Gray Matter Segmentation Research Articles

Integrative analysis of non-targeted lipidomic data and brain structural imaging identifies phosphatidylethanolamine associated with epileptogenesis.

Epilepsy is a chronic disease, while epileptogenesis is a latent period where brain will be transformed into an epileptic one. Mechanisms of epileptogenesis remain unclear. We aim to provide information of hippocampal lipidomic changes related with epileptogenesis in two kindling models. Combining hippocampal structural imaging indices, our study also attempts to assess biochemical alterations as a function of epileptogenesis in a non-invasive way. We constructed two kinds of chemical kindling models, which have long been used as models of epileptogenesis. Two kindling and one control groups were all subjected to structural imaging acquisition after successfully kindled. Voxel-based morphometry, a postprocessing method for brain imaging data, was used to segment and extract hippocampal gray matter volume for subsequent integrative analysis. LC-MS based lipidomic analysis was applied to identify distinct hippocampal lipidomic profiles between kindling and control groups. Further, we regress hippocampal structural indices on lipids to identify those associated with both epileptogenesis and brain structural changes. We report distinct lipidomic profiles between kindling groups and control. A total of 638 lipids were detected in all three groups. Among them were 98 individual lipids, showing significant alterations, in particular lipid class of phosphatidylethanolamine (PE), glucosylceramide and phosphatidylcholine. Hippocampal gray matter volumes were found significant different between groups (P = 0.0223). After combining brain imaging data, we demonstrate several individual PE, namely PE(O-18:1_22:3), PE(O-18:1_22:6) and PE(18:1_18:1), are associated with both epileptogenesis and hippocampal gray matter volume. This study suggests metabolic pathway of PE might involve in epileptogenesis. Also, for the first time, we link level of PE with structural brain imaging indices, in an attempt to potentiate the futuristic application of noninvasive brain imaging techniques to identify epileptogenesis in its latent period.

High-resolution multi-T1 -weighted contrast and T1 mapping with low sensitivity using the fluid and white matter suppression (FLAWS) sequence at 7T.

To demonstrate that fluid and white matter suppression (FLAWS) imaging can be used for high-resolution T1 mapping with low transmitted bias field ( ) sensitivity at 7T. The FLAWS sequence was optimized for 0.8-mm isotropic resolution imaging. The theoretical accuracy and precision of the FLAWS T1 mapping was compared with the one of the magnetization-prepared two rapid gradient echoes (MP2RAGE) sequence optimized for low sensitivity. FLAWS images were acquired at 7T on six healthy volunteers (21 to 48 years old; two women). MP2RAGE and saturation-prepared with two rapid gradient echoes (SA2RAGE) datasets were also acquired to obtain T1 mapping references and maps. The contrast-to-noise ratio (CNR) between brain tissues was measured in the FLAWS-hco and MP2RAGE-uni images. The Pearson correlation was measured between the MP2RAGE and FLAWS T1 maps. The effect of on FLAWS T1 mapping was assessed using the Pearson correlation. The FLAWS-hco images were characterized by a higher brain tissue CNR ( , , ) than the MP2RAGE-uni images ( , , ). The theoretical accuracy and precision of the FLAWS T1 mapping ( ) were in agreement with those provided by the MP2RAGE T1 mapping ( ). A good agreement was found between in vivo T1 values measured with the MP2RAGE and FLAWS sequences (r = 0.91). A weak correlation was found between the FLAWS T1 map and the map within cortical gray matter and white matter segmentations ( ; ). The results from this study suggest that FLAWS is a good candidate for high-resolution T1 -weighted imaging and T1 mapping at the field strength of 7T.

