with idiopathic autism analyzed in a previous study. The MAOA promoter polymorphism was genotyped and tested for relationships with gray and white matter volumes of the cerebral cortical lobes and cerebro-spinal fluid volume of the lateral ventricles.

RESULTS

MAOA genotype effects in FXS children were the same as those previously observed in idiopathic autism: the low activity MAOA promoter polymorphism allele was associated with increased gray and white matter volumes in all cerebral lobes. The effect was most pronounced in frontal lobe gray matter and all three white matter regions: frontal gray, F = 4.39, P = 0.04; frontal white, F = 5.71, P = 0.02; temporal white, F = 4.73, P = 0.04; parieto-occipital white, F = 5.00, P = 0.03. Analysis of combined FXS and idiopathic autism samples produced P values for these regions <0.01 and effect sizes of approximately 0.10.

CONCLUSIONS

The MAOA promoter polymorphism is similarly associated with brain structure volumes in both idiopathic autism and FXS. These data illuminate a number of important aspects of autism and FXS heritability: a genetic effect on a core biological trait of illness, the specificity/generalizability of the genetic effect, and the utility of examining individual genetic effects on the background of a single gene disorder such as FXS.

Ensemble segmentation for GBM brain tumors on MR images using confidence-based averaging

PURPOSE

Ensemble segmentation methods combine the segmentation results of individual methods into a final one, with the goal of achieving greater robustness and accuracy. The goal of this study was to develop an ensemble segmentation framework for glioblastoma multiforme tumors on single-channel T1w postcontrast magnetic resonance images.

METHODS

Three base methods were evaluated in the framework: fuzzy connectedness, GrowCut, and voxel classification using support vector machine. A confidence map averaging (CMA) method was used as the ensemble rule.

RESULTS

The performance is evaluated on a comprehensive dataset of 46 cases including different tumor appearances. The accuracy of the segmentation result was evaluated using the F1-measure between the semiautomated segmentation result and the ground truth.

CONCLUSIONS

The results showed that the CMA ensemble result statistically approximates the best segmentation result of all the base methods for each case.

PORTR: Pre-operative and post-recurrence brain tumor registration

We propose a new method for deformable registration of pre-operative and post-recurrence brain MR scans of glioma patients. Performing this type of intra-subject registration is challenging as tumor, resection, recurrence, and edema cause large deformations, missing correspondences, and inconsistent intensity profiles between the scans. To address this challenging task, our method, called PORTR, explicitly accounts for pathological information. It segments tumor, resection cavity, and recurrence based on models specific to each scan. PORTR then uses the resulting maps to exclude pathological regions from the image-based correspondence term while simultaneously measuring the overlap between the aligned tumor and resection cavity. Embedded into a symmetric registration framework, we determine the optimal solution by taking advantage of both discrete and continuous search methods. We apply our method to scans of 24 glioma patients. Both quantitative and qualitative analysis of the results clearly show that our method is superior to other state-of-the-art approaches.

Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline

Automated segmenting and labeling of individual brain anatomical regions, in MRI are challenging, due to the issue of individual structural variability. Although atlas-based segmentation has shown its potential for both tissue and structure segmentation, due to the inherent natural variability as well as disease-related changes in MR appearance, a single atlas image is often inappropriate to represent the full population of datasets processed in a given neuroimaging study. As an alternative for the case of single atlas segmentation, the use of multiple atlases alongside label fusion techniques has been introduced using a set of individual “atlases” that encompasses the expected variability in the studied population. In our study, we proposed a multi-atlas segmentation scheme with a novel graph-based atlas selection technique. We first paired and co-registered all atlases and the subject MR scans. A directed graph with edge weights based on intensity and shape similarity between all MR scans is then computed. The set of neighboring templates is selected via clustering of the graph. Finally, weighted majority voting is employed to create the final segmentation over the selected atlases. This multi-atlas segmentation scheme is used to extend a single-atlas-based segmentation toolkit entitled AutoSeg, which is an open-source, extensible C++ based software pipeline employing BatchMake for its pipeline scripting, developed at the Neuro Image Research and Analysis Laboratories of the University of North Carolina at Chapel Hill. AutoSeg performs N4 intensity inhomogeneity correction, rigid registration to a common template space, automated brain tissue classification based skull-stripping, and the multi-atlas segmentation. The multi-atlas-based AutoSeg has been evaluated on subcortical structure segmentation with a testing dataset of 20 adult brain MRI scans and 15 atlas MRI scans. The AutoSeg achieved mean Dice coefficients of 81.73% for the subcortical structures.

