Search

The falx cerebri is a meningeal projection of dura in the brain, separating the cerebral hemispheres. It has stiffer mechanical properties than surrounding tissue and must be accurately segmented for building computational models of traumatic brain injury. In this work, we propose a method to segment the falx using T1-weighted magnetic resonance images (MRI) and susceptibility-weighted MRI (SWI). Multi-atlas whole brain segmentation is performed using the T1-weighted MRI and the gray matter cerebrum labels are extended into the longitudinal fissure using fast marching to find an initial estimate of the falx. To correct the falx boundaries, we register and then deform a set of SWI with manually delineated falx boundaries into the subject space. The continuous-STAPLE algorithm fuses sets of corresponding points to produce an estimate of the corrected falx boundary. Correspondence between points on the deformed falx boundaries is obtained using coherent point drift. We compare our method to manual ground truth, a multi-atlas approach without correction, and single-atlas approaches.

Background and Purpose: MR imaging can be used to measure structural changes in the brains of individuals with multiple sclerosis and is essential for diagnosis, longitudinal monitoring, and therapy evaluation. The North American Imaging in Multiple Sclerosis Cooperative steering committee developed a uniform high-resolution 3T MR imaging protocol relevant to the quantification of cerebral lesions and atrophy and implemented it at 7 sites across the United States. To assess intersite variability in scan data, we imaged a volunteer with relapsing-remitting MS with a scan-rescan at each site., Materials and Methods: All imaging was acquired on Siemens scanners (4 Skyra, 2 Tim Trio, and 1 Verio). Expert segmentations were manually obtained for T1-hypointense and T2 (FLAIR) hyperintense lesions. Several automated lesion-detection and whole-brain, cortical, and deep gray matter volumetric pipelines were applied. Statistical analyses were conducted to assess variability across sites, as well as systematic biases in the volumetric measurements that were site-related., Results: Systematic biases due to site differences in expert-traced lesion measurements were significant ( P < .01 for both T1 and T2 lesion volumes), with site explaining >90% of the variation (range, 13.0-16.4 mL in T1 and 15.9-20.1 mL in T2) in lesion volumes. Site also explained >80% of the variation in most automated volumetric measurements. Output measures clustered according to scanner models, with similar results from the Skyra versus the other 2 units., Conclusions: Even in multicenter studies with consistent scanner field strength and manufacturer after protocol harmonization, systematic differences can lead to severe biases in volumetric analyses., (© 2017 by American Journal of Neuroradiology.)

Diffuse axonal injury (DAI) is a debilitating consequence of traumatic brain injury (TBI) attributed to abnormal stretching of axons caused by blunt head trauma or acceleration of the head. We developed an anatomically accurate, subject-specific, three-dimensional (3D) computational model of the human brain, and used it to study the dynamic deformations in the substructures of the brain when the head is subjected to rotational accelerations. The computational head models use anatomy and morphology of the white matter fibers obtained using MRI. Subject-specific full-field shearing motions in live human brains obtained through a recently developed tagged MRI imaging technique are then used to validate the models by comparing the measured and predicted heterogeneous dynamic mechanical response of the brain. These results are used to elucidate the dynamics of local shearing deformations in the brain substructures caused by rotational acceleration of the head. Our work demonstrates that the rotational dynamics of the brain has a timescale of ∼100 ms as determined by the shearing wave speeds, and thus the injuries associated with rotational accelerations likely occur over these time scales. After subject-specific validation using the live human subject data, a representative subject-specific head model is used to simulate a real life scenario that resulted in a concussive injury. Results suggest that regions of the brain, in the form of a toroid, encompassing the white matter, the cortical gray matter, and outer parts of the limbic system have a higher susceptibility to injury under axial rotations of the head.