7 T imaging reveals a gradient in spinal cord lesion distribution in multiple sclerosis.

We used 7 T MRI to: (i) characterize the grey and white matter pathology in the cervical spinal cord of patients with early relapsing-remitting and secondary progressive multiple sclerosis; (ii) assess the spinal cord lesion spatial distribution and the hypothesis of an outside-in pathological process possibly driven by CSF-mediated immune cytotoxic factors; and (iii) evaluate the association of spinal cord pathology with brain burden and its contribution to neurological disability. We prospectively recruited 20 relapsing-remitting, 15 secondary progressive multiple sclerosis participants and 11 age-matched healthy control subjects to undergo 7 T imaging of the cervical spinal cord and brain as well as conventional 3 T brain acquisition. Cervical spinal cord imaging at 7 T was used to segment grey and white matter, including lesions therein. Brain imaging at 7 T was used to segment cortical and white matter lesions and 3 T imaging for cortical thickness estimation. Cervical spinal cord lesions were mapped voxel-wise as a function of distance from the inner central canal CSF pool to the outer subpial surface. Similarly, brain white matter lesions were mapped voxel-wise as a function of distance from the ventricular system. Subjects with relapsing-remitting multiple sclerosis showed a greater predominance of spinal cord lesions nearer the outer subpial surface compared to secondary progressive cases. Inversely, secondary progressive participants presented with more centrally located lesions. Within the brain, there was a strong gradient of lesion formation nearest the ventricular system that was most evident in participants with secondary progressive multiple sclerosis. Lesion fractions within the spinal cord grey and white matter were related to the lesion fraction in cerebral white matter. Cortical thinning was the primary determinant of the Expanded Disability Status Scale, white matter lesion fractions in the spinal cord and brain of the 9-Hole Peg Test and cortical thickness and spinal cord grey matter cross-sectional area of the Timed 25-Foot Walk. Spinal cord lesions were localized nearest the subpial surfaces for those with relapsing-remitting and the central canal CSF surface in progressive disease, possibly implying CSF-mediated pathogenic mechanisms in lesion development that may differ between multiple sclerosis subtypes. These findings show that spinal cord lesions involve both grey and white matter from the early multiple sclerosis stages and occur mostly independent from brain pathology. Despite the prevalence of cervical spinal cord lesions and atrophy, brain pathology seems more strongly related to physical disability as measured by the Expanded Disability Status Scale.

Structural Network Analysis Using Diffusion MRI Tractography in Parkinson's Disease and Correlations With Motor Impairment.

Functional impairment of spatially distributed brain regions in Parkinson's disease (PD) suggests changes in integrative and segregative network characteristics, for which novel analysis methods are available. To assess underlying structural network differences between PD patients and controls, we employed MRI T1 gray matter segmentation and diffusion MRI tractography to construct connectivity matrices to compare patients and controls with data originating from two different centers. In the Dutch dataset (Data-NL), 14 PD patients, and 15 healthy controls were analyzed, while 19 patients and 18 controls were included in the Canadian dataset (Data-CA). All subjects underwent T1 and diffusion-weighted MRI. Patients were assessed with Part 3 of the Unified Parkinson's Disease Rating Scale (UPDRS). T1 images were segmented using FreeSurfer, while tractography was performed using ExploreDTI. The regions of interest from the FreeSurfer segmentation were combined with the white matter streamline sets resulting from the tractography, to construct connectivity matrices. From these matrices, both global and local efficiencies were calculated, which were compared between the PD and control groups and related to the UPDRS motor scores. The connectivity matrices showed consistent patterns among the four groups, without significant differences between PD patients and control subjects, either in Data-NL or in Data-CA. In Data-NL, however, global and local efficiencies correlated negatively with UPDRS scores at both the whole-brain and the nodal levels [false discovery rate (FDR) 0.05]. At the nodal level, particularly, the posterior parietal cortex showed a negative correlation between UPDRS and local efficiency, while global efficiency correlated negatively with the UPDRS in the sensorimotor cortex. The spatial patterns of negative correlations between UPDRS and parameters for network efficiency seen in Data-NL suggest subtle structural differences in PD that were below sensitivity thresholds in Data-CA. These correlations are in line with previously described functional differences. The methodological approaches to detect such differences are discussed.