Quantifying and modelling tissue maturation in the living human fetal brain

Recent advances in medical imaging are beginning to allow us to quantify brain tissue maturation in the growing human brain prior to normal term age, and are beginning to shed new light on early human brain growth. These advances compliment the work already done in cellular level imaging in animal and post mortem studies of brain development. The opportunities for collaborative research that bridges the gap between macroscopic and microscopic windows on the developing brain are significant. The aim of this paper is to provide a review of the current research into MR imaging of the living fetal brain with the aim of motivating improved interfaces between the two fields. The review begins with a description of faster MRI techniques that are capable of freezing motion of the fetal head during the acquisition of a slice, and how these have been combined with advanced post-processing algorithms to build 3D images from motion scattered slices. Such rich data has motivated the development of techniques to automatically label developing tissue zones within MRI data allowing their quantification in 3D and 4D within the normally growing fetal brain. These methods have provided the basis for later work that has created the first maps of tissue growth rate and cortical folding in normally developing brains in-utero. These measurements provide valuable findings that compliment those derived from post-mortem anatomy, and additionally allow for the possibility of larger population studies of the influence of maternal environmental and genes on early brain development.

Unsupervised segmentation, clustering, and groupwise registration of heterogeneous populations of brain MR images

Population analysis of brain morphology from magnetic resonance images contributes to the study and understanding of neurological diseases. Such analysis typically involves segmentation of a large set of images and comparisons of these segmentations between relevant subgroups of images (e.g., "normal" versus "diseased"). The images of each subgroup are usually selected in advance in a supervised way based on clinical knowledge. Their segmentations are typically guided by one or more available atlases, assumed to be suitable for the images at hand. We present a data-driven probabilistic framework that simultaneously performs atlas-guided segmentation of a heterogeneous set of brain MR images and clusters the images in homogeneous subgroups, while constructing separate probabilistic atlases for each cluster to guide the segmentation. The main benefits of integrating segmentation, clustering and atlas construction in a single framework are that: 1) our method can handle images of a heterogeneous group of subjects and automatically identifies homogeneous subgroups in an unsupervised way with minimal prior knowledge, 2) the subgroups are formed by automatical detection of the relevant morphological features based on the segmentation, 3) the atlases used by our method are constructed from the images themselves and optimally adapted for guiding the segmentation of each subgroup, and 4) the probabilistic atlases represent the morphological pattern that is specific for each subgroup and expose the groupwise differences between different subgroups. We demonstrate the feasibility of the proposed framework and evaluate its performance with respect to image segmentation, clustering and atlas construction on simulated and real data sets including the publicly available BrainWeb and ADNI data. It is shown that combined segmentation and atlas construction leads to improved segmentation accuracy. Furthermore, it is demonstrated that the clusters generated by our unsupervised framework largely coincide with the clinically determined subgroups in case of disease-specific differences in brain morphology and that the differences between the cluster-specific atlases are in agreement with the expected disease-specific patterns, indicating that our method is capable of detecting the different modes in a population. Our method can thus be seen as a comprehensive image-driven population analysis framework that can contribute to the detection of novel subgroups and distinctive image features, potentially leading to new insights in the brain development and disease.

Combining generative models for multifocal glioma segmentation and registration

In this paper, we propose a new method for simultaneously segmenting brain scans of glioma patients and registering these scans to a normal atlas. Performing joint segmentation and registration for brain tumors is very challenging when tumors include multifocal masses and have complex shapes with heterogeneous textures. Our approach grows tumors for each mass from multiple seed points using a tumor growth model and modifies a normal atlas into one with tumors and edema using the combined results of grown tumors. We also generate a tumor shape prior via the random walk with restart, utilizing multiple tumor seeds as initial foreground information. We then incorporate this shape prior into an EM framework which estimates the mapping between the modified atlas and the scans, posteriors for each tissue labels, and the tumor growth model parameters. We apply our method to the BRATS 2013 leaderboard dataset to evaluate segmentation performance. Our method shows the best performance among all participants.

Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge

Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p<0.05) and had an efficient implementation with a run time of 8min and 3s per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi-atlas registration, both on accuracy and computation time. Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, we showed that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained. All results are available online at http://promise12.grand-challenge.org/.

Registration of 3D fetal neurosonography and MRI

We propose a method for registration of 3D fetal brain ultrasound with a reconstructed magnetic resonance fetal brain volume. This method, for the first time, allows the alignment of models of the fetal brain built from magnetic resonance images with 3D fetal brain ultrasound, opening possibilities to develop new, prior information based image analysis methods for 3D fetal neurosonography. The reconstructed magnetic resonance volume is first segmented using a probabilistic atlas and a pseudo ultrasound image volume is simulated from the segmentation. This pseudo ultrasound image is then affinely aligned with clinical ultrasound fetal brain volumes using a robust block-matching approach that can deal with intensity artefacts and missing features in the ultrasound images. A qualitative and quantitative evaluation demonstrates good performance of the method for our application, in comparison with other tested approaches. The intensity average of 27 ultrasound images co-aligned with the pseudo ultrasound template shows good correlation with anatomy of the fetal brain as seen in the reconstructed magnetic resonance image.

