Automated model-based tissue classification of MR images of the brain

Characterizing growth patterns in longitudinal MRI using image contrast

Understanding the growth patterns of the early brain is crucial to the study of neuro-development. In the early stages of brain growth, a rapid sequence of biophysical and chemical processes take place. A crucial component of these processes, known as myelination, consists of the formation of a myelin sheath around a nerve fiber, enabling the effective transmission of neural impulses. As the brain undergoes myelination, there is a subsequent change in the contrast between gray matter and white matter as observed in MR scans. In this work, gray-white matter contrast is proposed as an effective measure of appearance which is relatively invariant to location, scanner type, and scanning conditions. To validate this, contrast is computed over various cortical regions for an adult human phantom. MR (Magnetic Resonance) images of the phantom were repeatedly generated using different scanners, and at different locations. Contrast displays less variability over changing conditions of scan compared to intensity-based measures, demonstrating that it is less dependent than intensity on external factors. Additionally, contrast is used to analyze longitudinal MR scans of the early brain, belonging to healthy controls and Down's Syndrome (DS) patients. Kernel regression is used to model subject-specific trajectories of contrast changing with time. Trajectories of contrast changing with time, as well as time-based biomarkers extracted from contrast modeling, show large differences between groups. The preliminary applications of contrast based analysis indicate its future potential to reveal new information not covered by conventional volumetric or deformation-based analysis, particularly for distinguishing between normal and abnormal growth patterns.

Contourlet-based active contour model for PET image segmentation

PURPOSE

PET-guided radiation therapy treatment planning, clinical diagnosis, assessment of tumor growth, and therapy response rely on the accurate delineation of the tumor volume and quantification of tracer uptake. Most PET image segmentation techniques proposed thus far are suboptimal in the presence of heterogeneity of tracer uptake within the lesion. This work presents an active contour model approach based on the method of Chan and Vese ["Active contours without edges," IEEE Trans. Image Process. 10, 266-277 (2001)] designed to take into account the high level of statistical uncertainty (noise) and to handle the heterogeneity of tumor uptake typically present in PET images.

METHODS

In the proposed method, the fitting terms in the Chan-Vese formulation are modified by introducing new input images, including the smoothed version of the original image using anisotropic diffusion filtering (ADF) and the contourlet transform of the image. The advantage of utilizing ADF for image smoothing is that it avoids blurring the object's edges and preserves the average activity within a region, which is important for accurate PET quantification. Moreover, incorporating the contourlet transform of the image into the fitting terms makes the energy functional more effective in directing the evolving curve toward the object boundaries due to the enhancement of the tumor-to-background ratio (TBR). The proper choice of the energy functional parameters has been formulated by making a clear consensus based on tumor heterogeneity and TBR levels. This cautious parameter selection leads to proper handling of heterogeneous lesions. The algorithm was evaluated using simulated phantom and clinical studies, where the ground truth and histology, respectively, were available for accurate quantitative analysis of the segmentation results. The proposed technique was also compared to a number of previously reported image segmentation techniques.

RESULTS

The results were quantitatively analyzed using three evaluation metrics, including the spatial overlap index (SOI), the mean relative error (MRE), and the mean classification error (MCE). Although the performance of the proposed method was analogous to other methods for some datasets, overall the proposed algorithm outperforms all other techniques. In the largest clinical group comprising nine datasets, the proposed approach improved the SOI from 0.41±0.14 obtained using the best-performing algorithm to 0.54±0.12 and reduced the MRE from 54.23±103.29 to 0.19±16.63 and the MCE from 112.86±69.07 to 60.58±18.43.

CONCLUSIONS

The proposed segmentation technique is superior to other representative segmentation techniques in terms of highest overlap between the segmented volume and the ground truth∕histology and minimum relative and classification errors. Therefore, the proposed active contour model can result in more accurate tumor volume delineation from PET images.

Tissue-specific sparse deconvolution for low-dose CT perfusion

Sparse perfusion deconvolution has been recently proposed to effectively improve the image quality and diagnostic accuracy of low-dose perfusion CT by extracting the complementary information from the high-dose perfusion maps to restore the low-dose using a joint spatio-temporal model. However the low-contrast tissue classes where infarct core and ischemic penumbra usually occur in cerebral perfusion CT tend to be over-smoothed, leading to loss of essential biomarkers. In this paper, we extend this line of work by introducing tissue-specific sparse deconvolution to preserve the subtle perfusion information in the low-contrast tissue classes by learning tissue-specific dictionaries for each tissue class, and restore the low-dose perfusion maps by joining the tissue segments reconstructed from the corresponding dictionaries. Extensive validation on clinical datasets of patients with cerebrovascular disease demonstrates the superior performance of our proposed method with the advantage of better differentiation between abnormal and normal tissue in these patients.

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.

Groupwise multi-atlas segmentation of the spinal cord's internal structure

The spinal cord is an essential and vulnerable component of the central nervous system. Differentiating and localizing the spinal cord internal structure (i.e., gray matter vs. white matter) is critical for assessment of therapeutic impacts and determining prognosis of relevant conditions. Fortunately, new magnetic resonance imaging (MRI) sequences enable clinical study of the in vivo spinal cord's internal structure. Yet, low contrast-to-noise ratio, artifacts, and imaging distortions have limited the applicability of tissue segmentation techniques pioneered elsewhere in the central nervous system. Additionally, due to the inter-subject variability exhibited on cervical MRI, typical deformable volumetric registrations perform poorly, limiting the applicability of a typical multi-atlas segmentation framework. Thus, to date, no automated algorithms have been presented for the spinal cord's internal structure. Herein, we present a novel slice-based groupwise registration framework for robustly segmenting cervical spinal cord MRI. Specifically, we provide a method for (1) pre-aligning the slice-based atlases into a groupwise-consistent space, (2) constructing a model of spinal cord variability, (3) projecting the target slice into the low-dimensional space using a model-specific registration cost function, and (4) estimating robust segmentation susing geodesically appropriate atlas information. Moreover, the proposed framework provides a natural mechanism for performing atlas selection and initializing the free model parameters in an informed manner. In a cross-validation experiment using 67 MR volumes of the cervical spinal cord, we demonstrate sub-millimetric accuracy, significant quantitative and qualitative improvement over comparable multi-atlas frameworks, and provide insight into the sensitivity of the associated model parameters.