Automated age- and sex-specific volumetric estimation of regional brain atrophy: workflow and feasibility

ObjectivesAn automated workflow for age- and sex-specific estimation of regional brain volume changes from structural MRI relative to a standard population is presented and evaluated for feasibility.MethodsT1w MRI scans are preprocessed in a standardized way comprising gray matter (GM) segmentation, normalization, modulation, and spatial smoothing. Resulting GM images are then compared to precomputed age- and sex-specific GM templates derived from the population-based Nathan Kline Institute Rockland Sample, and voxel-wise z-maps are compiled. z-maps are color-coded and fused with the subject’s T1w images. The rate of technical success of the proposed workflow was evaluated in 1330 subjects of the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Furthermore, medial temporal atrophy (MTA) was assessed using the color-coded maps and with the MTA visual rating scale in these subjects. Sensitivities and specificity of color-coded maps and MTA scale were compared using McNemar’s test.ResultsOne test dataset was excluded due to severe motion artifacts. Out of the remaining 1329 datasets, atrophy map generation was successful in 1323 ADNI subjects (99.5%). Sensitivity for AD diagnosis (71.4 % vs. 53.3%, p < 0.0001 for left; 70.4% vs. 55.3%, p < 0.0001 for right hemisphere) and for MCI (45.4% vs. 17.4, p < 0.0001 for left; 43.5% vs. 14.6%, p < 0.0001 for right hemisphere) based on medial temporal atrophy assessment in color-coded maps was significantly higher than for MTA visual rating scale, while specificity was lower (78.4% vs. 93.8%, p < 0.0001 for left; 79.4% vs. 95.8%, p < 0.0001 for right hemisphere). The workflow is named veganbagel and is published as open-source software with an integrated PACS interface.ConclusionsAutomated brain volume change estimation with the proposed workflow is feasible and technically dependable. It provides high potential for radiologic assessment of brain volume changes and neurodegenerative diseases.Key Points• A workflow combining techniques from voxel-based morphometry and population-based neuroimaging data is feasible and technically highly dependable.• The workflow is provided as open-source software, named veganbagel.• Sensitivity of medial temporal atrophy assessment in atrophy maps from veganbagel exceeds the sensitivity of MTA visual rating scale for the diagnosis of Alzheimer’s disease.

An efficient method for PET image denoising by combining multi-scale transform and non-local means

The diagnosis of dementia, particularly in the early stages is very much helpful with Positron emission tomography (PET) image processing. The most important challenges in PET image processing are noise removal and region of interests (ROIs) segmentation. Although denoising and segmentation are performed independently, but the performance of the denoising process significantly affects the performance of the segmentation process. Due to the low signals to noise ratio and low contrast, PET image denoising is a challenging task. Individual wavelet, curvelet and non-local means (NLM) based methods are not well suited to handle both isotropic (smooth details) and anisotropic (edges and curves) features due to its restricted abilities. To address these issues, the present work proposes an efficient denoising framework for reducing the noise level of brain PET images based on the combination of multi-scale transform (wavelet and curvelet) and tree clustering non-local means (TNLM). The main objective of the proposed method is to extract the isotropic features from a noisy smooth PET image using tree clustering based non-local means (TNLM). Then curvelet-based denoising is applied to the residual image to extract the anisotropic features such as edges and curves. Finally, the extracted anisotropic features are inserted back into the isotropic features to obtain an estimated denoised image. Simulated phantom and clinical PET datasets have been used in this proposed work for testing and measuring the performance in the medical applications, such as gray matter segmentation and precise tumor region identification without any interaction with other structural images like MRI or CT. The results in the experimental section show that the proposed denoising method has obtained better performance than existing wavelet, curvelet, wavelet-curvelet, non-local means (NLM) and deep learning methods based on the preservation of the edges. Qualitatively, a notable gain is achieved in the proposed denoised PET images in terms of contrast enhancement than other existing denoising methods.

Reduced accuracy of MRI deep grey matter segmentation in multiple sclerosis: an evaluation of four automated methods against manual reference segmentations in a multi-center cohort