Robust multi-site MR data processing: iterative optimization of bias correction, tissue classification, and registration

A robust multi-modal tool, for automated registration, bias correction, and tissue classification, has been implemented for large-scale heterogeneous multi-site longitudinal MR data analysis. This work focused on improving the an iterative optimization framework between bias-correction, registration, and tissue classification inspired from previous work. The primary contributions are robustness improvements from incorporation of following four elements: (1) utilize multi-modal and repeated scans, (2) incorporate high-deformable registration, (3) use extended set of tissue definitions, and (4) use of multi-modal aware intensity-context priors. The benefits of these enhancements were investigated by a series of experiments with both simulated brain data set (BrainWeb) and by applying to highly-heterogeneous data from a 32 site imaging study with quality assessments through the expert visual inspection. The implementation of this tool is tailored for, but not limited to, large-scale data processing with great data variation with a flexible interface. In this paper, we describe enhancements to a joint registration, bias correction, and the tissue classification, that improve the generalizability and robustness for processing multi-modal longitudinal MR scans collected at multi-sites. The tool was evaluated by using both simulated and simulated and human subject MRI images. With these enhancements, the results showed improved robustness for large-scale heterogeneous MRI processing.

Is the macromolecule signal tissue-specific in healthy human brain? A (1)H MRS study at 7 Tesla in the occipital lobe

PURPOSE

The macromolecule signal plays a key role in the precision and the accuracy of the metabolite quantification in short-TE (1) H MR spectroscopy. Macromolecules have been reported at 1.5 Tesla (T) to depend on the cerebral studied region and to be age specific. As metabolite concentrations vary locally, information about the profile of the macromolecule signal in different tissues may be of crucial importance.

METHODS

The aim of this study was to investigate, at 7T for healthy subjects, the neurochemical profile differences provided by macromolecule signal measured in two different tissues in the occipital lobe, predominantly composed of white matter tissue or of grey matter tissue.

RESULTS

White matter-rich macromolecule signal was relatively lower than the gray matter-rich macromolecule signal from 1.5 to 1.8 ppm and from 2.3 to 2.5 ppm with mean difference over these regions of 7% and 12% (relative to the reference peak at 0.9 ppm), respectively. The neurochemical profiles, when using either of the two macromolecule signals, were similar for 11 reliably quantified metabolites (CRLB < 20%) with relatively small concentration differences (< 0.3 μmol/g), except Glu (± 0.8 μmol/g).

CONCLUSION

Given the small quantification differences, we conclude that a general macromolecule baseline provides a sufficiently accurate neurochemical profile in occipital lobe at 7T in healthy human brain.

Level set segmentation of medical images based on local region statistics and maximum a posteriori probability

This paper presents a variational level set method for simultaneous segmentation and bias field estimation of medical images with intensity inhomogeneity. In our model, the statistics of image intensities belonging to each different tissue in local regions are characterized by Gaussian distributions with different means and variances. According to maximum a posteriori probability (MAP) and Bayes' rule, we first derive a local objective function for image intensities in a neighborhood around each pixel. Then this local objective function is integrated with respect to the neighborhood center over the entire image domain to give a global criterion. In level set framework, this global criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, image segmentation and bias field estimation are simultaneously achieved via a level set evolution process. Experimental results for synthetic and real images show desirable performances of our method.

Striatal shape in Parkinson's disease

Parkinson's disease (PD) is marked pathologically by nigrostriatal dopaminergic terminal loss. Histopathological and in vivo labeling studies demonstrate that this loss occurs most extensively in the caudal putamen and caudate head. Previous structural studies have suggested reduced striatal volume and atrophy of the caudate head in PD subjects. The spatial distribution of atrophy in the putamen, however, has not been characterized. We aimed to delineate the specific locations of atrophy in both of these striatal structures. T1- and T2-weighted brain MR (3T) images were obtained from 40 PD and 40 control subjects having no dementia and similar age and gender distributions. Shape analysis was performed using doubly segmented regions of interest. Compared to controls, PD subjects had lower putamen (p = 0.0003) and caudate (p = 0.0003) volumes. Surface contraction magnitudes were greatest on the caudal putamen (p ≤ 0.005) and head and dorsal body of the caudate (p ≤ 0.005). This spatial distribution of striatal atrophy is consistent with the known pattern of dopamine depletion in PD and may reflect global consequences of known cellular remodeling phenomena.

