Automatic segmentation of the brain and intracranial cerebrospinal fluid in T1-weighted volume MRI scans of the head, and its application to serial cerebral and intracranial volumetry

A comparison of intracranial volume estimation methods and their cross-sectional and longitudinal associations with age

Intracranial volume (ICV) is frequently used in volumetric magnetic resonance imaging (MRI) studies, both as a covariate and as a variable of interest. Findings of associations between ICV and age have varied, potentially due to differences in ICV estimation methods. Here, we compared five commonly used ICV estimation methods and their associations with age. T1-weighted cross-sectional MRI data was included for 651 healthy individuals recruited through the NORMENT Centre (mean age = 46.1 years, range = 12.0-85.8 years) and 2410 healthy individuals recruited through the UK Biobank study (UKB, mean age = 63.2 years, range = 47.0-80.3 years), where longitudinal data was also available. ICV was estimated with FreeSurfer (eTIV and sbTIV), SPM12, CAT12, and FSL. We found overall high correlations across ICV estimation method, with the lowest observed correlations between FSL and eTIV (r = .87) and between FSL and CAT12 (r = .89). Widespread proportional bias was found, indicating that the agreement between methods varied as a function of head size. Body weight, age, sex, and mean ICV across methods explained the most variance in the differences between ICV estimation methods, indicating possible confounding for some estimation methods. We found both positive and negative cross-sectional associations with age, depending on dataset and ICV estimation method. Longitudinal ICV reductions were found for all ICV estimation methods, with annual percentage change ranging from -0.293% to -0.416%. This convergence of longitudinal results across ICV estimation methods offers strong evidence for age-related ICV reductions in mid- to late adulthood.

Generalizing deep learning brain segmentation for skull removal and intracranial measurements

Total intracranial volume (TICV) and posterior fossa volume (PFV) are essential covariates for brain volumetric analyses with structural magnetic resonance imaging (MRI). Detailed whole brain segmentation provides a non-invasive way to measure brain regions. Furthermore, increasing neuroimaging data are distributed in a skull-stripped manner for privacy protection. Therefore, generalizing deep learning brain segmentation for skull removal and intracranial measurements is an appealing task. However, data availability is challenging due to a limited set of manually traced atlases with whole brain and TICV/PFV labels. In this paper, we employ U-Net tiles to achieve automatic TICV estimation and whole brain segmentation simultaneously on brains w/and w/o the skull. To overcome the scarcity of manually traced whole brain volumes, a transfer learning method is introduced to estimate additional TICV and PFV labels during whole brain segmentation in T1-weighted MRI. Specifically, U-Net tiles are first pre-trained using large-scale BrainCOLOR atlases without TICV and PFV labels, which are created by multi-atlas segmentation. Then the pre-trained models are refined by training the additional TICV and PFV labels using limited BrainCOLOR atlases. We also extend our method to handle skull-stripped brain MR images. From the results, our method provides promising whole brain segmentation and volume estimation results for both brains w/and w/o skull in terms of mean Dice similarity coefficients and mean surface distance and absolute volume similarity. This method has been made available in open source (https://github.com/MASILab/SLANTbrainSeg_skullstripped).

Intracranial Cerebrospinal Fluid Volume Evaluation in Healthy People and Hydrocephalus Patients using SPACE Sequence

Introduction

Cerebrospinal Fluid (CSF) is produced mainly by the choroid plexus but with a substantial influence by the ependymal lining of the ventricles in the brain. Hydrocephalus occurs as a result of discrepancy in the production as well as circulation of CSF as a result of congenital and acquired conditions. Nevertheless, studies on the differences between CSF dynamics according to age and gender are still insufficient. Thus, this study evaluated the volume of intracranial CSF in healthy people and hydrocephalus patients taking into account the differences between CSF dynamics according to age and gender using Sampling Perfection with Application optimised Contrast using different flip-angle Evolution (SPACE) sequence.

Methods

120 healthy volunteers and 60 patients with hydrocephalus were included in this study. SPACE sequence was used to evaluate intracranial CSF with a 3.0T magnetic resonance machine. The total volume of intracranial CSF and the amount of CSF in the ventricle were obtained using a software, and the volume ratio of CSF in the subarachnoid space, the ventricle and the subarachnoid space were calculated.

Results

The mean volume of intracranial CSF, ventricular CSF, and subarachnoid CSF of male volunteers were (206.9±47.7) cm3, (33.0±10.7) cm3, (173.9±37.9) cm3 respectively. The average volume of intracranial CSF, ventricular CSF, and subarachnoid CSF of female volunteers were (199.7±44.9) cm3, (30.8±9.4) cm3, and (168.9±37.0) cm3, respectively. Thus, no significant statistically (P>0.05) difference between males and females was found. (3) The mean values ​​of intracranial CSF, ventricle CSF and subarachnoid CSF, ventricle and subarachnoid CSF volume ratio in patients with hydrocephalus were significantly greater than health volunteers. Thus, the difference between the two groups was statistically significant (P<0.05).

Conclusion

SPACE sequence can quantitatively determine the content of CSF. The change of CSF volume has nothing to do with gender but with age. It is feasible to use SPACE sequence to evaluate the spatial distribution and volume of intracranial CSF.

Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus

OBJECTIVE

Imaging evaluation of the cerebral ventricles is important for clinical decision-making in pediatric hydrocephalus. Although quantitative measurements of ventricular size, over time, can facilitate objective comparison, automated tools for calculating ventricular volume are not structured for clinical use. The authors aimed to develop a fully automated deep learning (DL) model for pediatric cerebral ventricle segmentation and volume calculation for widespread clinical implementation across multiple hospitals.

METHODS

The study cohort consisted of 200 children with obstructive hydrocephalus from four pediatric hospitals, along with 199 controls. Manual ventricle segmentation and volume calculation values served as "ground truth" data. An encoder-decoder convolutional neural network architecture, in which T2-weighted MR images were used as input, automatically delineated the ventricles and output volumetric measurements. On a held-out test set, segmentation accuracy was assessed using the Dice similarity coefficient (0 to 1) and volume calculation was assessed using linear regression. Model generalizability was evaluated on an external MRI data set from a fifth hospital. The DL model performance was compared against FreeSurfer research segmentation software.

RESULTS

Model segmentation performed with an overall Dice score of 0.901 (0.946 in hydrocephalus, 0.856 in controls). The model generalized to external MR images from a fifth pediatric hospital with a Dice score of 0.926. The model was more accurate than FreeSurfer, with faster operating times (1.48 seconds per scan).

CONCLUSIONS

The authors present a DL model for automatic ventricle segmentation and volume calculation that is more accurate and rapid than currently available methods. With near-immediate volumetric output and reliable performance across institutional scanner types, this model can be adapted to the real-time clinical evaluation of hydrocephalus and improve clinician workflow.