Increased filaggrin expression was found to be correlated with severity scores in chronic spontaneous urticaria (CSU); however, the role of filaggrin breakdown products (FBPs) in CSU has not been studied. We collected stratum corneum (SC) specimens from the volar forearms of 10 CSU patients, 10 AD patients, and 10 healthy normal controls (NCs) and measured contents of FBPs (pyrrolidone carboxylic acid [PCA] and urocanic acid [UCA]) using UPLC-MS/MS, transepidermal water loss (TEWL) and epidermal pH. Compared to NCs, cis-UCA level was increased in CSU lesions (P < 0.05) and decreased in AD lesions (P < 0.01). The cis-to-trans-UCA ratio in SC specimens from CSU patients was significantly greater than those from AD and NC subjects. AD lesions had lower FBP and PCA contents compared to NC skin (both P < 0.001), and higher TEWL and pH compared to CSU lesions. Moreover, cis-UCA, but not trans-UCA, enhanced the IgE-mediated basophil activation, as well as IgE- and calcium-mediated degranulation of LAD-2 cells, in a dose-dependent manner. These findings suggest that increased cis-to-trans UCA ratio in the epidermis is a distinct feature of CSU, which could enhance mast cell degranulation. Modulation of cis-UCA may be a potential target for skin diseases associated with IgE-mediated mast cell degranulation.

The data presented in this article is related to the research article entitled "Longitudinal multiple sclerosis lesion segmentation: Resource and challenge" (Carass et al., 2017) [1]. In conjunction with the 2015 International Symposium on Biomedical Imaging, we organized a longitudinal multiple sclerosis (MS) lesion segmentation challenge providing training and test data to registered participants. The training data consists of five subjects with a mean of 4.4 (±0.55) time-points, and test data of fourteen subjects with a mean of 4.4 (±0.67) time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. The training data including multi-modal scans and manually delineated lesion masks is available for download. In addition, the testing data is also being made available in conjunction with a website for evaluating the automated analysis of the testing data.

In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website 2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Environmental cues provoke rapid transitions in gene expression to support growth and cellular plasticity through incompletely understood mechanisms. Lin28 RNA-binding proteins have evolutionarily conserved roles in post-transcriptional coordination of pro-growth gene expression, but signaling pathways allowing trophic stimuli to induce Lin28 have remained uncharacterized. We find that Lin28a protein exhibits rapid basal turnover in neurons and that mitogen-activated protein kinase (MAPK)-dependent phosphorylation of the RNA-silencing factor HIV TAR-RNA-binding protein (TRBP) promotes binding and stabilization of Lin28a, but not Lin28b, with an accompanying reduction in Lin28-regulated miRNAs, downstream of brain-derived neurotrophic factor (BDNF). Binding of Lin28a to TRBP in vitro is also enhanced by phospho-mimic TRBP. Further, phospho-TRBP recapitulates BDNF-induced neuronal dendritic spine growth in a Lin28a-dependent manner. Finally, we demonstrate MAPK-dependent TRBP and Lin28a induction, with physiological function in growth and survival, downstream of diverse growth factors in multiple primary cell types, supporting a broad role for this pathway in trophic responses., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Objective: Neural cell adhesion molecule (CD56) has been proposed as a potential marker for neuroendocrine differentiation in carcinomas, together with synaptophysin and chromogranin A. However, CD56 immunoreactivity by itself can be found in a broad variety of tumors, including ovarian neoplasms. CD56 has recently been suggested as a potential target for antibody-based therapy. However, for ovarian carcinoma, there is only limited data available regarding the pattern of CD56 immunoreactivity, coexpression of neuroendocrine markers, and correlation with histological types and clinical parameters., Methods: In our study, we therefore evaluated CD56 staining by immunohistochemistry on a tissue micrroarray with 206 ovarian carcinomas, including 151 high-grade serous, 7 low-grade serous, 32 endometrioid, 11 clear cell, 5 mucinous, as well as 33 atypically proliferating serous tumors/serous borderline tumors., Results: At least focal CD56 immunoreactivity was observed in 65% of carcinomas of all histological types. Moderate staining with at least 10% positive cells was found in 44 (28%) high-grade serous carcinomas (HGSOCs), 2 (29%) low-grade serous and 3(9%) endometrioid carcinomas. Strong immunoreactivity was limited to 10 (7%) HGSOCs. There was no correlation with the expression of chromogranin or synaptophysin. Serous borderline tumors showed only weak and focal staining in 11 (33%). Expression of CD56 overall was significantly associated with high-grade and advanced stage. In the subgroup of HGSOCs, CD56 expression was associated with reduced overall survival (median 30 vs. 47 months, P = 0.039, log rank, univariate analysis)., Conclusions: CD56 (neural cell adhesion molecule) is frequently expressed in ovarian carcinomas and is significantly associated with HGSOC and advanced tumor stage. Due to its lack of correlation with neuroendocrine differentiation, CD56 expression is of limited diagnostic value, but may rather serve as a marker for tumor progression or as a potential therapeutic target.