BackgroundDeep grey matter (DGM) atrophy in multiple sclerosis (MS) and its relation to cognitive and clinical decline requires accurate measurements. MS pathology may deteriorate the performance of automated segmentation methods. Accuracy of DGM segmentation methods is compared between MS and controls, and the relation of performance with lesions and atrophy is studied.MethodsOn images of 21 MS subjects and 11 controls, three raters manually outlined caudate nucleus, putamen and thalamus; outlines were combined by majority voting. FSL-FIRST, FreeSurfer, Geodesic Information Flow and volBrain were evaluated. Performance was evaluated volumetrically (intra-class correlation coefficient (ICC)) and spatially (Dice similarity coefficient (DSC)). Spearman's correlations of DSC with global and local lesion volume, structure of interest volume (ROIV), and normalized brain volume (NBV) were assessed.ResultsICC with manual volumes was mostly good and spatial agreement was high. MS exhibited significantly lower DSC than controls for thalamus and putamen. For some combinations of structure and method, DSC correlated negatively with lesion volume or positively with NBV or ROIV. Lesion-filling did not substantially change segmentations.ConclusionsAutomated methods have impaired performance in patients. Performance generally deteriorated with higher lesion volume and lower NBV and ROIV, suggesting that these may contribute to the impaired performance.

Altered Structural Brain Networks Related to Adrenergic/Muscarinic Receptor Autoantibodies in Chronic Fatigue Syndrome.

Recent studies suggest that the autoantibodies against adrenergic/muscarinic receptors might be one of the causes and potential markers of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The purpose of this study was to investigate the structural network changes related to autoantibody titers against adrenergic/muscarinic receptors in ME/CFS by performing a single-subject gray matter similarity-based structural network analysis. We prospectively examined 89 consecutive right-handed ME/CFS patients who underwent both brain MRI including 3D T1-wighted images and a blood analysis of autoantibodies titers against β1 adrenergic receptor (β1 AdR-Ab), β2 AdR-Ab, M3 acetylcholine receptor (M3 AchR-Ab), and M4 AchR-Ab. Single-subject gray matter similarity-based structural networks were extracted from segmented gray matter images for each patient. We calculated local network properties (betweenness centrality, clustering coefficient, and characteristic path length) and global network properties (normalized path length λ, normalized clustering coefficient γ, and small-world network value δ). We investigated the correlations between the autoantibody titers and regional gray matter/white matter volumes, the local network properties, and the global network properties. Betweenness centrality showed a significant positive correlation with β1-AdR-Ab in the right dorsolateral prefrontal cortex. The characteristic path length showed a significant negative correlation with β2-AdR-Ab in the right precentral gyrus. There were no significant correlations between the antibody titers and the regional gray matter/white matter volumes, and the global network properties. Our findings suggest that β1 AdR-Ab and β2 AdR-Ab are potential markers of ME/CFS.

Regional SUV quantification in hybrid PET/MR, a comparison of two atlas-based automatic brain segmentation methods

BackgroundQuantitative analysis of brain positron-emission tomography (PET) depends on structural segmentation, which can be time-consuming and operator-dependent when performed manually. Previous automatic segmentation usually registered subjects’ images onto an atlas template (defined as RSIAT here) for group analysis, which changed the individuals’ images and probably affected regional PET segmentation. In contrast, we could register atlas template to subjects’ images (RATSI), which created an individual atlas template and may be more accurate for PET segmentation. We segmented two representative brain areas in twenty Parkinson disease (PD) and eight multiple system atrophy (MSA) patients performed in hybrid positron-emission tomography/magnetic resonance imaging (PET/MR). The segmentation accuracy was evaluated using the Dice coefficient (DC) and Hausdorff distance (HD), and the standardized uptake value (SUV) measurements of these two automatic segmentation methods were compared, using manual segmentation as a reference.ResultsThe DC of RATSI increased, and the HD decreased significantly (P < 0.05) compared with the RSIAT in PD, while the results of one-way analysis of variance (ANOVA) found no significant differences in the SUVmean and SUVmax among the two automatic and the manual segmentation methods. Further, RATSI was used to compare regional differences in cerebral metabolism pattern between PD and MSA patients. The SUVmean in the segmented cerebellar gray matter for the MSA group was significantly lower compared with the PD group (P < 0.05), which is consistent with previous reports.ConclusionThe RATSI was more accurate for the caudate nucleus and putamen automatic segmentation and can be used for regional PET analysis in hybrid PET/MR.