Study the Longitudinal in vivo and Cross-Sectional ex vivo Brain Volume Difference for Disease Progression and Treatment Effect on Mouse Model of Tauopathy Using Automated MRI Structural Parcellation

Brain volume measurements extracted from structural MRI data sets are a widely accepted neuroimaging biomarker to study mouse models of neurodegeneration. Whether to acquire and analyze data in vivo or ex vivo is a crucial decision during the phase of experimental designs, as well as data analysis. In this work, we extracted the brain structures for both longitudinal in vivo and single-time-point ex vivo MRI acquired from the same animals using accurate automatic multi-atlas structural parcellation, and compared the corresponding statistical and classification analysis. We found that most gray matter structures volumes decrease from in vivo to ex vivo, while most white matter structures volume increase. The level of structural volume change also varies between different genetic strains and treatment. In addition, we showed superior statistical and classification power of ex vivo data compared to the in vivo data, even after resampled to the same level of resolution. We further demonstrated that the classification power of the in vivo data can be improved by incorporating longitudinal information, which is not possible for ex vivo data. In conclusion, this paper demonstrates the tissue-specific changes, as well as the difference in statistical and classification power, between the volumetric analysis based on the in vivo and ex vivo structural MRI data. Our results emphasize the importance of longitudinal analysis for in vivo data analysis.

Gyri of the human parietal lobe: Volumes, spatial extents, automatic labelling, and probabilistic atlases

Accurately describing the anatomy of individual brains enables interlaboratory communication of functional and developmental studies and is crucial for possible surgical interventions. The human parietal lobe participates in multimodal sensory integration including language processing and also contains the primary somatosensory area. We describe detailed protocols to subdivide the parietal lobe, analyze morphological and volumetric characteristics, and create probabilistic atlases in MNI152 stereotaxic space. The parietal lobe was manually delineated on 3D T1 MR images of 30 healthy subjects and divided into four regions: supramarginal gyrus (SMG), angular gyrus (AG), superior parietal lobe (supPL) and postcentral gyrus (postCG). There was the expected correlation of male gender with larger brain and intracranial volume. We examined a wide range of anatomical features of the gyri and the sulci separating them. At least a rudimentary primary intermediate sulcus of Jensen (PISJ) separating SMG and AG was identified in nearly all (59/60) hemispheres. Presence of additional gyri in SMG and AG was related to sulcal features and volumetric characteristics. The parietal lobe was slightly (2%) larger on the left, driven by leftward asymmetries of the postCG and SMG. Intersubject variability was highest for SMG and AG, and lowest for postCG. Overall the morphological characteristics tended to be symmetrical, and volumes also tended to covary between hemispheres. This may reflect developmental as well as maturation factors. To assess the accuracy with which the labels can be used to segment newly acquired (unlabelled) T1-weighted brain images, we applied multi-atlas label propagation software (MAPER) in a leave-one-out experiment and compared the resulting automatic labels with the manually prepared ones. The results showed strong agreement (mean Jaccard index 0.69, corresponding to a mean Dice index of 0.82, average mean volume error of 0.6%). Stereotaxic probabilistic atlases of each subregion were obtained. They illustrate the physiological brain torque, with structures in the right hemisphere positioned more anteriorly than in the left, and right/left positional differences of up to 10 mm. They also allow an assessment of sulcal variability, e.g. low variability for parietooccipital fissure and cingulate sulcus. Illustrated protocols, individual label sets, probabilistic atlases, and a maximum-probability atlas which takes into account surrounding structures are available for free download under academic licences.

Simultaneous total intracranial volume and posterior fossa volume estimation using multi-atlas label fusion

Total intracranial volume (TICV) is an essential covariate in brain volumetric analyses. The prevalent brain imaging software packages provide automatic TICV estimates. FreeSurfer and FSL estimate TICV using a scaling factor while SPM12 accumulates probabilities of brain tissues. None of the three provide explicit skull/CSF boundary (SCB) since it is challenging to distinguish these dark structures in a T1-weighted image. However, explicit SCB not only leads to a natural way of obtaining TICV (i.e., counting voxels inside the skull) but also allows sub-definition of TICV, for example, the posterior fossa volume (PFV). In this article, they proposed to use multi-atlas label fusion to obtain TICV and PFV simultaneously. The main contributions are: (1) TICV and PFV are simultaneously obtained with explicit SCB from a single T1-weighted image. (2) TICV and PFV labels are added to the widely used BrainCOLOR atlases. (3) Detailed mathematical derivation of non-local spatial STAPLE (NLSS) label fusion is presented. As the skull is clearly distinguished in CT images, we use a semi-manual procedure to obtain atlases with TICV and PFV labels using 20 subjects who both have a MR and CT scan. The proposed method provides simultaneous TICV and PFV estimation while achieving more accurate TICV estimation compared with FreeSurfer, FSL, SPM12, and the previously proposed STAPLE based approach. The newly developed TICV and PFV labels for the OASIS BrainCOLOR atlases provide acceptable performance, which enables simultaneous TICV and PFV estimation during whole brain segmentation. The NLSS method and the new atlases have been made freely available. Hum Brain Mapp 38:599-616, 2017. © 2016 Wiley Periodicals, Inc.

Relationship of recent stress to amygdala volume in depressed and healthy adults

BACKGROUND

The amygdala is an integral part of the extrahypothalamic stress-response system, and its volume related to childhood trauma has been studied, but less is known of associations with recent stressful life events. Amygdala volume differences also have been studied in depression, with conflicting results. We hypothesized that effects of stress may be a confound for amygdala volumetric differences in the context of depression.

METHODS

Right-handed participants (n=61) experiencing a major depressive episode during major depressive disorder (n=40) or bipolar depression (n=21) and healthy volunteers (n=60) underwent 1.5T magnetic resonance imaging (MRI). The amygdala perimeter was manually traced with an electronic mouse, based on anatomical landmarks on consecutive coronal slices, by raters blind to diagnosis. The effects of stress on amygdala volume were examined in linear regression models with self-reported physical/sexual abuse or highest category score on the St. Paul-Ramsey scale of stressful life events within the past 6 months as predictors, testing separately for age, sex, race, and depression status as covariates.

RESULTS

Diagnostic groups did not differ significantly with respect to mean age (depressed, 37.8±11.8yrs; healthy, 34.9±13.8yrs) or proportion of males (depressed, 39%, healthy, 50%). We found no association between physical and/or sexual abuse history and amygdala volume. Life stress within the last six months, however, was associated with smaller left amygdala volume. The association between stress and amygdala volume did not differ by diagnostic group.

LIMITATIONS

Most depressed patients were off medications for at least 2 weeks; however, this may not have been long enough to reverse effects of medications on amygdala structure.

CONCLUSIONS

That life stress of relatively short duration was associated with amygdala size in the entire sample, while temporally distant life stress was not, suggests that amygdala volume changes may occur rapidly and reversibly, and independent of depression status.

Brain Extraction Using Label Propagation and Group Agreement: Pincram

Accurately delineating the brain on magnetic resonance (MR) images of the head is a prerequisite for many neuroimaging methods. Most existing methods exhibit disadvantages in that they are laborious, yield inconsistent results, and/or require training data to closely match the data to be processed. Here, we present pincram, an automatic, versatile method for accurately labelling the adult brain on T1-weighted 3D MR head images. The method uses an iterative refinement approach to propagate labels from multiple atlases to a given target image using image registration. At each refinement level, a consensus label is generated. At the subsequent level, the search for the brain boundary is constrained to the neighbourhood of the boundary of this consensus label. The method achieves high accuracy (Jaccard coefficient > 0.95 on typical data, corresponding to a Dice similarity coefficient of > 0.97) and performs better than many state-of-the-art methods as evidenced by independent evaluation on the Segmentation Validation Engine. Via a novel self-monitoring feature, the program generates the "success index," a scalar metadatum indicative of the accuracy of the output label. Pincram is available as open source software.

A Unified Framework for Brain Segmentation in MR Images

Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets.