DevStaR: high-throughput quantification of C. elegans developmental stages

We present DevStaR, an automated computer vision and machine learning system that provides rapid, accurate, and quantitative measurements of C. elegans embryonic viability in high-throughput (HTP) applications. A leading genetic model organism for the study of animal development and behavior, C. elegans is particularly amenable to HTP functional genomic analysis due to its small size and ease of cultivation, but the lack of efficient and quantitative methods to score phenotypes has become a major bottleneck. DevStaR addresses this challenge using a novel hierarchical object recognition machine that rapidly segments, classifies, and counts animals at each developmental stage in images of mixed-stage populations of C. elegans. Here, we describe the algorithmic design of the DevStaR system and demonstrate its performance in scoring image data acquired in HTP screens.

An efficient algorithm for multiphase image segmentation with intensity bias correction

This paper presents a variational model for simultaneous multiphase segmentation and intensity bias estimation for images corrupted by strong noise and intensity inhomogeneity. Since the pixel intensities are not reliable samples for region statistics due to the presence of noise and intensity bias, we use local information based on the joint density within image patches to perform image partition. Hence, the pixel intensity has a multiplicative distribution structure. Then, the maximum-a-posteriori (MAP) principle with those pixel density functions generates the model. To tackle the computational problem of the resultant nonsmooth nonconvex minimization, we relax the constraint on the characteristic functions of partition regions, and apply primal-dual alternating gradient projections to construct a very efficient numerical algorithm. We show that all the variables have closed-form solutions in each iteration, and the computation complexity is very low. In particular, the algorithm involves only regular convolutions and pointwise projections onto the unit ball and canonical simplex. Numerical tests on a variety of images demonstrate that the proposed algorithm is robust, stable, and attains significant improvements on accuracy and efficiency over the state-of-the-arts.

MR parameter quantification with magnetization-prepared double echo steady-state (MP-DESS)

PURPOSE

The mapping of MR relaxation times and proton density has been the subject of research in medical imaging for many years, as it offers the possibility for longitudinal investigation of disease and the correlation with related biochemical processes. The purpose of this study is to provide a fast mapping protocol, which simultaneously acquires MR relaxation times and relative proton density without compromising accuracy and precision.

METHODS

This work presents a novel magnetization-prepared double echo steady-state (MP-DESS) sequence, which was designed to be sensitive to parameter variations of interest, and insensitive to variations of confounding variables. It provides high sensitivity against variations of the MR relaxation times, high acquisition efficiency, and it is insensitive to off-resonance. Accurate phase graph modeling of the MP-DESS signal is used to obtain unbiased parameter estimates.

RESULTS

The approach is validated in phantom and in vivo measurements. A whole-brain acquisition of 1.4-mm isotropic resolution was acquired in 15 min. Comparisons to gold-standard methods suggest a mapping precision of 5% for T1 and M0 , and below 10% for T2.

CONCLUSION

A new quantitative imaging technique is introduced that allows fast and isotropic simultaneous MR parameter mapping of T1, T2, and M0.

Patterns of reduced cortical thickness in late-life depression and relationship to psychotherapeutic response

OBJECTIVE

Cortical atrophy has been associated with late-life depression (LLD) and recent findings suggest that reduced right hemisphere cortical thickness is associated with familial risk for major depressive disorder, but cortical thickness abnormalities in LLD have not been explored. Furthermore, cortical atrophy has been posited as a contributor to poor antidepressant treatment response in LLD, but the impact of cortical thickness on psychotherapy response is unknown. This study was conducted to evaluate patterns of cortical thickness in LLD and in relation to psychotherapy treatment outcomes.

METHODS

Participants included 22 individuals with LLD and 12 age-matched comparison subjects. LLD participants completed 12 weeks of psychotherapy and treatment response was defined as a 50% reduction in depressive symptoms. All participants underwent magnetic resonance imaging of the brain, and cortical mapping of gray matter tissue thickness was calculated.

RESULTS

LLD individuals demonstrated thinner cortex than controls prominently in the right frontal, parietal, and temporal brain regions. Eleven participants (50%) exhibited positive psychotherapy response after 12 weeks of treatment. Psychotherapy nonresponders demonstrated thinner cortex in bilateral posterior cingulate and parahippocampal cortices, left paracentral, precuneus, cuneus, and insular cortices, and the right medial orbitofrontal and lateral occipital cortices relative to treatment responders.