Biological sex classification with structural MRI data shows increased misclassification in transgender women

Transgender individuals (TIs) show brain-structural alterations that differ from their biological sex as well as their perceived gender. To substantiate evidence that the brain structure of TIs differs from male and female, we use a combined multivariate and univariate approach. Gray matter segments resulting from voxel-based morphometry preprocessing of N = 1753 cisgender (CG) healthy participants were used to train (N = 1402) and validate (20% holdout N = 351) a support-vector machine classifying the biological sex. As a second validation, we classified N = 1104 patients with depression. A third validation was performed using the matched CG sample of the transgender women (TW) application sample. Subsequently, the classifier was applied to N = 26 TW. Finally, we compared brain volumes of CG-men, women, and TW-pre/post treatment cross-sex hormone treatment (CHT) in a univariate analysis controlling for sexual orientation, age, and total brain volume. The application of our biological sex classifier to the transgender sample resulted in a significantly lower true positive rate (TPR-male = 56.0%). The TPR did not differ between CG-individuals with (TPR-male = 86.9%) and without depression (TPR-male = 88.5%). The univariate analysis of the transgender application-sample revealed that TW-pre/post treatment show brain-structural differences from CG-women and CG-men in the putamen and insula, as well as the whole-brain analysis. Our results support the hypothesis that brain structure in TW differs from brain structure of their biological sex (male) as well as their perceived gender (female). This finding substantiates evidence that TIs show specific brain-structural alterations leading to a different pattern of brain structure than CG-individuals.

P79 StarNEt: A convolutional neural network for automated white and gray matter segmentation from magnetic resonance imaging in the human brain

P79 StarNEt: A convolutional neural network for automated white and gray matter segmentation from magnetic resonance imaging in the human brain

On the value of QSM from MPRAGE for segmenting and quantifying iron-rich deep gray matter.

Quantitative susceptibility mapping (QSM) has been employed for both iron evaluation and segmentation of deep gray matter (DGM), but QSM sequences are not typically used in standard brain volumetric studies, which use T1-weighted magnetization-prepared rapid acquisition with gradient echo (MPRAGE) with short TE. Here, QSM produced directly from standard MPRAGE phase ( ) is evaluated for segmentation and quantification of highly iron-rich DGM regions. Simulations were used to explore quality and possible limitations. In addition, QSM from a standard multi-echo gradient-echo ( ) was compared to in 40 healthy adults at 3T. DGM structures with weak contrast on MPRAGE magnitude were evaluated for improving segmentation with , with focus on the iron-rich globus pallidus (GP). Furthermore, susceptibility quantification was assessed on six DGM nuclei and compared to standard . Limited by TE and signal-to-noise ratio, only iron-rich regions like GP and dentate nucleus produced adequate contrast on , confining applications to such regions. improved GP segmentation with mean Dice scores raised by 9.0%, and mean volumetric differences reduced by 9.7%. Simulations suggested that phase contrast-to-noise ratio (CNR) should be above 3.0 to attain segmentation improvement. For quantification purposes, higher CNR is required, and typical provided comparable estimates to in large iron-rich DGM nuclei. Despite the short TE of standard MPRAGE, can improve GP segmentation over the use of MPRAGE magnitude alone and roughly quantify high-iron regions in DGM. Thus, reconstructing can be a useful addition to volumetric studies that rarely include standard .

A novel CNN based Alzheimer’s disease classification using hybrid enhanced ICA segmented gray matter of MRI

Predicting Alzheimer’s Disease (AD) from Mild Cognitive Impairment (MCI) and Cognitive Normal (CN) has become wide. Recent advancement in neuroimaging in adoption with machine learning techniques are especially useful for pattern recognition of medical imaging to assist the physician in early diagnosis of AD. It is observed that the early abnormal brain atrophy and healthy brain atrophy are same. In our endeavor, we proposed a model that differentiation MCI and CN more accurately to escalate early diagnosis of AD. In this paper, we applied both binary and multi class classification, 4463 Slide are divided in to two groups one for training and another for testing at subject level, achieves 100 % of accuracy, 100 % of sensitivity and 100 % of Specificity in the case of AD-CN. 96.2 % of accuracy, 93 % Sensitivity and 100 % Specificity in the case of AD-MCI. 98.0 % of accuracy, 96 % of sensitivity, 100 specificity in the case of CN-MCI. 86.7 % accuracy, 89.6 % of sensitivity, 86.61 % of specificity in the case of AD-MCI-CN. The model is further tested using 10 fold cross validation and obtained 98.0 % of accuracy, to differentiate CN and MCI. Our proposed framework generated results are significantly improving prediction of AD from MCI and CN than compare to the previous work flows and used to differentiate the AD at early stage.