Robust volume assessment of brain tissues for 3-dimensional fourier transformation MRI via a novel multispectral technique

A new TRIO algorithm method integrating three different algorithms is proposed to perform brain MRI segmentation in the native coordinate space, with no need of transformation to a standard coordinate space or the probability maps for segmentation. The method is a simple voxel-based algorithm, derived from multispectral remote sensing techniques, and only requires minimal operator input to depict GM, WM, and CSF tissue clusters to complete classification of a 3D high-resolution multislice-multispectral MRI data. Results showed very high accuracy and reproducibility in classification of GM, WM, and CSF in multislice-multispectral synthetic MRI data. The similarity indexes, expressing overlap between classification results and the ground truth, were 0.951, 0.962, and 0.956 for GM, WM, and CSF classifications in the image data with 3% noise level and 0% non-uniformity intensity. The method particularly allows for classification of CSF with 0.994, 0.961 and 0.996 of accuracy, sensitivity and specificity in images data with 3% noise level and 0% non-uniformity intensity, which had seldom performed well in previous studies. As for clinical MRI data, the quantitative data of brain tissue volumes aligned closely with the brain morphometrics in three different study groups of young adults, elderly volunteers, and dementia patients. The results also showed very low rates of the intra- and extra-operator variability in measurements of the absolute volumes and volume fractions of cerebral GM, WM, and CSF in three different study groups. The mean coefficients of variation of GM, WM, and CSF volume measurements were in the range of 0.03% to 0.30% of intra-operator measurements and 0.06% to 0.45% of inter-operator measurements. In conclusion, the TRIO algorithm exhibits a remarkable ability in robust classification of multislice-multispectral brain MR images, which would be potentially applicable for clinical brain volumetric analysis and explicitly promising in cross-sectional and longitudinal studies of different subject groups.

3D mapping of cerebrospinal fluid local volume changes in patients with hydrocephalus treated by surgery: preliminary study

OBJECTIVE

To develop automated deformation modelling for the assessment of cerebrospinal fluid (CSF) local volume changes in patients with hydrocephalus treated by surgery.

METHODS

Ventricular and subarachnoid CSF volume changes were mapped by calculating the Jacobian determinant of the deformation fields obtained after non-linear registration of pre- and postoperative images. A total of 31 consecutive patients, 15 with communicating hydrocephalus (CH) and 16 with non-communicating hydrocephalus (NCH), were investigated before and after surgery using a 3D SPACE (sampling perfection with application optimised contrast using different flip-angle evolution) sequence. Two readers assessed CSF volume changes using 3D colour-encoded maps. The Evans index and postoperative volume changes of the lateral ventricles and sylvian fissures were quantified and statistically compared.

RESULTS

Before surgery, sylvian fissure and brain ventricle volume differed significantly between CH and NCH (P = 0.001 and P = 0.025, respectively). After surgery, 3D colour-encoded maps allowed for the visual recognition of the CSF volume changes in all patients. The amounts of ventricle volume loss of CH and NCH patients were not significantly different (P = 0.30), whereas readjustment of the sylvian fissure volume was conflicting in CH and NCH patients (P < 0.001). The Evans index correlated with ventricle volume in NCH patients.

CONCLUSION

3D mapping of CSF volume changes is feasible providing a quantitative follow-up of patients with hydrocephalus.

KEY POINTS

• MRI can provide helpful information about cerebrospinal fluid volumes. • 3D CSF mapping allows quantitative follow-up in communicating and non-communicating hydrocephalus. • Following intervention, fissures and cisterns readjust in both forms of hydrocephalus. • These findings support the hypothesis of suprasylvian block in communicating hydrocephalus. • 3D mapping may improve shunt dysfunction detection and guide valve pressure settings.

3D mapping of cerebrospinal fluid local volume changes in patients with hydrocephalus treated by surgery: preliminary study

OBJECTIVE

To develop automated deformation modelling for the assessment of cerebrospinal fluid (CSF) local volume changes in patients with hydrocephalus treated by surgery.

METHODS

Ventricular and subarachnoid CSF volume changes were mapped by calculating the Jacobian determinant of the deformation fields obtained after non-linear registration of pre- and postoperative images. A total of 31 consecutive patients, 15 with communicating hydrocephalus (CH) and 16 with non-communicating hydrocephalus (NCH), were investigated before and after surgery using a 3D SPACE (sampling perfection with application optimised contrast using different flip-angle evolution) sequence. Two readers assessed CSF volume changes using 3D colour-encoded maps. The Evans index and postoperative volume changes of the lateral ventricles and sylvian fissures were quantified and statistically compared.

RESULTS

Before surgery, sylvian fissure and brain ventricle volume differed significantly between CH and NCH (P = 0.001 and P = 0.025, respectively). After surgery, 3D colour-encoded maps allowed for the visual recognition of the CSF volume changes in all patients. The amounts of ventricle volume loss of CH and NCH patients were not significantly different (P = 0.30), whereas readjustment of the sylvian fissure volume was conflicting in CH and NCH patients (P < 0.001). The Evans index correlated with ventricle volume in NCH patients.

CONCLUSION

3D mapping of CSF volume changes is feasible providing a quantitative follow-up of patients with hydrocephalus.

KEY POINTS

• MRI can provide helpful information about cerebrospinal fluid volumes. • 3D CSF mapping allows quantitative follow-up in communicating and non-communicating hydrocephalus. • Following intervention, fissures and cisterns readjust in both forms of hydrocephalus. • These findings support the hypothesis of suprasylvian block in communicating hydrocephalus. • 3D mapping may improve shunt dysfunction detection and guide valve pressure settings.

Automatic segmentation of cerebral white matter hyperintensities using only 3D FLAIR images

Magnetic Resonance (MR) white matter hyperintensities have been shown to predict an increased risk of developing cognitive decline. However, their actual role in the conversion to dementia is still not fully understood. Automatic segmentation methods can help in the screening and monitoring of Mild Cognitive Impairment patients who take part in large population-based studies. Most existing segmentation approaches use multimodal MR images. However, multiple acquisitions represent a limitation in terms of both patient comfort and computational complexity of the algorithms. In this work, we propose an automatic lesion segmentation method that uses only three-dimensional fluid-attenuation inversion recovery (FLAIR) images. We use a modified context-sensitive Gaussian mixture model to determine voxel class probabilities, followed by correction of FLAIR artifacts. We evaluate the method against the manual segmentation performed by an experienced neuroradiologist and compare the results with other unimodal segmentation approaches. Finally, we apply our method to the segmentation of multiple sclerosis lesions by using a publicly available benchmark dataset. Results show a similar performance to other state-of-the-art multimodal methods, as well as to the human rater.

Imaging of the entire cerebrospinal fluid volume with a multistation 3D SPACE MR sequence: feasibility study in patients with hydrocephalus

OBJECTIVES

To evaluate the feasibility of imaging the entire cerebrospinal fluid (CSF) volume using the SPACE MR sequence.

METHODS

The SPACE sequence encompassing the brain and spine was performed at 1.5 T in 12 healthy volunteers and 26 consecutive patients with hydrocephalus. Image contrast was estimated using difference ratios in signal intensity between CSF and its background. Segmentation of CSF was performed using geometrical features and a topological assumption of CSF shapes. Subarachnoid and ventricular CSF space volumes were assessed in volunteers and patients and linear discriminant analysis was performed.