CONCLUSIONS

Our findings suggest more distributed right hemisphere cortical abnormalities in LLD than have been previously reported. In addition, our findings suggest that reduced bilateral cortical thickness may be an important phenotypic marker of individuals at higher risk for poor response to psychotherapy.

Localized FCM Clustering with Spatial Information for Medical Image Segmentation and Bias Field Estimation

This paper presents a novel fuzzy energy minimization method for simultaneous segmentation and bias field estimation of medical images. We first define an objective function based on a localized fuzzy c-means (FCM) clustering for the image intensities in a neighborhood around each point. Then, this objective function is integrated with respect to the neighborhood center over the entire image domain to formulate a global fuzzy energy, which depends on membership functions, a bias field that accounts for the intensity inhomogeneity, and the constants that approximate the true intensities of the corresponding tissues. Therefore, segmentation and bias field estimation are simultaneously achieved by minimizing the global fuzzy energy. Besides, to reduce the impact of noise, the proposed algorithm incorporates spatial information into the membership function using the spatial function which is the summation of the membership functions in the neighborhood of each pixel under consideration. Experimental results on synthetic and real images are given to demonstrate the desirable performance of the proposed algorithm.

Voxel-wise co-analysis of macro- and microstructural brain alteration in mild cognitive impairment and Alzheimer's disease using anatomical and diffusion MRI

BACKGROUND AND PURPOSE

To determine if a voxel-wise "co-analysis" of structural and diffusion tensor magnetic resonance imaging (MRI) together reveals additional brain regions affected in mild cognitive impairment (MCI) and Alzheimer's disease (AD) than voxel-wise analysis of the individual MRI modalities alone.

METHODS

Twenty-one patients with MCI, 21 patients with AD, and 21 cognitively normal healthy elderly were studied with MRI. Maps of deformation and fractional anisotropy (FA) were computed and used as dependent variables in univariate and multivariate statistical models.

RESULTS

Univariate voxel-wise analysis of macrostructural changes in MCI showed atrophy in the right anterior temporal lobe, left posterior parietal/precuneus region, WM adjacent to the cingulate gyrus, and dorsolateral prefrontal regions, consistent with prior research. Univariate voxel-wise analysis of microstructural changes in MCI showed reduced FA in the left posterior parietal region extending into the corpus callosum, consistent with previous work. The multivariate analysis, which provides more information than univariate tests when structural and FA measures are correlated, revealed additional MCI-related changes in corpus callosum and temporal lobe.

CONCLUSION

These results suggest that in corpus callosum and temporal regions macro- and microstructural variations in MCI can be congruent, providing potentially new insight into the mechanisms of brain tissue degeneration.

Fast, sequence adaptive parcellation of brain MR using parametric models

In this paper we propose a method for whole brain parcellation using the type of generative parametric models typically used in tissue classification. Compared to the non-parametric, multi-atlas segmentation techniques that have become popular in recent years, our method obtains state-of-the-art segmentation performance in both cortical and subcortical structures, while retaining all the benefits of generative parametric models, including high computational speed, automatic adaptiveness to changes in image contrast when different scanner platforms and pulse sequences are used, and the ability to handle multi-contrast (vector-valued intensities) MR data. We have validated our method by comparing its segmentations to manual delineations both within and across scanner platforms and pulse sequences, and show preliminary results on multi-contrast test-retest scans, demonstrating the feasibility of the approach.

An improved variational level set method for MR image segmentation and bias field correction

In this paper, we propose an improved variational level set approach to correct the bias and to segment the magnetic resonance (MR) images with inhomogeneous intensity. First, we use a Gaussian distribution with bias field as a local region descriptor in two-phase level set formulation for segmentation and bias field correction of the images with inhomogeneous intensities. By using the information of the local variance in this descriptor, our method is able to obtain accurate segmentation results. Furthermore, we extend this method to three-phase level set formulation for brain MR image segmentation and bias field correction. By using this three-phase level set function to replace the four-phase level set function, we can reduce the number of convolution operations in each iteration and improve the efficiency. Compared with other approaches, this algorithm demonstrates a superior performance.

Generalized rough fuzzy c-means algorithm for brain MR image segmentation

Fuzzy sets and rough sets have been widely used in many clustering algorithms for medical image segmentation, and have recently been combined together to better deal with the uncertainty implied in observed image data. Despite of their wide spread applications, traditional hybrid approaches are sensitive to the empirical weighting parameters and random initialization, and hence may produce less accurate results. In this paper, a novel hybrid clustering approach, namely the generalized rough fuzzy c-means (GRFCM) algorithm is proposed for brain MR image segmentation. In this algorithm, each cluster is characterized by three automatically determined rough-fuzzy regions, and accordingly the membership of each pixel is estimated with respect to the region it locates. The importance of each region is balanced by a weighting parameter, and the bias field in MR images is modeled by a linear combination of orthogonal polynomials. The weighting parameter estimation and bias field correction have been incorporated into the iterative clustering process. Our algorithm has been compared to the existing rough c-means and hybrid clustering algorithms in both synthetic and clinical brain MR images. Experimental results demonstrate that the proposed algorithm is more robust to the initialization, noise, and bias field, and can produce more accurate and reliable segmentations.