Influence of Mild White Matter Lesions on Voxel-based Morphometry

Purpose:The aim of this study was to investigate whether the detectability of brain volume change in voxel-based morphometry (VBM) with gray matter images is affected by mild white matter lesions (MWLs).Methods:Three-dimensional T1-weighted images (3D-T1WIs) of 11 healthy subjects were obtained using a 3T MR scanner. We initially created 3D-T1WIs with focal cortical atrophy simulated cortical atrophy in left amygdala (type A) and the left medial frontal lobe (type B) from control 3D-T1WIs. Next, the following three types of MWL images were created: type A + 1L and type B + 1L images, only one white matter lesion; type A + 4L and type B + 4L images, four white matter lesions at distant positions; and type A + 4L* and type B + 4L* images, four white matter lesions at clustered positions. Comparisons between the control group and the other groups were performed with VBM using segmented gray matter images.Results:The gray matter volume was significantly lower in the type A group than in the control group, and similar results were observed in the type A + 1L, type A + 4L, and type A + 4L* groups. Additionally, the gray matter volume was significantly lower in the type B group than in the control group, and similar results were observed in the type B + 1L, type B + 4L, and type B + 4L* groups, but the cluster size in type B + 4L* was smaller than that in type B.Conclusion:Our study showed that the detectability of brain volume change in VBM with gray matter images was not decreased by MWLs as lacunar infarctions. Therefore, we think that group comparisons with VBM should be analyzed by groups including and excluding subjects with MWLs, respectively.

Simultaneous voxel-based magnetic susceptibility and morphometry analysis using magnetization-prepared spoiled turbo multiple gradient echo.

This study aimed to develop and test a simultaneous acquisition and analysis pipeline for voxel-based magnetic susceptibility and morphometry (VBMSM) on a single dataset using young volunteers, elderly healthy volunteers, and an Alzheimer's disease (AD) group. 3D T1 -weighted and multi-echo phase images for VBM and quantitative susceptibility mapping (QSM) were simultaneously acquired using a magnetization-prepared spoiled turbo multiple gradient echo sequence with inversion pulse for QSM (MP-QSM). The magnitude image was split into gray matter (GM) and white matter (WM) and was spatially normalized. The susceptibility map was reconstructed from the phase images. The segmented image and susceptibility map were compared with those obtained from conventional multiple spoiled gradient echo (mGRE) and MP-spoiled gradient echo (MP-GRE) in healthy volunteers to validate the availability of MP-QSM by numerical measurements. To assess the feasibility of the VBMSM analysis pipeline, voxel-based comparisons of susceptibility and morphometry in MP-QSM were conducted in volunteers with a bimodal age distribution, and in elderly volunteers and the AD group, using spatially normalized GM and WM volume images and a susceptibility map. GM/WM contrasts in MP-QSM, MP-GRE, and mGRE were 0.14 ± 0.011, 0.17 ± 0.015, and 0.045 ± 0.010, respectively. Segmented GM and WM volumes in the MP-QSM closely coincided with those in the MP-GRE. Region of interest analyses indicated that the mean susceptibility values in MP-QSM were completely in agreement with those in mGRE. In an evaluation of the aging effect, a significant increase and decrease in susceptibility and volume were found by VBMSM in deep GM and WM, respectively. Between the elderly volunteers and the AD group, the characteristic susceptibility and volume changes in GM and WM were observed. The proposed MP-QSM sequence makes it possible to acquire acceptable-quality images for simultaneous analysis and determine brain atrophy and susceptibility distribution without image registration by using voxel-based analyses.