5-ALA complete resections go beyond MR contrast enhancement: shift corrected volumetric analysis of the extent of resection in surgery for glioblastoma

BACKGROUND

The technique of 5-aminolevulinic acid (5-ALA) tumor fluorescence is increasingly used to improve visualization of tumor tissue and thereby to increase the rate of patients with gross total resections. In this study, we measured the resection volumes in patients who underwent 5-ALA-guided surgery for non-eloquent glioblastoma and compared them with the preoperative tumor volume.

METHODS

We selected 13 patients who had received a complete resection according to intraoperative 5-ALA induced fluorescence and CRET according to post-operative T1 contrast-enhanced MRI. The volumes of pre-operative contrast enhancing tissue, post-operative resection cavity and resected tissue were determined through shift-corrected volumetric analysis.

RESULTS

The mean resection cavity (29 cm(3)) was marginally smaller than the pre-operative contrast-enhancing tumor (39 cm(3), p = 0.32). However, the mean overall resection volume (84 cm(3)) was significantly larger than the pre-operative contrast-enhancing tumor (39 cm(3), p = 0.0087). This yields a mean volume of resected 5-ALA positive, but radiological non-enhancing tissue of 45 cm(3). The mean calculated rim of resected tissue surpassed pre-operative tumor diameter by 6 mm (range 0-10 mm).

CONCLUSIONS

Results of the current study imply that (i) the resection cavity underestimates the volume of resected tissue and (ii) 5-ALA complete resections go significantly beyond the volume of pre-operative contrast-enhancing tumor bulk on MRI, indicating that 5-ALA also stains MRI non-enhancing tumor tissue. Use of 5-ALA may thus enable extension of coalescent tumor resection beyond radiologically evident tumor. The impact of this more extended resection method on time to progression and overall survival has not been determined, and potentially puts adjacent and functionally intact tissue at risk.

Subject Specific Sparse Dictionary Learning for Atlas based Brain MRI Segmentation

Quantitative measurements from segmentations of soft tissues from magnetic resonance images (MRI) of human brains provide important biomarkers for normal aging, as well as disease progression. In this paper, we propose a patch-based tissue classification method from MR images using sparse dictionary learning from an atlas. Unlike most atlas-based classification methods, deformable registration from the atlas to the subject is not required. An "atlas" consists of an MR image, its tissue probabilities, and the hard segmentation. The "subject" consists of the MR image and the corresponding affine registered atlas probabilities (or priors). A subject specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches. The same sparse combination is applied to the segmentation patches of the atlas to generate tissue memberships of the subject. The novel combination of prior probabilities in the example patches enables us to distinguish tissues having similar intensities but having different spatial location. We show that our method outperforms two state-of-the-art whole brain tissue segmentation methods. We experimented on 12 subjects having manual tissue delineations, obtaining mean Dice coefficients of 0:91 and 0:87 for cortical gray matter and cerebral white matter, respectively. In addition, experiments on subjects with ventriculomegaly shows significantly better segmentation using our approach than the competing methods.

SELF-SUPERVISED MRI TISSUE SEGMENTATION BY DISCRIMINATIVE CLUSTERING

The study of brain lesions can benefit from a clear identification of transitions between healthy and pathological tissues, through the analysis of brain imaging data. Current signal processing methods, able to address these issues, often rely on strong prior information. In this article, a new method for tissue segmentation is proposed. It is based on a discriminative strategy, in a self-supervised machine learning approach. This method avoids the use of prior information, which makes it very versatile, and able to cope with different tissue types. It also returns tissue probabilities for each voxel, crucial for a good characterization of the evolution of brain lesions. Simulated as well as real benchmark data were used to validate the accuracy of the method and compare it against other segmentation algorithms.

Preterm birth affects the developmental synergy between cortical folding and cortical connectivity observed on multimodal MRI

The survival rates of infants born prematurely have improved as a result of advances in neonatal care, although there remains an increased risk of subsequent disability. Accurate measurement of the shape and appearance of the very preterm brain at term-equivalent age may guide the development of predictive biomarkers of neurological outcome. We demonstrate in 92 preterm infants (born at an average gestational age of 27.0±2.7weeks) scanned at term equivalent age (scanned at 40.4±1.74weeks) that the cortical sulcation ratio varies spatially over the cortical surface at term equivalent age and correlates significantly with gestational age at birth (r=0.49,p<0.0001). In the underlying white matter, fractional anisotropy of local white matter regions correlated significantly with gestational age at birth at term equivalent age (for the genu of the corpus callosum r=0.26,p=0.02 and for the splenium r=0.52,p<0.001) and in addition the fractional anisotropy in these local regions varies according to location. Finally, we demonstrate that connectivity measurements from tractography correlate significantly and specifically with the sulcation ratio of the overlying cortical surface at term equivalent age in a subgroup of 20 infants (r={0.67,0.61,0.86}, p={0.004,0.01,0.00002}) for tract systems emanating from the left and right corticospinal tracts and the corpus callosum respectively). Combined, these results suggest a close relationship between the cortical surface phenotype and underlying white matter structure assessed by diffusion weighted MRI. The spatial surface pattern may allow inference on the connectivity and developmental trajectory of the underlying white matter complementary to diffusion imaging and this result may guide the development of biomarkers of functional outcome.