Automatic MRI meningioma segmentation using estimation maximization

With the advancement of the imaging facility and image processing technique, computer assisted surgical planning and image guided technology have become increasingly used in neurosurgery. For MRI has the characteristic of multi-spectral image data., so knowledge-base techniques is widely used in brain MRI segmentation. Here we recognize the location of the tumor automatically and provide an accurate result by Estimation Maximization method. Simultaneously, promote the efficiency of reading image as well.

Trajectories of early brain volume development in fragile X syndrome and autism

OBJECTIVE

To examine patterns of early brain growth in young children with fragile X syndrome (FXS) compared with a comparison group (controls) and a group with idiopathic autism.

METHOD

The study included 53 boys 18 to 42 months of age with FXS, 68 boys with idiopathic autism (autism spectrum disorder), and a comparison group of 50 typically developing and developmentally delayed controls. Structural brain volumes were examined using magnetic resonance imaging across two time points, at 2 to 3 and again at 4 to 5 years of age, and total brain volumes and regional (lobar) tissue volumes were examined. In addition, a selected group of subcortical structures implicated in the behavioral features of FXS (e.g., basal ganglia, hippocampus, amygdala) was studied.

RESULTS

Children with FXS had larger global brain volumes compared with controls but were not different than children with idiopathic autism, and the rate of brain growth from 2 to 5 years of age paralleled that seen in controls. In contrast to children with idiopathic autism who had generalized cortical lobe enlargement, children with FXS showed specific enlargement in the temporal lobe white matter, cerebellar gray matter, and caudate nucleus, but a significantly smaller amygdala.

CONCLUSIONS

This structural longitudinal magnetic resonance imaging study of preschoolers with FXS observed generalized brain overgrowth in children with FXS compared with controls, evident at age 2 and maintained across ages 4 to 5. In addition, different patterns of brain growth that distinguished boys with FXS from boys with idiopathic autism were found.

An open source multivariate framework for n-tissue segmentation with evaluation on public data

We introduce Atropos, an ITK-based multivariate n-class open source segmentation algorithm distributed with ANTs ( http://www.picsl.upenn.edu/ANTs). The Bayesian formulation of the segmentation problem is solved using the Expectation Maximization (EM) algorithm with the modeling of the class intensities based on either parametric or non-parametric finite mixtures. Atropos is capable of incorporating spatial prior probability maps (sparse), prior label maps and/or Markov Random Field (MRF) modeling. Atropos has also been efficiently implemented to handle large quantities of possible labelings (in the experimental section, we use up to 69 classes) with a minimal memory footprint. This work describes the technical and implementation aspects of Atropos and evaluates its performance on two different ground-truth datasets. First, we use the BrainWeb dataset from Montreal Neurological Institute to evaluate three-tissue segmentation performance via (1) K-means segmentation without use of template data; (2) MRF segmentation with initialization by prior probability maps derived from a group template; (3) Prior-based segmentation with use of spatial prior probability maps derived from a group template. We also evaluate Atropos performance by using spatial priors to drive a 69-class EM segmentation problem derived from the Hammers atlas from University College London. These evaluation studies, combined with illustrative examples that exercise Atropos options, demonstrate both performance and wide applicability of this new platform-independent open source segmentation tool.

Reconstruction of fetal brain MRI with intensity matching and complete outlier removal

We propose a method for the reconstruction of volumetric fetal MRI from 2D slices, comprising super-resolution reconstruction of the volume interleaved with slice-to-volume registration to correct for the motion. The method incorporates novel intensity matching of acquired 2D slices and robust statistics which completely excludes identified misregistered or corrupted voxels and slices. The reconstruction method is applied to motion-corrupted data simulated from MRI of a preterm neonate, as well as 10 clinically acquired thick-slice fetal MRI scans and three scan-sequence optimized thin-slice fetal datasets. The proposed method produced high quality reconstruction results from all the datasets to which it was applied. Quantitative analysis performed on simulated and clinical data shows that both intensity matching and robust statistics result in statistically significant improvement of super-resolution reconstruction. The proposed novel EM-based robust statistics also improves the reconstruction when compared to previously proposed Huber robust statistics. The best results are obtained when thin-slice data and the correct approximation of the point spread function is used. This paper addresses the need for a comprehensive reconstruction algorithm of 3D fetal MRI, so far lacking in the scientific literature.