RESULTS

Image contrast was 0.94 between the CSF and the brain and 0.90 between the CSF and the spinal cord. According to the phantom study, the accuracy of CSF volume measurement was 98.5 %. A clear distinction between patients and healthy volunteers was obtained using the linear discriminant analysis. Significant linear regression was found in healthy volunteers between ventricular (Vv) and the whole subarachnoid CSF volume (Vs) with Vv = 0.083 Vs.

CONCLUSIONS

Imaging of the entire CSF volume is feasible in healthy volunteers and patients with hydrocephalus. CSF volume can be obtained on a whole-body scale. This approach may be of use for the diagnosis and follow-up of patients with hydrocephalus.

KEY POINTS

• MRI assessment of CSF volume is feasible in healthy volunteers/hydrocephalus patients. • CSF volume can be obtained on a whole-body scale. • The ratio of subarachnoid and ventricular CSF is constant in healthy volunteers. • CSF linear discriminant analysis can distinguish between patients and healthy volunteers. • Entire CSF volume imaging is useful for diagnosing and following hydrocephalus.

FMRI activation with an "affective speech" paradigm in vegetative and minimally conscious States: applicability and prognostic value

Vegetative state (VS) and minimally conscious state (MCS) are considered different clinical entities but their differential diagnosis remains challenging. Some VS patients can show an MCS-like activation in functional magnetic resonance imaging (fMRI) studies that seems to predict recovery from VS. We studied fMRI activation with an affective speech paradigm in a cohort of non-communicative brain-injured individuals consecutively admitted to a post-acute neurorehabilitation facility in five years. Among 93 eligible subjects, 65 met the clinical criteria for VS and 28 for MCS. Because of exclusion criteria, activation studies were performed in only 30 cases out of 93 and analysed in only 24 (about ¼ of the eligible cases): 19 VS and five MCS patients. The passive acoustic stimulus consisted in a familiar voice narrating a significant episode in the patient's life, administered by nonmagnetic earphones. All the MCS patients showed an activation spread to secondary associative cortices but also 52.7% of the VS patients displayed an "atypical" large-scale activation pattern. Regarding the clinical outcome, 80% of the patients with large-scale network activation (LSNA) had some recovery of consciousness. Our results confirm that the VS patients with LSNA at fMRI study have potential for further recovery of consciousness, whereas no patient without activation or only typical activation improved. fMRI study with an affective speech paradigm, when applicable, seems to have a valuable prognostic value in VS patients, even if there are major limitations in terms of applicability.

Topology-based nonlocal fuzzy segmentation of brain MR image with inhomogeneous and partial volume intensity

PURPOSE

The aim was to automatically segment brain magnetic resonance (MR) image with inhomogeneous and partial volume (PV) intensity for brain and neurophysiology analysis.

METHODS

Rather than assuming the presence of a single bias field over the image data, we first apply a local model to MR image analysis. With the brain topology knowledge, several specific local regions are selected, and typical brain tissues are then extracted for the prior estimation of fuzzy clustering center and member function. A new nonlocal fuzzy labeling scheme is applied to global optimization segmentation based on the block comparison and distance weight, which is robust to noise and inhomogeneous intensity. The nonlocal labeling provides optimized fuzzy member value and local intensity estimation of brain tissues such as cerebrospinal fluid (CSF), white matter (WM), and gray matter (GM). In addition to inhomogeneous intensity, PV may lead to error segmentation. To correct error segmentation because of PV, this article also provides two correction schemes. The first one is to extract CSF in deep sulci, which captures more CSF candidate by intensity comparison and topology shape comparison. The local pure CSF, WM, and GM is then estimated to correct the interfaces of CSF/GM and WM/GM.

RESULTS

The segmentation experiments are performed on both brainweb-simulated images and Internet brain segmentation repository database (IBSR) real images. The experimental results demonstrate the robust and efficient performance of our approach.

CONCLUSIONS

Our approach can be applied to automatic segmentation of the brain MR image.

An accurate skull stripping method based on simplex meshes and histogram analysis for magnetic resonance images

Skull stripping methods are designed to eliminate the non-brain tissue in magnetic resonance (MR) brain images. Removal of non-brain tissues is a fundamental step in enabling the processing of brain MR images. The aim of this study is to develop an automatic accurate skull stripping method based on deformable models and histogram analysis. A rough-segmentation step is used to find the optimal starting point for the deformation and is based on thresholds and morphological operators. Thresholds are computed using comparisons with an atlas, and modeling by Gaussians. The deformable model is based on a simplex mesh and its deformation is controlled by the image local gray levels and the information obtained on the gray level modeling of the rough-segmentation. Our Simplex Mesh and Histogram Analysis Skull Stripping (SMHASS) method was tested on the following international databases commonly used in scientific articles: BrainWeb, Internet Brain Segmentation Repository (IBSR), and Segmentation Validation Engine (SVE). A comparison was performed against three of the best skull stripping methods previously published: Brain Extraction Tool (BET), Brain Surface Extractor (BSE), and Hybrid Watershed Algorithm (HWA). Performance was measured using the Jaccard index (J) and Dice coefficient (κ). Our method showed the best performance and differences were statistically significant (p<0.05): J=0.904 and κ=0.950 on BrainWeb; J=0.905 and κ=0.950 on IBSR; J=0.946 and κ=0.972 on SVE.

The Rotterdam Scan Study: design and update up to 2012

Neuroimaging plays an important role in etiologic research on neurological diseases in the elderly. The Rotterdam Scan Study was initiated as part of the ongoing Rotterdam Study with the aim to unravel causes of neurological disease by performing neuroimaging in a population-based longitudinal setting. In 1995 and 1999 random subsets of the Rotterdam Study underwent neuroimaging, whereas from 2005 onwards MRI has been implemented into the core protocol of the Rotterdam Study. In this paper, we discuss the background and rationale of the Rotterdam Scan Study. We also describe the imaging protocol and post-processing techniques, and highlight the main findings to date. Finally, we make recommendations for future research, which will also be the main focus of investigation in the Rotterdam Scan Study.

Regional volume analysis of the Parkinson disease brain in early disease stage: gray matter, white matter, striatum, and thalamus

BACKGROUND AND PURPOSE

Loss of dopaminergic neurons in the nigrostriatal pathway is well-documented in PD, whereas neuronal changes beyond the nigrostriatal pathway are uncertain. The purpose of our study was to estimate volume changes in the striatum and thalamus, which are areas of the basal ganglia, as well as in GM and WM located beyond the nigrostriatal pathway, in early-stage PD.

MATERIALS AND METHODS

We enrolled 30 participants (15 healthy controls and 15 patients with PDND with H & Y stage I or II). Cognitive function was assessed by using the MMSE. ICV and the volumes of the caudate nucleus, putamen, thalamus, GM, and WM were calculated via 3D volume analysis by using MR imaging.

RESULTS

A comparison of the PD group with the control group revealed an absence of significant differences between them regarding age and MMSE scores. Comparison of the volumes of regional brain structures of patients with PD with those of controls revealed the presence of significant differences in the caudate nucleus, thalamus, and WM (P<.05) between the groups. However, there were no significant differences in the volumes of the putamen and GM or in ICV between patients with PD and controls. The results of ANCOVA by using the covariates of age and ICV showed a significant difference in the caudate nucleus, thalamus, and WM between patients with PD and controls (P<.05).

CONCLUSIONS

We suggest that loss of WM volume may occur in early disease stages and that variation of the volumes of the caudate nucleus and thalamus may be an early phenomenon of disease progression.