Contour propagation using feature-based deformable registration for lung cancer

Accurate target delineation of CT image is a critical step in radiotherapy treatment planning. This paper describes a novel strategy for automatic contour propagation, based on deformable registration, for CT images of lung cancer. The proposed strategy starts with a manual-delineated contour in one slice of a 3D CT image. By means of feature-based deformable registration, the initial contour in other slices of the image can be propagated automatically, and then refined by active contour approach. Three algorithms are employed in the strategy: the Speeded-Up Robust Features (SURF), Thin-Plate Spline (TPS), and an adapted active contour (Snake), used to refine and modify the initial contours. Five pulmonary cancer cases with about 400 slices and 1000 contours have been used to verify the proposed strategy. Experiments demonstrate that the proposed strategy can improve the segmentation performance in the pulmonary CT images. Jaccard similarity (JS) mean is about 0.88 and the maximum of Hausdorff distance (HD) is about 90%. In addition, delineation time has been considerably reduced. The proposed feature-based deformable registration method in the automatic contour propagation improves the delineation efficiency significantly.

A method for handling intensity inhomogenieties in fMRI sequences of moving anatomy of the early developing brain

This paper presents a method for intensity inhomogeniety removal in fMRI studies of a moving subject. In such studies, subtle changes in signal as the subject moves in the presence of a bias field can be a significant confound for BOLD signal analysis. The proposed method avoids the need for a specific tissue model or assumptions about tissue homogeneity by making use of the multiple views of the underlying bias field provided by the subject's motion. A parametric bias field model is assumed and a regression model is used to estimate the basis function weights of this model. Quantitative evaluation of the effects of motion and noise in motion estimates are performed using simulated data. Results demonstrate the strength and robustness of the new method compared to the state of the art 4D nonparametric bias estimator (N4ITK). We also qualitatively demonstrate the impact of the method on resting state neuroimage analysis of a moving adult brain with simulated motion and bias fields, as well as on in vivo moving fetal fMRI.

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.

RATS: Rapid Automatic Tissue Segmentation in rodent brain MRI

BACKGROUND

High-field MRI is a popular technique for the study of rodent brains. These datasets, while similar to human brain MRI in many aspects, present unique image processing challenges. We address a very common preprocessing step, skull-stripping, which refers to the segmentation of the brain tissue from the image for further processing. While several methods exist for addressing this problem, they are computationally expensive and often require interactive post-processing by an expert to clean up poorly segmented areas. This further increases total processing time per subject.

NEW METHOD

We propose a novel algorithm, based on grayscale mathematical morphology and LOGISMOS-based graph segmentation, which is rapid, robust and highly accurate.

RESULTS

Comparative results obtained on two challenging in vivo datasets, consisting of 22 T1-weighted rat brain images and 10 T2-weighted mouse brain images illustrate the robustness and excellent performance of the proposed algorithm, in a fraction of the computational time needed by existing algorithms.

COMPARISON WITH EXISTING METHODS

In comparison to current state-of-the-art methods, our approach achieved average Dice similarity coefficient of 0.92 ± 0.02 and average Hausdorff distance of 13.6 ± 5.2 voxels (vs. 0.85 ± 0.20, p<0.05 and 42.6 ± 22.9, p << 0.001) for the rat dataset, and 0.96 ± 0.01 and average Hausdorff distance of 21.6 ± 12.7 voxels (vs. 0.93 ± 0.01, p <<0.001 and 33.7 ± 3.5, p <<0.001) for the mouse dataset. The proposed algorithm took approximately 90s per subject, compared to 10-20 min for the neural-network based method and 30-90 min for the atlas-based method.

CONCLUSIONS

RATS is a robust and computationally efficient method for accurate rodent brain skull-stripping even in challenging data.

Deformable templates guided discriminative models for robust 3D brain MRI segmentation

Automatically segmenting anatomical structures from 3D brain MRI images is an important task in neuroimaging. One major challenge is to design and learn effective image models accounting for the large variability in anatomy and data acquisition protocols. A deformable template is a type of generative model that attempts to explicitly match an input image with a template (atlas), and thus, they are robust against global intensity changes. On the other hand, discriminative models combine local image features to capture complex image patterns. In this paper, we propose a robust brain image segmentation algorithm that fuses together deformable templates and informative features. It takes advantage of the adaptation capability of the generative model and the classification power of the discriminative models. The proposed algorithm achieves both robustness and efficiency, and can be used to segment brain MRI images with large anatomical variations. We perform an extensive experimental study on four datasets of T1-weighted brain MRI data from different sources (1,082 MRI scans in total) and observe consistent improvement over the state-of-the-art systems.

Improved inference in Bayesian segmentation using Monte Carlo sampling: application to hippocampal subfield volumetry

Many segmentation algorithms in medical image analysis use Bayesian modeling to augment local image appearance with prior anatomical knowledge. Such methods often contain a large number of free parameters that are first estimated and then kept fixed during the actual segmentation process. However, a faithful Bayesian analysis would marginalize over such parameters, accounting for their uncertainty by considering all possible values they may take. Here we propose to incorporate this uncertainty into Bayesian segmentation methods in order to improve the inference process. In particular, we approximate the required marginalization over model parameters using computationally efficient Markov chain Monte Carlo techniques. We illustrate the proposed approach using a recently developed Bayesian method for the segmentation of hippocampal subfields in brain MRI scans, showing a significant improvement in an Alzheimer's disease classification task. As an additional benefit, the technique also allows one to compute informative "error bars" on the volume estimates of individual structures.

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.

Automatic functional analysis of left ventricle in cardiac cine MRI

RATIONALE AND OBJECTIVES

A fully automated left ventricle segmentation method for the functional analysis of cine short axis (SAX) magnetic resonance (MR) images was developed, and its performance evaluated with 133 studies of subjects with diverse pathology: ischemic heart failure (n=34), non-ischemic heart failure (n=30), hypertrophy (n=32), and healthy (n=37).