The falx cerebri and tentorium cerebelli are dural structures found in the brain. Due to the roles both structures play in constraining brain motion, the falx and tentorium must be identified and included in finite element models of the head to accurately predict brain dynamics during injury events. To date there has been very little research work on automatically segmenting these two structures, which is understandable given that their 1) thin structure challenges the resolution limits of in vivo 3D imaging, and 2) contrast with respect to surrounding tissue is low in standard magnetic resonance imaging. An automatic segmentation algorithm to find the falx and tentorium which uses the results of a multi-atlas segmentation and cortical reconstruction algorithm is proposed. Gray matter labels are used to find the location of the falx and tentorium. The proposed algorithm is applied to five datasets with manual delineations. 3D visualizations of the final results are provided, and Hausdorff distance (HD) and mean surface distance (MSD) is calculated to quantify the accuracy of the proposed method. For the falx, the mean HD is 43.84 voxels and the mean MSD is 2.78 voxels, with the largest errors occurring at the frontal inferior falx boundary. For the tentorium, the mean HD is 14.50 voxels and mean MSD is 1.38 voxels.

Automatic skull-stripping or brain extraction of magnetic resonance (MR) images is often a fundamental step in many neuroimage processing pipelines. The accuracy of subsequent image processing relies on the accuracy of the skull-stripping. Although many automated stripping methods have been proposed in the past, it is still an active area of research particularly in the context of brain pathology. Most stripping methods are validated on T 1 -w MR images of normal brains, especially because high resolution T 1 -w sequences are widely acquired and ground truth manual brain mask segmentations are publicly available for normal brains. However, different MR acquisition protocols can provide complementary information about the brain tissues, which can be exploited for better distinction between brain, cerebrospinal fluid, and unwanted tissues such as skull, dura, marrow, or fat. This is especially true in the presence of pathology, where hemorrhages or other types of lesions can have similar intensities as skull in a T 1 -w image. In this paper, we propose a sparse patch based Multi-cONtrast brain STRipping method (MONSTR), 2 where non-local patch information from one or more atlases, which contain multiple MR sequences and reference delineations of brain masks, are combined to generate a target brain mask. We compared MONSTR with four state-of-the-art, publicly available methods: BEaST, SPECTRE, ROBEX, and OptiBET. We evaluated the performance of these methods on 6 datasets consisting of both healthy subjects and patients with various pathologies. Three datasets (ADNI, MRBrainS, NAMIC) are publicly available, consisting of 44 healthy volunteers and 10 patients with schizophrenia. Other three in-house datasets, comprising 87 subjects in total, consisted of patients with mild to severe traumatic brain injury, brain tumors, and various movement disorders. A combination of T 1 -w, T 2 -w were used to skull-strip these datasets. We show significant improvement in stripping over the competing methods on both healthy and pathological brains. We also show that our multi-contrast framework is robust and maintains accurate performance across different types of acquisitions and scanners, even when using normal brains as atlases to strip pathological brains, demonstrating that our algorithm is applicable even when reference segmentations of pathological brains are not available to be used as atlases., (Copyright © 2016 Elsevier Inc. All rights reserved.)

By choosing different pulse sequences and their parameters, magnetic resonance imaging (MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield inconsistencies with MRI acquisitions across datasets or scanning sessions that can in turn cause inconsistent automated image analysis. Although image synthesis of MR images has been shown to be helpful in addressing this problem, an inability to synthesize both T 2 -weighted brain images that include the skull and FLuid Attenuated Inversion Recovery (FLAIR) images has been reported. The method described herein, called REPLICA, addresses these limitations. REPLICA is a supervised random forest image synthesis approach that learns a nonlinear regression to predict intensities of alternate tissue contrasts given specific input tissue contrasts. Experimental results include direct image comparisons between synthetic and real images, results from image analysis tasks on both synthetic and real images, and comparison against other state-of-the-art image synthesis methods. REPLICA is computationally fast, and is shown to be comparable to other methods on tasks they are able to perform. Additionally REPLICA has the capability to synthesize both T 2 -weighted images of the full head and FLAIR images, and perform intensity standardization between different imaging datasets., (Copyright © 2016 Elsevier B.V. All rights reserved.)