Gray Matter Atrophy in Parkinson's Disease and the Parkinsonian Variant of Multiple System Atrophy: A Combined ROI- and Voxel-Based Morphometric Study

OBJECTIVES:Parkinson’s disease (PD) and the parkinsonian variant of multiple system atrophy (MSA-P) are distinct neurodegenerative disorders that share similar clinical features of parkinsonism. The morphological alterations of these diseases have yet to be understood. The purpose of this study was to evaluate gray matter atrophy in PD and MSA-P using regions of interest (ROI)-based measurements and voxel-based morphometry (VBM).METHODS:We studied 41 patients with PD, 20 patients with MSA-P, and 39 controls matched for age, sex, and handedness using an improved T1-weighted sequence that eased gray matter segmentation. The gray matter volumes were measured using ROI and VBM.RESULTS:ROI volumetric measurements showed significantly reduced bilateral putamen volumes in MSA-P patients compared with those in PD patients and controls (p<0.05), and the volumes of the bilateral caudate nucleus were significantly reduced in both MSA-P and PD patients compared with those in the controls (p<0.05). VBM analysis revealed multifocal cortical and subcortical atrophy in both MSA-P and PD patients, and the volumes of the cerebellum and temporal lobes were remarkably reduced in MSA-P patients compared with the volumes in PD patients (p<0.05).CONCLUSIONS:Both PD and MSA-P are associated with gray matter atrophy, which mainly involves the bilateral putamen, caudate nucleus, cerebellum, and temporal lobes. ROI and VBM can be used to identify these morphological alterations, and VBM is more sensitive and repeatable and less time-consuming, which may have potential diagnostic value.

Lower gray matter volumes of frontal lobes and insula in adolescents with anorexia nervosa restricting type: Findings from a Brain Morphometry Study.

Brain atrophy in anorexia nervosa (AN) is one of the most marked structural brain changes observed in mental disorders. In this study, we propose a whole brain analysis approach to characterize global and regional cerebral volumes in adolescents with restricting-type anorexia nervosa (AN-r). A total of 48 adolescent females (age range 13-18 years) were enrolled in the study (24 right-handed AN-r in the early stages of the illness and treated in the same clinical setting and 24 age-matched healthy controls [HC]). High-resolution T1-weighted magnetic resonance images were acquired. Cerebral volumes, including the total amounts of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) were obtained with the Statistical Parametric Mapping software (SPM8); specific cortical regional volumes were computed by applying an atlas-based cortical parcellation to the SPM8 GM segments. Analysis of variance (ANOVA) was performed to identify any significant between-group differences in global and regional brain volumes. The analyses revealed reduced total GM volumes (p= 0.02) and increased CSF (p= 0.05) in AN-r, compared with HC. No significant between-group difference was found in WM volumes. At the regional level, significantly lower GM volumes in both frontal lobes (p= 0.006) and in the left insula (p= 0.016) were detected. No significant relationships were found between cerebral volumes and duration of illness, psychiatric comorbidities, psychopharmacological treatment, prepubertal phase, or presence of amenorrhea. The topographic distribution of GM reduction in a homogenous group of AN-r involves regions responsible for the emotional and cognitive deficits associated with the illness. These findings are discussed in relation to the roles of the insular cortex and the frontal lobes.

SUITer: An Automated Method for Improving Segmentation of Infratentorial Structures at Ultra-High-Field MRI.

The advent of high and ultra-high-field MRI has significantly improved the investigation of infratentorial structures by providing high-resolution images. However, none of the publicly available methods for cerebellar image analysis has been optimized for high-resolution images yet. We present the implementation of an automated algorithm-SUITer (spatially unbiased infratentorial for enhanced resolution) method for cerebellar lobules parcellation on high-resolution MR images acquired at both 3 and 7T MRI. SUITer was validated on five manually segmented data and compared with SUIT, FreeSurfer, and convolutional neural networks (CNN). SUITer was then applied to 3 and 7T MR images from 10 multiple sclerosis (MS) patients and 10 healthy controls (HCs). The difference in volumes estimation for the cerebellar grey matter (GM), between the manual segmentation (ground truth), SUIT, CNN, and SUITer was reduced when computed by SUITer compared to SUIT (5.56 vs. 29.23 mL) and CNN (5.56 vs. 9.43 mL). FreeSurfer showed low volumes difference (3.56 mL). SUITer segmentations showed a high correlation (R2 = .91) and a high overlap with manual segmentations for cerebellar GM (83.46%). SUITer also showed low volumes difference (7.29 mL), high correlation (R2 = .99), and a high overlap (87.44%) for cerebellar GM segmentations across magnetic fields. SUITer showed similar cerebellar GM volume differences between MS patients and HC at both 3T and 7T (7.69 and 7.76 mL, respectively). SUITer provides accurate segmentations of infratentorial structures across different resolutions and MR fields.