MATERIALS AND METHODS

The proposed automatic method locates the left ventricle (LV), then for each image detects the contours of the endocardium, epicardium, papillary muscles and trabeculations. Manually and automatically determined contours and functional parameters were compared quantitatively.

RESULTS

There was no significant difference between automatically and manually determined end systolic volume (ESV), end diastolic volume (EDV), ejection fraction (EF) and left ventricular mass (LVM) for each of the four groups (paired sample t-test, α=0.05). The automatically determined functional parameters showed high correlations with those derived from manual contours, and the Bland-Altman analysis biases were small (1.51 mL, 1.69 mL, -0.02%, -0.66 g for ESV, EDV, EF and LVM, respectively).

CONCLUSIONS

The proposed technique automatically and rapidly detects endocardial, epicardial, papillary muscles' and trabeculations' contours providing accurate and reproducible quantitative MRI parameters, including LV mass and EF.

Adaptive image segmentation for robust measurement of longitudinal brain tissue change

We present a method that significantly improves magnetic resonance imaging (MRI) based brain tissue segmentation by modeling the topography of boundaries between tissue compartments. Edge operators are used to identify tissue interfaces and thereby more realistically model tissue label dependencies between adjacent voxels on opposite sides of an interface. When applied to a synthetic MRI template corrupted by additive noise, it provided more consistent tissue labeling across noise levels than two commonly used methods (FAST and SPM5). When applied to longitudinal MRI series it provided lesser variability in individual trajectories of tissue change, suggesting superior ability to discriminate real tissue change from noise. These results suggest that this method may be useful for robust longitudinal brain tissue change estimation.

Metabolic abnormalities in lobar and subcortical brain regions of abstinent polysubstance users: magnetic resonance spectroscopic imaging

AIMS

The aim of the study was to explore neurometabolic and associated cognitive characteristics of patients with polysubstance use (PSU) in comparison with patients with predominant alcohol use using proton magnetic resonance spectroscopy.

METHODS

Brain metabolite concentrations were examined in lobar and subcortical brain regions of three age-matched groups: 1-month-abstinent alcohol-dependent PSU, 1-month-abstinent individuals dependent on alcohol alone (ALC) and light drinking controls (CON). Neuropsychological testing assessed cognitive function.

RESULTS

While CON and ALC had similar metabolite levels, persistent metabolic abnormalities (primarily higher myo-inositol) were present in temporal gray matter, cerebellar vermis and lenticular nuclei of PSU. Moreover, lower cortical gray matter concentration of the neuronal marker N-acetylaspartate within PSU correlated with higher cocaine (but not alcohol) use quantities and with a reduced cognitive processing speed.

CONCLUSIONS

These metabolite group differences reflect cellular/astroglial injury and/or dysfunction in alcohol-dependent PSU. Associations of other metabolite concentrations with neurocognitive performance suggest their functional relevance. The metabolic alterations in PSU may represent polydrug abuse biomarkers and/or potential targets for pharmacological and behavioral PSU-specific treatment.

Grey and white matter abnormalities in temporal lobe epilepsy with and without mesial temporal sclerosis

Temporal lobe epilepsy with (TLE-mts) and without (TLE-no) mesial temporal sclerosis display different patterns of cortical neuronal loss, suggesting that the distribution of white matter damage may also differ between the sub-groups. The purpose of this study was to examine patterns of white matter damage in TLE-mts and TLE-no and to determine if identified changes are related to neuronal loss at the presumed seizure focus. The 4 T diffusion tensor imaging (DTI) and T1-weighted data were acquired for 22 TLE-mts, 21 TLE-no and 31 healthy controls. Tract-based spatial statistics (TBSS) was used to compare fractional anisotropy (FA) maps and voxel-based morphometry (VBM) was used to identify grey matter (GM) volume atrophy. Correlation analysis was conducted between the FA maps and neuronal loss at the presumed seizure focus. In TLE-mts, reduced FA was identified in the genu, body and splenium of the corpus callosum, bilateral corona radiata, cingulum, external capsule, ipsilateral internal capsule and uncinate fasciculus. In TLE-no, FA decreases were identified in the genu, the body of the corpus callosum and ipsilateral anterior corona radiata. The FA positively correlated with ipsilateral hippocampal volume. Widespread extra-focal GM atrophy was associated with both sub-groups. Despite widespread and extensive GM atrophy displaying different anatomical patterns in both sub-groups, TLE-mts demonstrated more extensive FA abnormalities than TLE-no. The microstructural organization in the corpus callosum was related to hippocampal volume in both patients and healthy subjects demonstrating the association of these distal regions.

Polysubstance and alcohol dependence: unique abnormalities of magnetic resonance-derived brain metabolite levels

BACKGROUND

Although comorbid substance misuse is common in alcohol dependence, and polysubstance abusers (PSU) represent the largest group of individuals seeking treatment for drug abuse today, we know little about potential brain abnormalities in this population. Brain magnetic resonance spectroscopy studies of mono-substance use disorders (e.g., alcohol or cocaine) reveal abnormal levels of cortical metabolites (reflecting neuronal integrity, cell membrane turnover/synthesis, cellular bioenergetics, gliosis) and altered concentrations of glutamate and γ-aminobutyric acid (GABA). The concurrent misuse of several substances may have unique and different effects on brain biology and function compared to any mono-substance misuse.

METHODS

High field brain magnetic resonance spectroscopy at 4 T and neurocognitive testing were performed at one month of abstinence in 40 alcohol dependent individuals (ALC), 28 alcohol dependent PSU and 16 drug-free controls. Absolute metabolite concentrations were calculated in anterior cingulate (ACC), parieto-occipital (POC) and dorso-lateral prefrontal cortices (DLPFC).