Automated brain tissue segmentation based on fractional signal mapping from inversion recovery Look-Locker acquisition

Most current automated segmentation methods are performed on T(1)- or T(2)-weighted MR images, relying on relative image intensity that is dependent on other MR parameters and sensitive to B(1) magnetic field inhomogeneity. Here, we propose an image segmentation method based on quantitative longitudinal magnetization relaxation time (T(1)) of brain tissues. Considering the partial volume effect, fractional volume maps of brain tissues (white matter, gray matter, and cerebrospinal fluid) were obtained by fitting the observed signal in an inversion recovery procedure to a linear combination of three exponential functions, which represents the relaxations of each of the tissue types. A Look-Locker acquisition was employed to accelerate the acquisition process. The feasibility and efficacy of this proposed method were evaluated using simulations and experiments. The potential applications of this method in the study of neurological disease as well as normal brain development and aging are discussed.

A robust method to estimate the intracranial volume across MRI field strengths (1.5T and 3T)

As population-based studies may obtain images from scanners with different field strengths, a method to normalize regional brain volumes according to intracranial volume (ICV) independent of field strength is needed. We found systematic differences in ICV estimation, tested in a cohort of healthy subjects (n = 5) that had been imaged using 1.5T and 3T scanners, and confirmed in two independent cohorts. This was related to systematic differences in the intensity of cerebrospinal fluid (CSF), with higher intensities for CSF located in the ventricles compared with CSF in the cisterns, at 3T versus 1.5T, which could not be removed with three different applied bias correction algorithms. We developed a method based on tissue probability maps in MNI (Montreal Neurological Institute) space and reverse normalization (reverse brain mask, RBM) and validated it against manual ICV measurements. We also compared it with alternative automated ICV estimation methods based on Statistical Parametric Mapping (SPM5) and Brain Extraction Tool (FSL). The proposed RBM method was equivalent to manual ICV normalization with a high intraclass correlation coefficient (ICC = 0.99) and reliable across different field strengths. RBM achieved the best combination of precision and reliability in a group of healthy subjects, a group of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) and can be used as a common normalization framework.

White matter lesion extension to automatic brain tissue segmentation on MRI

A fully automated brain tissue segmentation method is optimized and extended with white matter lesion segmentation. Cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM) are segmented by an atlas-based k-nearest neighbor classifier on multi-modal magnetic resonance imaging data. This classifier is trained by registering brain atlases to the subject. The resulting GM segmentation is used to automatically find a white matter lesion (WML) threshold in a fluid-attenuated inversion recovery scan. False positive lesions are removed by ensuring that the lesions are within the white matter. The method was visually validated on a set of 209 subjects. No segmentation errors were found in 98% of the brain tissue segmentations and 97% of the WML segmentations. A quantitative evaluation using manual segmentations was performed on a subset of 6 subjects for CSF, GM and WM segmentation and an additional 14 for the WML segmentations. The results indicated that the automatic segmentation accuracy is close to the interobserver variability of manual segmentations.

Evaluation of automated brain MR image segmentation and volumetry methods

We compare three widely used brain volumetry methods available in the software packages FSL, SPM5, and FreeSurfer and evaluate their performance using simulated and real MR brain data sets. We analyze the accuracy of gray and white matter volume measurements and their robustness against changes of image quality using the BrainWeb MRI database. These images are based on "gold-standard" reference brain templates. This allows us to assess between- (same data set, different method) and also within-segmenter (same method, variation of image quality) comparability, for both of which we find pronounced variations in segmentation results for gray and white matter volumes. The calculated volumes deviate up to >10% from the reference values for gray and white matter depending on method and image quality. Sensitivity is best for SPM5, volumetric accuracy for gray and white matter was similar in SPM5 and FSL and better than in FreeSurfer. FSL showed the highest stability for white (<5%), FreeSurfer (6.2%) for gray matter for constant image quality BrainWeb data. Between-segmenter comparisons show discrepancies of up to >20% for the simulated data and 24% on average for the real data sets, whereas within-method performance analysis uncovered volume differences of up to >15%. Since the discrepancies between results reach the same order of magnitude as volume changes observed in disease, these effects limit the usability of the segmentation methods for following volume changes in individual patients over time and should be taken into account during the planning and analysis of brain volume studies.

MR image segmentation using a power transformation approach

This study proposes a segmentation method for brain MR images using a distribution transformation approach. The method extends traditional Gaussian mixtures expectation-maximization segmentation to a power transformed version of mixed intensity distributions, which includes Gaussian mixtures as a special case. As MR intensities tend to exhibit non-Gaussianity due to partial volume effects, the proposed method is designed to fit non-Gaussian tissue intensity distributions. One advantage of the method is that it is intuitively appealing and computationally simple. To avoid performance degradation caused by intensity inhomogeneity, different methods for correcting bias fields were applied prior to image segmentation, and their correction effects on the segmentation results were examined in the empirical study. The partitions of brain tissues (i.e., gray and white matter) resulting from the method were validated and evaluated against manual segmentation results based on 38 real T1-weighted image volumes from the internet brain segmentation repository, and 18 simulated image volumes from BrainWeb. The Jaccard and Dice similarity indexes were computed to evaluate the performance of the proposed approach relative to the expert segmentations. Empirical results suggested that the proposed segmentation method yielded higher similarity measures for both gray matter and white matter as compared with those based on the traditional segmentation using the Gaussian mixtures approach.

Effect of BDNF val66met polymorphism on age-related amygdala volume changes in healthy subjects

Brain-derived neurotrophic factor (BDNF) has been implicated in the mechanism of age-related regional brain volumetric changes. Healthy volunteers with the valine to methionine polymorphism at codon 66 of the BDNF gene (val66met) exhibit decreased volume of a number of brain structures, including hippocampus, temporal and occipital lobar gray matter volumes, and a negative correlation between age and the volume of bilateral dorsolateral prefrontal cortices. We sought to characterize the relationship between age, BDNF and amygdala volumes among healthy volunteers. We measured amygdala volumes in 55 healthy, right-handed volunteers who underwent structural magnetic resonance imaging (MRI) and were also characterized demographically and genotyped with respect to BDNF. Using an ANCOVA model, we found that amygdala volumes were inversely correlated with age in BDNF val66met carriers but not in non-carriers. This is the first report of age-related BDNF val66met polymorphism effects on amygdala volume.

VASO-based calculations of CBV change: accounting for the dynamic CSF volume

The goal of the vascular space occupancy (VASO) imaging technique is to use selective nulling of the blood signal to infer relative changes in cerebral blood volume (CBV). In accordance with recent work, we show that changes in the local CSF fraction (x(c)) with activation can significantly impact the VASO signal, thereby limiting our ability to infer DeltaCBV from DeltaVASO alone. Here we calculate CBV change using a VASO-based method which ACcounts for the Dynamic Cerebrospinal (ACDC) fluid fraction. By combining data from two separate VASO acquisitions that eliminate either the blood signal (VASO(b)) or the CSF signal (VASO(c)), a nonlinear least-squares optimization may then be used to simultaneously solve for the relative changes in CBV and CSF with activation. The method is applied across the whole brain during a breath-holding task, offering insight into the relationship between changes in CBV and x(c) associated with global vasodilatation. Calculations of mean changes in CBV in different volumes of interest obtained from the proposed method compare much better with previous (gold-standard) PET data than traditional VASO methods that do not account for a nonzero Deltax(c) with activation. This confirms the necessity of incorporating the dynamic CSF volume into VASO-based calculations of DeltaCBV.