Formulating spatially varying performance in the statistical fusion framework

To date, label fusion methods have primarily relied either on global [e.g., simultaneous truth and performance level estimation (STAPLE), globally weighted vote] or voxelwise (e.g., locally weighted vote) performance models. Optimality of the statistical fusion framework hinges upon the validity of the stochastic model of how a rater errs (i.e., the labeling process model). Hitherto, approaches have tended to focus on the extremes of potential models. Herein, we propose an extension to the STAPLE approach to seamlessly account for spatially varying performance by extending the performance level parameters to account for a smooth, voxelwise performance level field that is unique to each rater. This approach, Spatial STAPLE, provides significant improvements over state-of-the-art label fusion algorithms in both simulated and empirical data sets.

Fuzzy local Gaussian mixture model for brain MR image segmentation

Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of noise and intensity inhomogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy. In this paper, we assume that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation. This algorithm estimates the segmentation result that maximizes the posterior probability by minimizing an objective energy function, in which a truncated Gaussian kernel function is used to impose the spatial constraint and fuzzy memberships are employed to balance the contribution of each GMM. We compared our algorithm to state-of-the-art segmentation approaches in both synthetic and clinical data. Our results show that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and substantially improve the accuracy of brain MR image segmentation.

Cortical surface mapping using topology correction, partial flattening and 3D shape context-based non-rigid registration for use in quantifying atrophy in Alzheimer's disease

Magnetic resonance (MR) provides a non-invasive way to investigate changes in the brain resulting from aging or neurodegenerative disorders such as Alzheimer's disease (AD). Performing accurate analysis for population studies is challenging because of the interindividual anatomical variability. A large set of tools is found to perform studies of brain anatomy and population analysis (FreeSurfer, SPM, FSL). In this paper we present a newly developed surface-based processing pipeline (MILXCTE) that allows accurate vertex-wise statistical comparisons of brain modifications, such as cortical thickness (CTE). The brain is first segmented into the three main tissues: white matter, gray matter and cerebrospinal fluid, after CTE is computed, a topology corrected mesh is generated. Partial inflation and non-rigid registration of cortical surfaces to a common space using shape context are then performed. Each of the steps was firstly validated using MR images from the OASIS database. We then applied the pipeline to a sample of individuals randomly selected from the AIBL study on AD and compared with FreeSurfer. For a population of 50 individuals we found correlation of cortical thickness in all the regions of the brain (average r=0.62 left and r=0.64 right hemispheres). We finally computed changes in atrophy in 32 AD patients and 81 healthy elderly individuals. Significant differences were found in regions known to be affected in AD. We demonstrated the validity of the method for use in clinical studies which provides an alternative to well established techniques to compare different imaging biomarkers for the study of neurodegenerative diseases.

Longitudinally guided level sets for consistent tissue segmentation of neonates

Quantification of brain development as well as disease-induced pathologies in neonates often requires precise delineation of white matter, grey matter and cerebrospinal fluid. Unlike adults, tissue segmentation in neonates is significantly more challenging due to the inherently lower tissue contrast. Most existing methods take a voxel-based approach and are limited to working with images from a single time-point, even though longitudinal scans are available. We take a different approach by taking advantage of the fact that the pattern of the major sulci and gyri are already present in the neonates and generally preserved but fine-tuned during brain development. That is, the segmentation of late-time-point image can be used to guide the segmentation of neonatal image. Accordingly, we propose a novel longitudinally guided level-sets method for consistent neonatal image segmentation by combining local intensity information, atlas spatial prior, cortical thickness constraint, and longitudinal information into a variational framework. The minimization of the proposed energy functional is strictly derived from a variational principle. Validation performed on both simulated and in vivo neonatal brain images shows promising results.

MRI patterns of atrophy and hypoperfusion associations across brain regions in frontotemporal dementia