RESULTS

Compared to ALC, PSU demonstrated significant metabolic abnormalities in the DLPFC and strong trends to lower GABA in the ACC. Metabolite levels in ALC and light drinking controls were statistically equivalent. Within PSU, lower DLPFC GABA levels are related to greater cocaine consumption. Several cortical metabolite concentrations were associated with cognitive performance.

CONCLUSIONS

While metabolite concentrations in ALC at one month of abstinence were largely normal, PSU showed persistent and functionally significant metabolic abnormalities, primarily in the DLPFC. Our results point to specific metabolic deficits as biomarkers in polysubstance misuse and as targets for pharmacological and behavioral PSU-specific treatment.

Repetitive and self-injurious behaviors: associations with caudate volume in autism and fragile X syndrome

Background

Following from previous work suggesting that neurobehavioral features distinguish fragile X and idiopathic variants of autism, we investigated the relationships between four forms of repetitive behavior (stereotypy, self-injury, compulsivity, ritual behavior) and caudate nuclei volume in two groups: boys with fragile X syndrome, a subset of whom met criteria for autism, and a comparison group of boys with idiopathic autism.

Methods

Bilateral caudate nuclei volumes were measured in boys aged 3 to 6 years with fragile X syndrome (n = 41), the subset of boys with fragile X syndrome and autism (n = 16), and boys with idiopathic autism (n = 30). Repetitive behaviors were measured using the Repetitive Behavior Scales-Revised.

Results

For boys with idiopathic autism, left caudate volume was modestly associated with self-injury, while both compulsive and ritual behaviors showed significant positive correlations with bilateral caudate nuclei volumes, replicating previous results. For boys with fragile X syndrome, there was no such association between caudate volume and compulsive behaviors. However, we did identify significant positive correlations between self-injury total scores and number of self-injury topographies with bilateral caudate nuclei volumes.

Conclusions

These findings suggest a specific role for the caudate nucleus in the early pathogenesis of self-injurious behavior associated with both idiopathic autism and fragile X syndrome. Results further indicate that the caudate may be differentially associated with compulsive behavior, highlighting the utility of isolating discrete brain-behavior associations within and between subtypes of autism spectrum disorder.

Accurate segmentation of brain images into 34 structures combining a non-stationary adaptive statistical atlas and a multi-atlas with applications to Alzheimer's disease

Accurate segmentation of the 30+ subcortical structures in MR images of whole diseased brains is challenging due to inter-subject variability and complex geometry of brain anatomy. However a clinically viable solution yielding precise segmentation of the structures would enable: 1) accurate, objective measurement of structure volumes many of which are associated with diseases such as Alzheimer's, 2) therapy monitoring and 3) drug development. Our contributions are two-fold. First we construct an extended adaptive statistical atlas method (EASA) to use a non-stationary relaxation factor rather than a global one. This permits finer control over adaptivity allowing 34 structures to be simultaneously segmented rather than just 4 as in [13]. Second we use the output of a weighted majority voting (WMV) label fusion multi-atlas method as the input to EASA in a hybrid WMV-EASA approach. We assess our proposed approaches on 18 healthy subjects in the public IBSR database and on 9 subjects with Alzheimer's disease in the AIBL database. EASA is shown to produce state-of-the-art accuracy on healthy brains in a fraction of the time of comparable methods, while our hybrid WMV-EASA visibly improves segmentation accuracy for structures throughout the diseased brains.

Longitudinal Heschl's gyrus growth during childhood and adolescence in typical development and autism

Heightened auditory sensitivity and atypical auditory processing are common in autism. Functional studies suggest abnormal neural response and hemispheric activation to auditory stimuli, yet the neurodevelopment underlying atypical auditory function in autism is unknown. In this study, we model longitudinal volumetric growth of Heschl's gyrus gray matter and white matter during childhood and adolescence in 40 individuals with autism and 17 typically developing participants. Up to three time points of magnetic resonance imaging data, collected on average every 2.5 years, were examined from individuals 3-12 years of age at the time of their first scan. Consistent with previous cross-sectional studies, no group differences were found in Heschl's gyrus gray matter volume or asymmetry. However, reduced longitudinal gray matter volumetric growth was found in the right Heschl's gyrus in autism. Reduced longitudinal white matter growth in the left hemisphere was found in the right-handed autism participants. Atypical Heschl's gyrus white matter volumetric growth was found bilaterally in the autism individuals with a history of delayed onset of spoken language. Heightened auditory sensitivity, obtained from the Sensory Profile, was associated with reduced volumetric gray matter growth in the right hemisphere. Our longitudinal analyses revealed dynamic gray and white matter changes in Heschl's gyrus throughout childhood and adolescence in both typical development and autism.

Cortical surface reconstruction via unified Reeb analysis of geometric and topological outliers in magnetic resonance images

In this paper we present a novel system for the automated reconstruction of cortical surfaces from T1-weighted magnetic resonance images. At the core of our system is a unified Reeb analysis framework for the detection and removal of geometric and topological outliers on tissue boundaries. Using intrinsic Reeb analysis, our system can pinpoint the location of spurious branches and topological outliers, and correct them with localized filtering using information from both image intensity distributions and geometric regularity. In this system, we have also developed enhanced tissue classification with Hessian features for improved robustness to image inhomogeneity, and adaptive interpolation to achieve sub-voxel accuracy in reconstructed surfaces. By integrating these novel developments, we have a system that can automatically reconstruct cortical surfaces with improved quality and dramatically reduced computational cost as compared with the popular FreeSurfer software. In our experiments, we demonstrate on 40 simulated MR images and the MR images of 200 subjects from two databases: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and International Consortium of Brain Mapping (ICBM), the robustness of our method in large scale studies. In comparisons with FreeSurfer, we show that our system is able to generate surfaces that better represent cortical anatomy and produce thickness features with higher statistical power in population studies.