Probabilistic brain tissue segmentation in neonatal magnetic resonance imaging

A fully automated method has been developed for segmentation of four different structures in the neonatal brain: white matter (WM), central gray matter (CEGM), cortical gray matter (COGM), and cerebrospinal fluid (CSF). The segmentation algorithm is based on information from T2-weighted (T2-w) and inversion recovery (IR) scans. The method uses a K nearest neighbor (KNN) classification technique with features derived from spatial information and voxel intensities. Probabilistic segmentations of each tissue type were generated. By applying thresholds on these probability maps, binary segmentations were obtained. These final segmentations were evaluated by comparison with a gold standard. The sensitivity, specificity, and Dice similarity index (SI) were calculated for quantitative validation of the results. High sensitivity and specificity with respect to the gold standard were reached: sensitivity >0.82 and specificity >0.9 for all tissue types. Tissue volumes were calculated from the binary and probabilistic segmentations. The probabilistic segmentation volumes of all tissue types accurately estimated the gold standard volumes. The KNN approach offers valuable ways for neonatal brain segmentation. The probabilistic outcomes provide a useful tool for accurate volume measurements. The described method is based on routine diagnostic magnetic resonance imaging (MRI) and is suitable for large population studies.

Automatic volumetric measurement of segmented brain structures on magnetic resonance imaging

PURPOSE

The purpose of this study was to produce a fully automatic volumetric segmented brain image system (AVSIS) and to evaluate its utility for measuring hippocampal volumes and total intracranial volume (TIV).

MATERIALS AND METHODS

We developed a combination technique comprising an anatomical standardization technique to measure TIV, whole brain volume (WBV), and hippocampal volume obtained by magnetic resonance (MR) imaging. Altogether, 15 healthy volunteers and 15 patients with Alzheimer's disease (AD) underwent three-dimensional spoiled gradient echo (3D-SPGR) imaging. Three measurements were performed by manual volumetry as the gold standard, a previous semiautomatic method, and our new method, AVSIS.

RESULTS

WBV and hippocampal volume in the AD group were significantly smaller than those in the healthy volunteer group measured by the semiautomatic method, manual method, and AVSIS. Each volume measured by AVSIS or semiautomatic method correlated with that measured by the manual method. The correlation coefficients between TIVs, WBVs, or hippocampal volumes measured by AVSIS and the manual method were 0.910, 0.902, and 0.918, respectively; the correlation coefficients between TIVs, or WBVs, measured by the previous semiautomatic method and the manual method were 0.875, and 0.886, respectively.

CONCLUSION

We developed a system for a fully automatic measurement of segmented brain structures and obtained good results.

Automatic detection and quantification of hippocampal atrophy on MRI in temporal lobe epilepsy: a proof-of-principle study

In temporal lobe epilepsy (TLE), hippocampal atrophy (HA) is a marker of poor prognosis regarding seizure remission, but predicts success of anterior temporal lobe resection. Manual quantification of HA on MRI is time-consuming and limited by investigator availability. Normal ranges of hippocampal volumes, both in absolute terms and relative to intracranial volume, and of hippocampal asymmetry were defined using an automatic label propagation and decision fusion technique based on thirty manually derived atlases of healthy controls. Manual test-retest reliability and overlaps of automatically and manually determined hippocampal volumes were quantified with similarity indices (SIs). Correct clinical identification of ipsilateral HA, and contralaterally normal hippocampal volumes, was determined in nine patients with histologically confirmed hippocampal sclerosis in terms of volumes and asymmetry indices (AIs) for standard statistical thresholds and with receiver operating characteristic (ROC) analysis. Manual test-retest reliability was very high, with SIs between 0.87 and 0.90. Manual and automatic hippocampus labels overlapped with a SI of 0.83 on the unaffected but with 0.76 on the atrophic side. Accuracy was higher for less atrophic hippocampi. The automatic method correctly identified 6/9 HAs in terms of absolute volume, 7/9 in terms of relative volume at a standard 2 SD threshold, and 9/9 for AIs. ROC-determined thresholds allowed clinically desirable correct identification of all HAs (100% sensitivity) with 85-100% specificity for volumes, and 100% specificity for AIs. The method has the potential to automatically detect unilateral HA, but further work is needed to determine its performance in detecting clinically important bilateral disease.

Virtual definition of neuronal tissue by cluster analysis of multi-parametric imaging (virtual-dot-com imaging)

Individual mapping of cerebral, morphological, functionally related structures using MRI was carried out using a new multi-contrast acquisition and analysis framework, called virtual-dot-com imaging. So far, conventional anatomical MRI has been able to provide gross segmentation of gray/white matter boundaries and a few sub-cortical structures. By combining a handful of imaging contrasts mechanisms (T1, T2, magnetization transfer, T2* and proton density), we were able to further segment sub-cortical tissue to its sub-nuclei arrangement, a segmentation that is difficult based on conventional, single-contrast MRI. Using an automatic four-step image and signal processing algorithm, we segmented the thalamus to at least 7 sub-nuclei with high similarity across subjects and high statistical significance within subjects (p<0.0001). The identified sub-nuclei resembled the known anatomical arrangement of the thalamus given in various atlases. Each cluster was characterized by a unique MRI contrast fingerprint. With this procedure, the weighted proportions of the different cellular compartments could be estimated, a property available to date only by histological analysis. Each sub-nucleus could be characterized in terms of normalized MRI contrast and compared to other sub-nuclei. The different weights of the contrasts (T1/T2/T2*/PD/MT, etc.) for each sub-nuclei cluster might indicate the intra-cluster morphological arrangement of the tissue that it represents. The implications of this methodology are far-ranging, from non-invasive, in vivo, individual mapping of histologically distinct brain areas to automatic identification of pathological processes.

Brain serotonin transporter binding in depressed patients with bipolar disorder using positron emission tomography

CONTEXT

Depression in bipolar disorder is clinically indistinguishable from that observed in major depressive disorder. As in major depression, selective serotonin reuptake inhibitors targeting brain serotonin transporters are first-line treatments for bipolar depression. Associations of serotonin transporter promoter polymorphisms and bipolarity have been reported; however, research on alterations in serotonergic neurotransmission in bipolar depression remains scant.

OBJECTIVES

To assess in vivo brain serotonin transporter binding potential (BP(1), proportional to serotonin transporter number) in patients with bipolar depression and controls and to examine the relationship between serotonin transporter binding and genotype.

DESIGN

Case-control study.

SETTING

University hospital.

PARTICIPANTS

A sample of 18 medication-free patients with bipolar depression and 41 controls.

MAIN OUTCOME MEASURES

In vivo brain serotonin transporter binding was measured using positron emission tomography and radiolabeled trans-1,2,3,5,6,10-beta-hexahydro-6-[4-(methylthio)phenyl]pyrrolo-[2,1-a]-isoquinoline ([(11)C](+)-McNeil 5652). Participants were genotyped assessing biallelic and triallelic 5-HTTLPR polymorphisms.

RESULTS

Patients with bipolar disorder had 16% to 26% lower serotonin transporter BP(1) in the midbrain, amygdala, hippocampus, thalamus, putamen, and anterior cingulate cortex. Triallelic 5-HTTLPR genotypes were unrelated to serotonin transporter BP(1).

CONCLUSIONS

Lower serotonin transporter BP(1) in bipolar depression overlaps with that observed in major depression and suggests that serotonergic dysfunction is common to depressive conditions.