We assessed the utility of a brief MRI protocol, appropriate for the acute setting, to detect acute traumatic brain injury (TBI) in patients with suspected mild TBI (mTBI) and distinguish traumatic from nontraumatic brain injury by comparing trauma with nontrauma patients. Twenty-two patients with suspected mTBI were included in this exploratory study over a period of 9 months. Median time from injury to MR scanning was 5.4 h (interquartile range 3.6-15.3). To determine the specificity of certain findings for TBI, 61 patients presenting with suspected minor acute stroke were included as a comparative group using the same MRI methods. A selected series of MRI sequences (diffusion-weighted imaging, fluid attenuated inversion recovery [FLAIR], and T2* weighted) were independently evaluated by two neuroradiologists blinded to clinical diagnosis, for presence of specific findings. In a separate session, all cases in which at least one MRI sequence above was positive were classified as TBI, stroke, or indeterminate. Intracranial MRI abnormalities were observed in 47 (57%) of the 83 studied patients. Based on findings on MRI, 12 (55%) of 22 suspected mTBI patients were classified as having traumatic injury. Nine (47%) of the 19 suspected mTBI patients with a negative CT had findings on MRI. Abnormalities on MRI consistent with trauma were observed most frequently on postcontrast FLAIR (83%) and T2*-weighted (58%) sequences. We demonstrated the ability of a fast MRI protocol to identify trauma-related abnormalities not seen on CT, and differentiate acute trauma from nonspecific chronic disease in a blinded cohort of mTBI patients., Competing Interests: Author Disclosure Statement No competing financial interests exist.

It is not known whether lateralized olfactory sensitivity deficits are present in MS. Since projections from the olfactory bulb to the olfactory cortex are largely ipsilateral, and since both functional imaging and psychophysical studies suggest that the right side of the brain may be more involved in olfactory processing than the left, we addressed this issue by administering well-validated tests of odor detection, along with tests of odor identification, to each side of the nose of 73 MS patients and 73 age-, gender-, and race-matched normal controls. We also determined, in 63 of the MS patients, whether correlations were present between the olfactory test measures and MRI-determined lesions in brain regions ipsilateral and contralateral to the nose side that was tested. No significant left:right differences in either olfactory sensitivity or identification were present, although in both cases mean performance was lower in the MS than in the control subjects (ps<0.0001). Scores on the two sides of the nose were positively correlated with one another (threshold r=0.56, p<0.0001; Identification r=0.71, p<0.0001). The percent of MS patients whose bilateral test scores fell below the 10th percentile of controls did not differ between the odor identification and detection threshold tests. Both left and right odor identification and detection test scores were weakly correlated with lesion volumes in temporal and frontal lobe brain regions (r's<0.40). Our findings demonstrate that MS does not differentially influence odor perception on left and right sides of the nose, regardless of whether sensitivity or identification is being measured. They also indicate that tests of odor identification and detection are similarly influenced by MS and that such influences are associated with central brain lesions., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Post-traumatic stress disorder (PTSD) is commonly associated with mild traumatic brain injury (mTBI). To better understand their relationship, we examined neuroanatomical structures and neuropsychological performance in a sample of individuals with mTBI, with and without PTSD symptoms. Thirty-nine subjects with mTBI were dichotomized into those with (n = 12) and without (n = 27) significant PTSD symptoms based on scores on the PTSD Checklist. Using a region-of-interest approach, fronto-temporal volumes, fiber bundles obtained by diffusion tensor imaging, and neuropsychological scores were compared between the two groups. After controlling for total intracranial volume and age, subjects with mTBI and PTSD symptoms exhibited volumetric differences in the entorhinal cortex, an area associated with memory networks, relative to mTBI-only patients (F = 4.28; p = 0.046). Additionally, subjects with PTSD symptoms showed reduced white matter integrity in the right cingulum bundle (axial diffusivity, F = 6.04; p = 0.020). Accompanying these structural alterations, mTBI and PTSD subjects also showed impaired performance in encoding (F = 5.98; p = 0.019) and retrieval (F = 7.32; p = 0.010) phases of list learning and in tests of processing speed (Wechsler Adult Intelligence Scale Processing Speed Index, F = 12.23; p = 0.001; Trail Making Test A, F = 5.56; p = 0.024). Increased volume and white matter disruptions in these areas, commonly associated with memory functions, may be related to functional disturbances during cognitively demanding tasks. Differences in brain volume and white matter integrity between mTBI subjects and those with mTBI and co-morbid PTSD symptoms point to neuroanatomical differences that may underlie poorer recovery of mTBI subjects who experience PTSD symptoms. These findings support theoretical models of PTSD and its relationship to learning deficits., Competing Interests: Author Disclosure Statement No competing financial interests exist.