Parameterization of the distribution of white and grey matter in MRI using the α-stable distribution

This work presents a study of the distribution of the grey matter (GM) and white matter (WM) in brain magnetic resonance imaging (MRI). The distribution of GM and WM is characterized using a mixture of α-stable distributions. A Bayesian α-stable mixture model for histogram data is presented and unknown parameters are sampled using the Metropolis-Hastings algorithm. The proposed methodology is tested in 18 real images from the MRI brain segmentation repository. The GM and WM distributions are accurately estimated. The α-stable distribution mixture model presented in this paper can be used as previous step in more complex MRI segmentation procedures using spatial information. Furthermore, due to the fact that the α-stable distribution is a generalization of the Gaussian distribution, the proposed methodology can be applied instead of the Gaussian mixture model, which is widely used in segmentation of brain MRI in the literature.

Structural brain atlases: design, rationale, and applications in normal and pathological cohorts

Structural magnetic resonance imaging (MRI) provides anatomical information about the brain in healthy as well as in diseased conditions. On the other hand, functional MRI (fMRI) provides information on the brain activity during performance of a specific task. Analysis of fMRI data requires the registration of the data to a reference brain template in order to identify the activated brain regions. Brain templates also find application in other neuroimaging modalities, such as diffusion tensor imaging and multi-voxel spectroscopy. Further, there are certain differences (e.g., brain shape and size) in the brains of populations of different origin and during diseased conditions like in Alzheimer's disease (AD), population and disease-specific brain templates may be considered crucial for accurate registration and subsequent analysis of fMRI as well as other neuroimaging data. This manuscript provides a comprehensive review of the history, construction and application of brain atlases. A chronological outline of the development of brain template design, starting from the Talairach and Tournoux atlas to the Chinese brain template (to date), along with their respective detailed construction protocols provides the backdrop to this manuscript. The manuscript also provides the automated workflow-based protocol for designing a population-specific brain atlas from structural MRI data using LONI Pipeline graphical workflow environment. We conclude by discussing the scope of brain templates as a research tool and their application in various neuroimaging modalities.

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.

Closed-form relaxation for MRF-MAP tissue classification using discrete Laplace equations

While Markov random fields are very popular segmentation models in medical image processing, the associated maximum a posteriori (MAP) estimation problem is usually solved using iterative methods that are prone to local maxima. We show that a variant of the random walker algorithm can be seen as a relaxation method for the MAP problem under the Potts model. The key advantage of this technique is that it boils down to a sparse linear system with a uniquely defined explicit solution. Our experiments further demonstrate that the resulting MAP approximation can be used to improve the classical mean-field algorithm in terms of MAP estimation quality.

System for integrated neuroimaging analysis and processing of structure

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

Structural MRI in frontotemporal dementia: comparisons between hippocampal volumetry, tensor-based morphometry and voxel-based morphometry

BACKGROUND

MRI is an important clinical tool for diagnosing dementia-like diseases such as Frontemporal Dementia (FTD). However there is a need to develop more accurate and standardized MRI analysis methods.

OBJECTIVE

To compare FTD with Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) with three automatic MRI analysis methods - Hippocampal Volumetry (HV), Tensor-based Morphometry (TBM) and Voxel-based Morphometry (VBM), in specific regions of interest in order to determine the highest classification accuracy.

METHODS

Thirty-seven patients with FTD, 46 patients with AD, 26 control subjects, 16 patients with progressive MCI (PMCI) and 48 patients with stable MCI (SMCI) were examined with HV, TBM for shape change, and VBM for gray matter density. We calculated the Correct Classification Rate (CCR), sensitivity (SS) and specificity (SP) between the study groups.

RESULTS

We found unequivocal results differentiating controls from FTD with HV (hippocampus left side) (CCR = 0.83; SS = 0.84; SP = 0.80), with TBM (hippocampus and amygdala (CCR = 0.80/SS = 0.71/SP = 0.94), and with VBM (all the regions studied, especially in lateral ventricle frontal horn, central part and occipital horn) (CCR = 0.87/SS = 0.81/SP = 0.96). VBM achieved the highest accuracy in differentiating AD and FTD (CCR = 0.72/SS = 0.67/SP = 0.76), particularly in lateral ventricle (frontal horn, central part and occipital horn) (CCR = 0.73), whereas TBM in superior frontal gyrus also achieved a high accuracy (CCR = 0.71/SS = 0.68/SP = 0.73). TBM resulted in low accuracy (CCR = 0.62) in the differentiation of AD from FTD using all regions of interest, with similar results for HV (CCR = 0.55).

CONCLUSION

Hippocampal atrophy is present not only in AD but also in FTD. Of the methods used, VBM achieved the highest accuracy in its ability to differentiate between FTD and AD.

Non-local statistical label fusion for multi-atlas segmentation

Multi-atlas segmentation provides a general purpose, fully-automated approach for transferring spatial information from an existing dataset ("atlases") to a previously unseen context ("target") through image registration. The method to resolve voxelwise label conflicts between the registered atlases ("label fusion") has a substantial impact on segmentation quality. Ideally, statistical fusion algorithms (e.g., STAPLE) would result in accurate segmentations as they provide a framework to elegantly integrate models of rater performance. The accuracy of statistical fusion hinges upon accurately modeling the underlying process of how raters err. Despite success on human raters, current approaches inaccurately model multi-atlas behavior as they fail to seamlessly incorporate exogenous intensity information into the estimation process. As a result, locally weighted voting algorithms represent the de facto standard fusion approach in clinical applications. Moreover, regardless of the approach, fusion algorithms are generally dependent upon large atlas sets and highly accurate registration as they implicitly assume that the registered atlases form a collectively unbiased representation of the target. Herein, we propose a novel statistical fusion algorithm, Non-Local STAPLE (NLS). NLS reformulates the STAPLE framework from a non-local means perspective in order to learn what label an atlas would have observed, given perfect correspondence. Through this reformulation, NLS (1) seamlessly integrates intensity into the estimation process, (2) provides a theoretically consistent model of multi-atlas observation error, and (3) largely diminishes the need for large atlas sets and very high-quality registrations. We assess the sensitivity and optimality of the approach and demonstrate significant improvement in two empirical multi-atlas experiments.