Statistical neuroanatomy of the human inferior frontal gyrus and probabilistic atlas in a standard stereotaxic space

We manually defined the inferior frontal gyrus (IFG) on high-resolution MRIs in native space in 30 healthy subjects (15 female, median age 31 years; 15 male, median age 30 years), resulting in 30 individual atlases. Using standard software (SPM99), these were spatially transformed to a widely used stereotaxic space (MNI/ICBM 152) to create probabilistic maps. In native space, the total IFG volume was on average 5%, and the gray matter (GM) portion 12% larger in women (not significant). Expressed as a percentage of ipsilateral frontal lobe volume (i.e., correcting for brain size), the IFG was an average of 20%, and the GM portion of the IFG 27%, larger in women (P < 0.005). Correcting for total lobar volume yielded the same result. No asymmetry was found in IFG volumes. There were significant positional differences between the right and left IFGs, with the right IFG being further lateral in both native and stereotaxic space. Variability was similar on the left and right, but more pronounced anteriorly and superiorly. We show differences in IFG volume, composition, and position between sexes and between hemispheres. Applications include probabilistic determination of location in group studies, automatic labeling of new scans, and detection of anatomical abnormalities in patients.

In vivo quantification of serotonin transporters using [(11)C]DASB and positron emission tomography in humans: modeling considerations

Positron emission tomography (PET) studies of the serotonin transporter (5-HTT) in the human brain are increasingly using the radioligand [(11)C]N, N-dimethyl-2-(2-amino-4-cyanophenylthio) benzylamine. A variety of models have been applied to such data in several published articles; however to date, these models have not been validated with test-retest data. We recruited 11 healthy subjects and conducted two identical scans on each subject on the same day. We considered four different models (one- and two-tissue compartment kinetic models, likelihood estimation in graphical analysis (LEGA; a bias-free alternative to the graphical method), and basis pursuit) along with fast noniterative approximations to the kinetic models. We considered four different outcome measures (total volume of distribution (V(T)), binding potential with (BP) and without (BP(1)), free-fraction adjustment, and specific-to-nonspecific equilibrium partition coefficient (BP(2))). To assess the performance of each model, we compared results using six different metrics (percent difference (PD) and within-subject mean sum of squares for reproducibility, interclass coefficient for reliability, variance across subjects, identifiability based on bootstrap resampling of residuals for each method, and time stability analysis to determine minimal required scanning time). We considered analysis of both at the voxel level and at the region of interest (ROI) level and compared results from these two approaches to assess agreement. We determined that 100 mins of scanning time is adequate and that for ROI-level analysis, LEGA gives best results. Average PD is 5.51 for V(T), 20.7 for BP, 17.2 for BP(1), and 16.5 for BP(2) across all regions. For voxel-level analysis we determined that the one-tissue compartment noniterative model is best.

Medial temporal lobe volume of nondemented elderly individuals with poor cognitive functions

Poor cognitive performance of elderly individuals with low educational attainment is often difficult to interpret in dementia evaluation. Lack of education, as well as dementia, is often associated with poor cognitive test performance. To elucidate the underlying structural change of low cognitive performance in elderly individuals with low educational attainment, this study examined the relationship between low cognitive performance (LCP) and brain volumes, especially regions vulnerable to Alzheimer's disease, in nondemented elderly Koreans. Individuals with LCP (n=14) were matched on age and education with individuals with normal cognitive performance (n=14). The two groups were compared on the MR-based volumetric measures in the hippocampus, the entorhinal cortex, the amygdala, the temporal lobe, the frontal lobe, the cerebrum, and the intracranial cavity. Intracranial volume (p<.05) and absolute hippocampus (p<.05) and frontal lobe volumes (p<.05) were significantly reduced in individuals with LCP. Normalized volumes of the hippocampus and the frontal lobe did not differ in the two cognitive performance groups. ICV was associated with the K-DRS scores. General cognitive functioning of the LCP individuals, measured with the Korean version of the DRS, did not deteriorate in the 1- or 2-year follow-up cognitive tests. LCP in a nondemented elderly population with limited education appears to be associated with stable lower intelligence rather than increased risk for dementia of the Alzheimer's type.

Automatic anatomical brain MRI segmentation combining label propagation and decision fusion

Regions in three-dimensional magnetic resonance (MR) brain images can be classified using protocols for manually segmenting and labeling structures. For large cohorts, time and expertise requirements make this approach impractical. To achieve automation, an individual segmentation can be propagated to another individual using an anatomical correspondence estimate relating the atlas image to the target image. The accuracy of the resulting target labeling has been limited but can potentially be improved by combining multiple segmentations using decision fusion. We studied segmentation propagation and decision fusion on 30 normal brain MR images, which had been manually segmented into 67 structures. Correspondence estimates were established by nonrigid registration using free-form deformations. Both direct label propagation and an indirect approach were tested. Individual propagations showed an average similarity index (SI) of 0.754+/-0.016 against manual segmentations. Decision fusion using 29 input segmentations increased SI to 0.836+/-0.009. For indirect propagation of a single source via 27 intermediate images, SI was 0.779+/-0.013. We also studied the effect of the decision fusion procedure using a numerical simulation with synthetic input data. The results helped to formulate a model that predicts the quality improvement of fused brain segmentations based on the number of individual propagated segmentations combined. We demonstrate a practicable procedure that exceeds the accuracy of previous automatic methods and can compete with manual delineations.

Comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET

Magnetic resonance imaging (MRI)-guided partial volume effect correction (PVC) in brain positron emission tomography (PET) is now a well-established approach to compensate the large bias in the estimate of regional radioactivity concentration, especially for small structures. The accuracy of the algorithms developed so far is, however, largely dependent on the performance of segmentation methods partitioning MRI brain data into its main classes, namely gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). A comparative evaluation of three brain MRI segmentation algorithms using simulated and clinical brain MR data was performed, and subsequently their impact on PVC in 18F-FDG and 18F-DOPA brain PET imaging was assessed. Two algorithms, the first is bundled in the Statistical Parametric Mapping (SPM2) package while the other is the Expectation Maximization Segmentation (EMS) algorithm, incorporate a priori probability images derived from MR images of a large number of subjects. The third, here referred to as the HBSA algorithm, is a histogram-based segmentation algorithm incorporating an Expectation Maximization approach to model a four-Gaussian mixture for both global and local histograms. Simulated under different combinations of noise and intensity non-uniformity, MR brain phantoms with known true volumes for the different brain classes were generated. The algorithms' performance was checked by calculating the kappa index assessing similarities with the "ground truth" as well as multiclass type I and type II errors including misclassification rates. The impact of image segmentation algorithms on PVC was then quantified using clinical data. The segmented tissues of patients' brain MRI were given as input to the region of interest (RoI)-based geometric transfer matrix (GTM) PVC algorithm, and quantitative comparisons were made. The results of digital MRI phantom studies suggest that the use of HBSA produces the best performance for WM classification. For GM classification, it is suggested to use the EMS. Segmentation performed on clinical MRI data show quite substantial differences, especially when lesions are present. For the particular case of PVC, SPM2 and EMS algorithms show very similar results and may be used interchangeably. The use of HBSA is not recommended for PVC. The partial volume corrected activities in some regions of the brain show quite large relative differences when performing paired analysis on 2 algorithms, implying a careful choice of the segmentation algorithm for GTM-based PVC.