Background: Autophagy and neutrophil extracellular DNA traps (NETs) are implicated in asthma; however, their roles in asthma pathogenesis have not been elucidated., Objectives: We compared autophagy and NET production levels from peripheral blood neutrophils (PBNs) of patients with severe asthma (SA) and non-severe asthma (NSA). Additionally, we investigated the inflammatory effects of NETs on human airway epithelial cells (AECs) and peripheral blood eosinophils (PBEs)., Methods: Peripheral blood neutrophils from patients with SA (n = 30) and NSA (n = 38) were treated with interleukin (IL)-8 (100 ng/mL). Autophagy (light chain 3-II expression) and NET production levels were evaluated by Western blot, immunofluorescence microscopy, and PicoGreen assay. The effects of NETs on AECs were assessed by investigating cell death, cell detachment, expression of occludin and claudin-1, and IL-8 production; the effects of NETs on PBEs were examined by investigating the activation and release of eosinophil cationic protein (ECP) and eosinophil-derived neurotoxin (EDN)., Results: Untreated and IL-8-treated PBNs from the SA group produced higher autophagy and NET levels compared with those from the NSA group (P < 0.01). IL-8 increased autophagy and NET levels in PBNs from the SA group, but not from the NSA group. NET levels were correlated with autophagy levels in PBNs (P < 0.001). IL-8-induced NET production levels negatively were correlated with FEV1/FVC (r = -0.700, P = 0.016). NETs induced cell death, detachment, degradation of occludin and claudin-1, and IL-8 production from AECs. Higher levels of NET-induced ECP and EDN were released from PBEs in SA compared with NSA groups., Conclusions and Clinical Relevance: Neutrophil autophagy and NETs could enhance asthma severity by damaging airway epithelium and triggering inflammatory responses of AECs and PBEs. Modulating neutrophil autophagy and NET production may be a new target therapy for SA., (© 2016 John Wiley & Sons Ltd.)

Different magnetic resonance imaging pulse sequences are used to generate image contrasts based on physical properties of tissues, which provide different and often complementary information about them. Therefore multiple image contrasts are useful for multimodal analysis of medical images. Often, medical image processing algorithms are optimized for particular image contrasts. If a desirable contrast is unavailable, contrast synthesis (or modality synthesis) methods try to "synthesize" the unavailable constrasts from the available ones. Most of the recent image synthesis methods generate synthetic brain images, while whole head magnetic resonance (MR) images can also be useful for many applications. We propose an atlas based patch matching algorithm to synthesize T 2 -w whole head (including brain, skull, eyes etc) images from T 1 -w images for the purpose of distortion correction of diffusion weighted MR images. The geometric distortion in diffusion MR images due to in-homogeneous B 0 magnetic field are often corrected by non-linearly registering the corresponding b = 0 image with zero diffusion gradient to an undistorted T 2 -w image. We show that our synthetic T 2 -w images can be used as a template in absence of a real T 2 -w image. Our patch based method requires multiple atlases with T 1 and T 2 to be registeLowRes to a given target T 1 . Then for every patch on the target, multiple similar looking matching patches are found on the atlas T 1 images and corresponding patches on the atlas T 2 images are combined to generate a synthetic T 2 of the target. We experimented on image data obtained from 44 patients with traumatic brain injury (TBI), and showed that our synthesized T 2 images produce more accurate distortion correction than a state-of-the-art registration based image synthesis method.