Lymph node detection and segmentation in chest CT data using discriminative learning and a spatial prior

Lymph nodes have high clinical relevance and routinely need to be considered in clinical practice. Automatic detection is, however, challenging due to clutter and low contrast. In this paper, a method is presented that fully automatically detects and segments lymph nodes in 3-D computed tomography images of the chest. Lymph nodes can easily be confused with other structures, it is therefore vital to incorporate as much anatomical prior knowledge as possible in order to achieve a good detection performance. Here, a learned prior of the spatial distribution is used to model this knowledge. Different prior types with increasing complexity are proposed and compared to each other. This is combined with a powerful discriminative model that detects lymph nodes from their appearance. It first generates a number of candidates of possible lymph node center positions. Then, a segmentation method is initialized with a detected candidate. The graph cuts method is adapted to the problem of lymph nodes segmentation. We propose a setting that requires only a single positive seed and at the same time solves the small cut problem of graph cuts. Furthermore, we propose a feature set that is extracted from the segmentation. A classifier is trained on this feature set and used to reject false alarms. Cross-validation on 54 CT datasets showed that for a fixed number of four false alarms per volume image, the detection rate is well more than doubled when using the spatial prior. In total, our proposed method detects mediastinal lymph nodes with a true positive rate of 52.0% at the cost of only 3.1 false alarms per volume image and a true positive rate of 60.9% with 6.1 false alarms per volume image, which compares favorably to prior work on mediastinal lymph node detection.

Treatment of multiple system atrophy using intravenous immunoglobulin

Background

Multiple system atrophy (MSA) is a progressive neurodegenerative disorder of unknown etiology, manifesting as combination of parkinsonism, cerebellar syndrome and dysautonomia. Disease-modifying therapies are unavailable. Activation of microglia and production of toxic cytokines suggest a role of neuroinflammation in MSA pathogenesis. This pilot clinical trial evaluated safety and tolerability of intravenous immunoglobulin (IVIG) in MSA.

Methods

This was a single-arm interventional, single-center, open-label pilot study. Interventions included monthly infusions of the IVIG preparation Privigen®, dose 0.4 gram/kg, for 6 months. Primary outcome measures evaluated safety and secondary outcome measures evaluated preliminary efficacy of IVIG. Unified MSA Rating Scale (UMSARS) was measured monthly. Quantitative brain imaging using 3T MRI was performed before and after treatment.

Results

Nine subjects were enrolled, and seven (2 women and 5 men, age range 55–64 years) completed the protocol. There were no serious adverse events. Systolic blood pressure increased during IVIG infusions (p<0.05). Two participants dropped out from the study because of a non-threatening skin rash. The UMSARS-I (activities of daily living) and USMARS-II (motor functions) improved significantly post-treatment. UMSARS-I improved in all subjects (pre-treatment 23.9 ± 6.0 vs. post-treatment 19.0±5.9 (p=0.01). UMSARS-II improved in 5 subjects, was unchanged in 1 and worsened in 1 (pre-treatment 26.1±7.5 vs. post-treatment 23.3±7.3 (p=0.025). The MR imaging results were not different comparing pre- to post-treatment.

Conclusions

Treatment with IVIG appears to be safe, feasible and well tolerated and may improve functionality in MSA. A larger, placebo-controlled study is needed.

Brain-derived neurotrophic factor genotype is associated with brain gray and white matter tissue volumes recovery in abstinent alcohol-dependent individuals

Neuroimaging studies have linked the methionine (Met) allele of the brain-derived neurotrophic factor (BDNF) gene to abnormal regional brain volumes in several psychiatric and neurodegenerative diseases. However, no neuroimaging studies assessed the effects of this allele on brain morphology in alcohol use disorders and its demonstrated change during abstinence from alcohol. Here we assessed the effects of the BDNF Val66Met (rs6265) polymorphism on regional brain tissue volumes and their recovery during short-term abstinence in treatment-seeking alcohol-dependent individuals. 3D T1 weighted magnetic resonance images from 62 individuals were acquired at 1.5 T at one week of abstinence from alcohol; 41 of the participants were rescanned at 5 weeks of abstinence. The images were segmented into gray matter (GM), white matter (WM) and cerebrospinal fluid and parcellated into regional volumes. The BDNF genotype was determined from blood samples using the TaqMan technique. Alcohol-dependent Val (Valine)/Met heterozygotes and Val homozygotes had similar regional brain volumes at either time point. However, Val homozygotes had significant GM volume increases, while Val/Met heterozygotes increased predominantly in WM volumes over the scan interval. Longitudinal increases in GM but not WM volumes were related to improvements in neurocognitive measures during abstinence. The findings suggest that functionally significant brain tissue volume recovery during abstinence from alcohol is influenced by BDNF genotype.

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.