Statistical analysis of morphological differences between brains

Recent study in neuroscience has observed evidence that the anatomic structures in human brains might have certain connection with the functioning. This triggers the interest in morphological study of cortical surfaces and in comparison of different ethnic groups. This article compares the MRI brain datasets of 10 Chinese and 10 Caucasians. A statistical analysis was applied to the white matter volumes in these datasets and evaluate the dissimilarities between the two groups using various intuitive measures. This analysis has revealed systematic morphological differences between the two ethnic groups.

Acute occupancy of brain serotonin transporter by sertraline as measured by [11C]DASB and positron emission tomography

BACKGROUND

In vivo determination of serotonin transporter (5-HTT) occupancy by selective serotonin reuptake inhibitors (SSRI) using positron emission tomography (PET) can aid in determination of dosing. Previous studies used chronic SSRI administration that may down-regulate 5-HTT and used the cerebellum as reference region despite measurable 5-HTT. We examine the reference region and occupancy after acute sertraline dosing.

METHODS

We conducted autoradiography of human postmortem cerebellum to determine an optimal reference region. We quantified 5-HTT binding using [(11)C]DASB and arterial input functions in 17 healthy volunteers. Baseline PET scans were followed by a scan 4-6 days after 25 mg to 100mg of daily sertraline. Several modeling methods and outcome measures were assessed.

RESULTS

Cerebellar gray matter is the optimal reference region. Occupation of 5-HTT sites saturates at low plasma sertraline levels (K(D) = 1.9 ng/ml) with maximal occupancies of 106.8 +/- 8.3% across all brain regions. There is a weak correlation between oral sertraline and plasma sertraline. Occupancy measures vary based on the reference region and outcome measure used.

CONCLUSIONS

Occupancy studies and postmortem autoradiography can help define the optimal reference region. Reference tissue modeling using the optimal reference region returns the same occupancy measures as those determined using an arterial input function.

MRI-based volumetry of head compartments: Normative values of healthy adults

The size of head compartments (head and brain volume, intracranial volume, gray and white matter volume, cerebrospinal fluid volume) and their ratios were determined on the basis of magnetic resonance images of the head acquired in a reference population of 502 healthy subjects. Age-matched subgroups were selected to reveal gender-related differences and changes with age. Normative data are provided in the form of simple equations that allow transforming measured compartment volumes into z scores, offering the possibility to relate individual data to a larger population.

Altered serotonin 1A binding in major depression: a [carbonyl-C-11]WAY100635 positron emission tomography study

BACKGROUND

Serotonin 1A receptors (5-HT(1A)) are implicated in the pathophysiology of major depressive disorder (MDD) and in the action of selective serotonin reuptake inhibitors (SSRI). SSRI desensitize 5-HT(1A) and down-regulate 5-HT transporters (5-HTT) with the latter persisting for weeks after discontinuation of SSRI. MDD subjects are more likely to be homozygous for the functional 5-HT(1A) G(-1019) allele of the promoter polymorphism and are postulated to have higher 5-HT(1A) than healthy volunteers (controls). We measure 5-HT(1A) in MDD, assess the effects of antidepressant exposure (AE), and examine the role of the C(-1019)G polymorphism.

METHODS

Genotyped and determined 5-HT(1A) binding potential (BP) by positron emission tomography (PET) using [carbonyl-C-11]-WAY-100635 in 28 medication-free MDD subjects during a current major depressive episode and 43 controls.

RESULTS

No difference in BP between controls and MDD subjects (p = .235). There was a difference in BP comparing the controls, antidepressant naive (AN) MDD subjects, and subjects with AE across all regions (p = .013). Post hoc testing reveals higher BP in AN compared to controls (p = .008) and to AE (p = .007). The GG genotype is overrepresented in MDD subjects (p = .059), and BP appears higher with the G allele.

CONCLUSIONS

AN have higher 5-HT(1A) than controls and AE suggesting a model of depression characterized by an over expression of autoinhibitory somatodendritic 5-HT(1A) receptors, perhaps due to the higher expressing G allele, that may result in reduced terminal field 5-HT release. AE appears to have long-term effects on 5-HT(1A).

Brain serotonin1A receptor binding in major depression is related to psychic and somatic anxiety

BACKGROUND

The anxious phenotype of the 5-HT1A receptor knockout mouse and the anxiolytic properties of 5-HT1A agonists suggest that the 5-HT1A receptor modulates anxiety. We investigated the relationship of anxiety expressed in major depressive disorder (MDD) to regional 5-HT1A binding.

METHODS

Positron emission tomography with [carbonyl-11C]WAY-100635 was used to estimate regional 5-HT1A binding potential (BP) in 28 medication-free MDD subjects. Stepwise linear regression assessed the predictive capacity of three anxiety components, derived from a larger MDD sample and termed psychic, somatic, and motoric anxiety, on regional 5-HT1A BP.

RESULTS

Higher psychic (beta >or= .63) and lower somatic (beta <or= -.70) anxiety predicted over 50% of the variance in 5-HT1A BP in multiple cortical regions, but not in amygdala, hippocampus, or autoreceptors of the raphe nuclei. The psychic and somatic anxiety components were not related to depression severity. Comorbid panic disorder was associated with lower cortical and subcortical 5-HT1A BP.

CONCLUSIONS

The 5-HT1A receptor in the same brain regions has different relationships to psychic anxiety versus somatic anxiety. Lower 5-HT1A BP in panic disorder may be accounted for by higher somatic and lower psychic anxiety. Further study of the pathobiology of these anxiety components may identify distinct therapeutic targets or mechanisms.

Probabilistic segmentation of brain tissue in MR imaging

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

Regional heterogeneity of 5-HT1A receptors in human cerebellum as assessed by positron emission tomography

Two measures used in brain imaging are binding potential (BP) and the specific to nonspecific equilibrium partition coefficient (V(3)''). V(3)'' determined using the 5-HT(1A) ligand [(11)C]WAY-100635 is sensitive to changes in the free and nonspecific binding of the ligand in the reference region (V(2)). Healthy female volunteers have higher 5-HT(1A) BP but not V(3)'' compared with men, because V(2) is higher in women. While there could be several explanations for this observation, we hypothesized that women have more 5-HT(1A) receptors in the cerebellum. We explore the cerebellum to define a subregion that more accurately represents the free and nonspecific binding, potentially allowing the use of V(3)''. A quantitative autoradiogram in human brain using [(3)H]WAY-100635 identified a cerebellar subregion devoid of 5-HT(1A) receptors. In vivo 5-HT(1A) receptors were evaluated using [(11)C]WAY-100635 in 12 healthy women and 13 healthy men. Each subject had a metabolite-corrected arterial input function. The autoradiogram demonstrates the lowest concentration of 5-HT(1A) receptors in the cerebellar white matter (CW) and highest concentration in the cerebellar vermis (CV). The CW volume of distribution (V(T)) is lower than CV. Cerebellar white matter is adequately modeled by a one-tissue compartmental model, while a two-tissue model is necessary to model CV or the total cerebellum (CT). Women have a higher CW V(T) compared with men, suggesting a difference in V(2). Use of CW improves identifiability and time stability of BP in cortical regions. Cerebellar white matter might be a better reference region for use in future 5-HT(1A) studies using [(11)C]WAY-100635. With CW as a reference region, V(3)'' cannot be used to detect differences in 5-HT(1A) receptors between men and women, suggesting the need for arterial input functions to determine BP.