Whole brain segmentation and cortical surface reconstruction are two essential techniques for investigating the human brain. Spatial inconsistences, which can hinder further integrated analyses of brain structure, can result due to these two tasks typically being conducted independently of each other. FreeSurfer obtains self-consistent whole brain segmentations and cortical surfaces. It starts with subcortical segmentation, then carries out cortical surface reconstruction, and ends with cortical segmentation and labeling. However, this "segmentation to surface to parcellation" strategy has shown limitations in various cohorts such as older populations with large ventricles. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. A modification called MaCRUISE(+) is designed to perform well when white matter lesions are present. Comparing to the benchmarks CRUISE and FreeSurfer, the surface accuracy of MaCRUISE and MaCRUISE(+) is validated using two independent datasets with expertly placed cortical landmarks. A third independent dataset with expertly delineated volumetric labels is employed to compare segmentation performance. Finally, 200MR volumetric images from an older adult sample are used to assess the robustness of MaCRUISE and FreeSurfer. The advantages of MaCRUISE are: (1) MaCRUISE constructs self-consistent voxelwise segmentations and cortical surfaces, while MaCRUISE(+) is robust to white matter pathology. (2) MaCRUISE achieves more accurate whole brain segmentations than independently conducting the multi-atlas segmentation. (3) MaCRUISE is comparable in accuracy to FreeSurfer (when FreeSurfer does not exhibit global failures) while achieving greater robustness across an older adult population. MaCRUISE has been made freely available in open source., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Objective: Increased numbers of tumor-infiltrating lymphocytes (TILs) in high-grade serous ovarian cancer (HGSC) are associated with improved clinical outcome. Intraepithelial localization of TILs might be regulated by specific homing receptors, such as CD103, which is widely expressed by intraepithelial lymphocytes. Given the emerging role of CD103 TILs, we aimed to assess their contribution to the prognostic value of immunoscoring in HGSC., Methods: The density of intratumoral CD3 and CD103 lymphocytes was examined by immunohistochemistry on a tissue microarray of a series of 135 patients with advanced HGSC and correlated with CD4, CD8, CD56, FoxP3, and TCRγ T-cell counts, as well as E-cadherin staining and conventional prognostic parameters and clinical outcome., Results: Both the presence of CD103 cells, as well as high numbers of intraepithelial CD3 lymphocytes (CD3E), showed a significant correlation with overall survival, in the complete series, as well as in patients with optimal debulking and/or platinum sensitivity. Combining CD3 and CD103 counts improved prognostication and identified 3 major subgroups with respect to overall survival. The most pronounced effect was demonstrated for patients with optimally resected and platinum-sensitive tumors. Patients with CD3/CD103 tumors showed a 5-year survival rate at 90%, CD3/CD103 at 63%, and CD3/CD103 at 0% (P < 0.001)., Conclusions: These results suggest that combined assessment of CD103 and CD3 counts improves the prognostic value of TIL counts in HGSC and might identify patients with early relapse or long-term survival based on the type and extent of the immune response.

Nonsteroidal anti-inf lammatory drugs (NSAIDs) are widely prescribed for the treatment of inflammatory diseases, but their use is frequently related to hypersensitivity reactions. This review outlines our current knowledge of NSAID hypersensitivity (NHS) with regard to its pathogenic, molecular, and genetic mechanisms, as well as diagnosis and treatment. The presentation of NHS varies from a local (skin and/or airways) reaction to systemic reactions, including anaphylaxis. At the molecular level, NHS reactions can be classified as cross-reactive (mediated by cyclooxygenase inhibition) or selective (specific activation of immunoglobulin E antibodies or T cells). Genetic polymorphisms and epigenetic factors have been shown to be closely associated with NHS, and may be useful as predictive markers. To diagnose NHS, inhalation or oral challenge tests are applied, with the exclusion of any cross-reactive NSAIDs. For patients diagnosed with NHS, absolute avoidance of NSAIDs/aspirin is essential, and pharmacological treatment, including biologics, is often used to control their respiratory and cutaneous symptoms. Finally, desensitization is recommended only for selected patients with NHS. However, further research is required to develop new diagnostic methods and more effective treatments against NHS.

Empirical studies of taste function in multiple sclerosis (MS) are rare. Moreover, a detailed assessment of whether quantitative measures of taste function correlate with the punctate and patchy myelin-related lesions found throughout the CNS of MS patients has not been made. We administered a 96-trial test of sweet (sucrose), sour (citric acid), bitter (caffeine) and salty (NaCl) taste perception to the left and right anterior (CN VII) and posterior (CN IX) tongue regions of 73 MS patients and 73 matched controls. The number and volume of lesions were assessed using quantitative MRI in 52 brain regions of 63 of the MS patients. Taste identification scores were significantly lower in the MS patients for sucrose (p = 0.0002), citric acid (p = 0.0001), caffeine (p = 0.0372) and NaCl (p = 0.0004) and were present in both anterior and posterior tongue regions. The percent of MS patients with identification scores falling below the 5th percentile of controls was 15.07 % for caffeine, 21.9 % for citric acid, 24.66 % for sucrose, and 31.50 % for NaCl. Such scores were inversely correlated with lesion volumes in the temporal, medial frontal, and superior frontal lobes, and with the number of lesions in the left and right superior frontal lobes, right anterior cingulate gyrus, and left parietal operculum. Regardless of the subject group, women outperformed men on the taste measures. These findings indicate that a sizable number of MS patients exhibit taste deficits that are associated with MS-related lesions throughout the brain.