An efficient wavelet and curvelet-based PET image denoising technique.

Positron emission tomography (PET) image denoising is a challenging task due to the presence of noise and low spatial resolution compared with other imaging techniques such as magnetic resonance imaging (MRI) and computed tomography (CT). PET image noise can hamper further processing and analysis, such as segmentation and disease screening. The wavelet transform-based techniques have often been proposed for PET image denoising to handle isotropic (smooth details) features. The curvelet transform-based PET image denoising techniques have the ability to handle multi-scale and multi-directional properties such as edges and curves (anisotropic features) as compared with wavelet transform-based denoising techniques. The wavelet denoising method is not optimal for anisotropic features, whereas the curvelet denoising method sometimes has difficulty in handling isotropic features. In order to handle the weaknesses of individual wavelet and curvelet-based methods, the present research proposes an efficient PET image denoising technique based on the combination of wavelet and curvelet transforms, along with a new adaptive threshold selection to threshold the wavelet coefficients in each subband (except last level low pass (LL) residual). The proposed threshold utilizes the advantages of adaptive threshold taken from BayesShrink along with the neighborhood window concept. The present method was tested on both simulated phantom and clinical PET datasets. Experimental results show that our method has achieved better results than the existing methods such as VisuShrink, BayesShrink, NeighShrink, ModineighShrink, curvelet, and an existing wavelet curvelet-based method with respect to different noise measurement metrics, such as mean squared error (MSE), signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), and image quality index (IQI). Furthermore, notable performance is achieved in the case of medical applications such as gray matter segmentation and precise tumor region identification. Graphical Abstract Block diagram of the proposed method. (a) Steps of the proposed denoising method and (b) image information generated by each step of (a).

STrategically Acquired Gradient Echo (STAGE) imaging, part III: Technical advances and clinical applications of a rapid multi-contrast multi-parametric brain imaging method

One major thrust in radiology today is image standardization with a focus on rapidly acquired quantitative multi-contrast information. This is critical for multi-center trials, for the collection of big data and for the use of artificial intelligence in evaluating the data. Strategically acquired gradient echo (STAGE) imaging is one such method that can provide 8 qualitative and 7 quantitative pieces of information in 5 min or less at 3 T. STAGE provides qualitative images in the form of proton density weighted images, T1 weighted images, T2* weighted images and simulated double inversion recovery (DIR) images. STAGE also provides quantitative data in the form of proton spin density, T1, T2* and susceptibility maps as well as segmentation of white matter, gray matter and cerebrospinal fluid. STAGE uses vendors' product gradient echo sequences. It can be applied from 0.35 T to 7 T across all manufacturers producing similar results in contrast and quantification of the data. In this paper, we discuss the strengths and weaknesses of STAGE, demonstrate its contrast-to-noise (CNR) behavior relative to a large clinical data set and introduce a few new image contrasts derived from STAGE, including DIR images and a new concept referred to as true susceptibility weighted imaging (tSWI) linked to fluid attenuated inversion recovery (FLAIR) or tSWI-FLAIR for the evaluation of multiple sclerosis lesions. The robustness of STAGE T1 mapping was tested using the NIST/NIH phantom, while the reproducibility was tested by scanning a given individual ten times in one session and the same subject scanned once a week over a 12-week period. Assessment of the CNR for the enhanced T1W image (T1WE) showed a significantly better contrast between gray matter and white matter than conventional T1W images in both patients with Parkinson's disease and healthy controls. We also present some clinical cases using STAGE imaging in patients with stroke, metastasis, multiple sclerosis and a fetus with ventriculomegaly. Overall, STAGE is a comprehensive protocol that provides the clinician with numerous qualitative and quantitative images.