Magnetic Resonance Imaging (MRI) provides various imaging modes to study the brain. We tested the benefits of a joint analysis of multimodality MRI data in combination with a large-scale analysis that involved simultaneously all image voxels using joint independent components analysis (jICA) and compared the outcome to results using conventional voxel-by-voxel unimodality tests. Specifically, we designed a jICA to decompose multimodality MRI data into independent components that explain joint variations between the image modalities as well as variations across brain regions. We tested the jICA design on structural and perfusion-weighted MRI data from 12 patients diagnosed with behavioral variant frontotemporal dementia (bvFTD) and 12 cognitively normal elderly individuals. While unimodality analyses showed widespread brain atrophy and hypoperfusion in the patients, jICA further revealed two significant joint components of variations between atrophy and hypoperfusion across brain regions. The 1st joint component revealed associated brain atrophy and hypoperfusion predominantly in the right brain hemisphere in behavioral variant frontotemporal dementia, and the 2nd joint component revealed greater atrophy relative to hypoperfusion affecting predominantly the left hemisphere in behavioral variant frontotemporal dementia. The patterns are consistent with the clinical symptoms of behavioral variant frontotemporal dementia that relate to asymmetric compromises of the left and right brain hemispheres. The joint components also revealed that that structural alterations can be associated with physiological alterations in spatially separated but potentially connected brain regions. Finally, jICA outperformed voxel-by-voxel unimodal tests significantly in terms of an effect size, separating the behavioral variant frontotemporal dementia patients from the controls. Taken together, the results demonstrate the benefit of multimodality MRI in conjunction with jICA for mapping neurodegeneration, which may lead ultimately to an improved diagnosis of behavioral variant frontotemporal dementia and other forms of neurodegenerative diseases.

Bias field inconsistency correction of motion-scattered multislice MRI for improved 3D image reconstruction

A common solution to clinical MR imaging in the presence of large anatomical motion is to use fast multislice 2D studies to reduce slice acquisition time and provide clinically usable slice data. Recently, techniques have been developed which retrospectively correct large scale 3D motion between individual slices allowing the formation of a geometrically correct 3D volume from the multiple slice stacks. One challenge, however, in the final reconstruction process is the possibility of varying intensity bias in the slice data, typically due to the motion of the anatomy relative to imaging coils. As a result, slices which cover the same region of anatomy at different times may exhibit different sensitivity. This bias field inconsistency can induce artifacts in the final 3D reconstruction that can impact both clinical interpretation of key tissue boundaries and the automated analysis of the data. Here we describe a framework to estimate and correct the bias field inconsistency in each slice collectively across all motion corrupted image slices. Experiments using synthetic and clinical data show that the proposed method reduces intensity variability in tissues and improves the distinction between key tissue types.

Impact of methodologic choice for automatic detection of different aspects of brain atrophy by using temporal lobe epilepsy as a model

BACKGROUND AND PURPOSE

VBM, DBM, and cortical thickness measurement techniques are commonly used automated methods to detect structural brain changes based on MR imaging. The goal of this study was to demonstrate the pathology detected by the 3 methods and to provide guidance as to which method to choose for specific research questions. This goal was accomplished by 1) identifying structural abnormalities associated with TLE with (TLE-mts) and without (TLE-no) hippocampal sclerosis, which are known to be associated with different types of brain atrophy, by using these 3 methods; and 2) determining the aspect of the disease pathology identified by each method.

MATERIALS AND METHODS

T1-weighted MR images were acquired for 15 TLE-mts patients, 14 TLE-no patients, and 33 controls on a high-field 4T scanner. Optimized VBM was carried out by using SPM software, DBM was performed by using a fluid-flow registration algorithm, and cortical thickness was analyzed by using FS-CT.

RESULTS

In TLE-mts, the most pronounced volume losses were identified in the ipsilateral hippocampus and mesial temporal region, bilateral thalamus, and cerebellum, by using SPM-VBM and DBM. In TLE-no, the most widespread changes were cortical and identified by using FS-CT, affecting the bilateral temporal lobes, insula, and frontal and occipital lobes. DBM revealed 2 clusters of reduced volume complementing FS-CT analysis. SPM-VBM did not show any significant volume losses in TLE-no.

CONCLUSIONS

These results demonstrate that the 3 methods detect different aspects of brain atrophy and that the choice of the method should be guided by the suspected pathology of the disease.

Brain MRI segmentation with multiphase minimal partitioning: a comparative study

This paper presents the implementation and quantitative evaluation of a multiphase three-dimensional deformable model in a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to minimize the sensitivity of the method to initial conditions while avoiding the need for a priori information. This random initialization ensures robustness of the method with respect to the initialization and the minimization set up. Postprocessing corrections with morphological operators were applied to refine the details of the global segmentation method. A clinical study was performed on a database of 10 adult brain MRI volumes to compare the level set segmentation to three other methods: “idealized” intensity thresholding, fuzzy connectedness, and an expectation maximization classification using hidden Markov random fields. Quantitative evaluation of segmentation accuracy was performed with comparison to manual segmentation computing true positive and false positive volume fractions. A statistical comparison of the segmentation methods was performed through a Wilcoxon analysis of these error rates and results showed very high quality and stability of the multiphase three-dimensional level set method.