Adaptive prior probability and spatial temporal intensity change estimation for segmentation of the one-year-old human brain

The degree of white matter (WM) myelination is rather inhomogeneous across the brain. White matter appears differently across the cortical lobes in MR images acquired during early postnatal development. Specifically at 1-year of age, the gray/white matter contrast of MR T1 and T2 weighted images in prefrontal and temporal lobes is reduced as compared to the rest of the brain, and thus, tissue segmentation results commonly show lower accuracy in these lobes. In this novel work, we propose the use of spatial intensity growth maps (IGM) for T1 and T2 weighted images to compensate for local appearance inhomogeneity. The IGM captures expected intensity changes from 1 to 2 years of age, as appearance homogeneity is greatly improved by the age of 24 months. The IGM was computed as the coefficient of a voxel-wise linear regression model between corresponding intensities at 1 and 2 years. The proposed IGM method revealed low regression values of 1-10% in GM and CSF regions, as well as in WM regions at maturation stage of myelination at 1 year. However, in the prefrontal and temporal lobes we observed regression values of 20-25%, indicating that the IGM appropriately captures the expected large intensity change in these lobes mainly due to myelination. The IGM is applied to cross-sectional MRI datasets of 1-year-old subjects via registration, correction and tissue segmentation of the IGM-corrected dataset. We validated our approach in a small leave-one-out study of images with known, manual 'ground truth' segmentations.

Automated measurement of the human corpus callosum using MRI

The corpus callosum includes the majority of fibers that connect the two cortical hemispheres. Studies of cross-sectional callosal morphometry and area have revealed developmental, gender, and hemispheric differences in healthy populations and callosal deficits associated with neurodegenerative disease and brain injury. However, accurate quantification of the callosum using magnetic resonance imaging is complicated by intersubject variability in callosal size, shape, and location and often requires manual outlining of the callosum in order to achieve adequate performance. Here we describe an objective, fully automated protocol that utilizes voxel-based images to quantify the area and thickness both of the entire callosum and of different callosal compartments. We verify the method's accuracy, reliability, robustness, and multisite consistency and make comparisons with manual measurements using public brain-image databases. An analysis of age-related changes in the callosum showed increases in length and reductions in thickness and area with age. A comparison of older subjects with and without mild dementia revealed that reductions in anterior callosal area independently predicted poorer cognitive performance after factoring out Mini-Mental Status Examination scores and normalized whole brain volume. Open-source software implementing the algorithm is available at www.nitrc.org/projects/c8c8.

Glutamate, GABA, and other cortical metabolite concentrations during early abstinence from alcohol and their associations with neurocognitive changes

BACKGROUND

Little is known about the effects of alcohol dependence on cortical concentrations of glutamate (Glu) or gamma aminobutyric acid (GABA). We used proton magnetic resonance spectroscopy (MRS) to study cross-sectionally and longitudinally the concentrations of these in alcohol dependent individuals (ALC) during early abstinence from alcohol.

METHODS

Twenty ALC were studied at about one week of abstinence from alcohol (baseline) and 36 ALC at five weeks of abstinence and compared to 16 light/non-drinking controls (LD). Eleven ALC were studied twice during abstinence. Participants underwent clinical interviewing, blood work, neuropsychological testing, structural imaging and single-volume proton MRS at 4Tesla. Absolute concentrations of Glu, GABA and those of other (1)H MRS-detectable metabolites were measured in the anterior cingulate (ACC), parieto-occipital cortex (POC) and dorso-lateral prefrontal cortex (DLPFC). Relationships of metabolite levels to drinking severity and neurocognition were also assessed.

RESULTS

ALC at baseline had lower concentrations of Glu, N-acetylaspartate (NAA), choline- (Cho) and creatine-containing metabolites (Cr) than LD in the ACC, but had normal GABA and myo-inositol (mI) levels. At five weeks of abstinence, metabolite concentrations were not significantly different between groups. Between one and five weeks of abstinence, Glu, NAA and Cho levels in the ACC increased significantly. Higher cortical mI concentrations in ALC related to worse neurocognitive outcome.

CONCLUSION

These MRS data suggest compromised and regionally specific bioenergetics/metabolism in one-week-abstinent ALC that largely normalizes over four weeks of sustained abstinence. The correlation between mI levels and neurocognition affirms the functional relevance of this putative astrocyte marker.

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.

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.

Comparative methods for PET image segmentation in pharyngolaryngeal squamous cell carcinoma

Purpose

Several methods have been proposed for the segmentation of ¹⁸F-FDG uptake in PET. In this study, we assessed the performance of four categories of ¹⁸F-FDG PET image segmentation techniques in pharyngolaryngeal squamous cell carcinoma using clinical studies where the surgical specimen served as the benchmark.

Methods

Nine PET image segmentation techniques were compared including: five thresholding methods; the level set technique (active contour); the stochastic expectation-maximization approach; fuzzy clustering-based segmentation (FCM); and a variant of FCM, the spatial wavelet-based algorithm (FCM-SW) which incorporates spatial information during the segmentation process, thus allowing the handling of uptake in heterogeneous lesions. These algorithms were evaluated using clinical studies in which the segmentation results were compared to the 3-D biological tumour volume (BTV) defined by histology in PET images of seven patients with T3–T4 laryngeal squamous cell carcinoma who underwent a total laryngectomy. The macroscopic tumour specimens were collected “en bloc”, frozen and cut into 1.7- to 2-mm thick slices, then digitized for use as reference.

Results

The clinical results suggested that four of the thresholding methods and expectation-maximization overestimated the average tumour volume, while a contrast-oriented thresholding method, the level set technique and the FCM-SW algorithm underestimated it, with the FCM-SW algorithm providing relatively the highest accuracy in terms of volume determination (−5.9 ± 11.9%) and overlap index. The mean overlap index varied between 0.27 and 0.54 for the different image segmentation techniques. The FCM-SW segmentation technique showed the best compromise in terms of 3-D overlap index and statistical analysis results with values of 0.54 (0.26–0.72) for the overlap index.

Conclusion

The BTVs delineated using the FCM-SW segmentation technique were seemingly the most accurate and approximated closely the 3-D BTVs defined using the surgical specimens. Adaptive thresholding techniques need to be calibrated for each PET scanner and acquisition/processing protocol, and should not be used without optimization.