Normal pressure hydrocephalus (NPH) affects older adults and is thought to be caused by obstruction of the normal flow of cerebrospinal fluid (CSF). NPH typically presents with cognitive impairment, gait dysfunction, and urinary incontinence, and may account for more than five percent of all cases of dementia. Unlike most other causes of dementia, NPH can potentially be treated and the neurological dysfunction reversed by shunt surgery or endoscopic third ventriculostomy (ETV), which drain excess CSF. However, a major diagnostic challenge remains to robustly identify shunt-responsive NPH patients from patients with enlarged ventricles due to other neurodegenerative diseases. Currently, radiologists grade the severity of NPH by detailed examination and measurement of the ventricles based on stacks of 2D magnetic resonance images (MRIs). Here we propose a new method to automatically segment and label different compartments of the ventricles in NPH patients from MRIs. While this task has been achieved in healthy subjects, the ventricles in NPH are both enlarged and deformed, causing current algorithms to fail. Here we combine a patch-based tissue classification method with a registration-based multi-atlas labeling method to generate a novel algorithm that labels the lateral, third, and fourth ventricles in subjects with ventriculomegaly. The method is also applicable to other neurodegenerative diseases such as Alzheimer's disease; a condition considered in the differential diagnosis of NPH. Comparison with state of the art segmentation techniques demonstrate substantial improvements in labeling the enlarged ventricles, indicating that this strategy may be a viable option for the diagnosis and characterization of NPH.

Background/aims: Role of autophagy in neutrophil function and the association of autophagy and autophagy related (ATG) gene polymorphisms with asthma susceptibility were suggested. In this study, we investigated the genetic association of ATG5 and ATG7 polymorphisms with asthma risk, severity and neutrophilic airway inflammation., Methods: We recruited 408 asthma patients and 201 healthy controls. Sputum neutrophil counts were determined by H&E staining. Serum interleukin 8 (IL-8) levels were measured by enzyme-linked immunosorbent assay (ELISA). Genetic polymorphisms of ATG5 (-769T>C, -335G>A, and 8830C>T) and ATG7 (-100A>G and 25108G>C) were genotyped. The functional activities of ATG5 -769T>C and -335G>A variants were investigated by luciferase reporter assays., Results: No associations of ATG5 and ATG7 polymorphisms with asthma susceptibility and severity were found. ATG5 -769T>C and -335G>A were in complete linkage disequilibrium. In the asthma group, GA/AA genotypes at ATG5 -335G>A were associated with higher neutrophil counts in sputum (p < 0.05); CC/TT genotype at ATG5 8830C>T associated with lower FEV1% predicted value (p < 0.05). DNA fragments containing ATG5 -769T and -335G alleles had higher promoter activities compared to those with -769C and -335A in both human airway epithelial cells (A549, p < 0.01) and human mast cell (HMC-1, p < 0.001). GG and CC genotype at ATG7 -100A>G and 25108G>C were significantly associated with high serum levels of IL-8 (p < 0.05 for both variants)., Conclusions: Genetic polymorphisms of ATG5 and ATG7 could contribute to neutrophilic airway inflammation in the pathogenesis of adult asthma.

Whole brain segmentation (with comprehensive cortical and subcortical labels) and cortical surface reconstruction are two essential techniques for investigating the human brain. The two tasks are typically conducted independently, however, which leads to spatial inconsistencies and hinders further integrated cortical analyses. To obtain self-consistent whole brain segmentations and surfaces, FreeSurfer segregates the subcortical and cortical segmentations before and after the cortical surface reconstruction. However, this "segmentation to surface to parcellation" strategy has shown limitations in various situations. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. To our knowledge, this is the first work that achieves the reliability of state-of-the-art multi-atlas segmentation and labeling methods together with accurate and consistent cortical surface reconstruction. Compared with previous methods, MaCRUISE has three features: (1) MaCRUISE obtains 132 cortical/subcortical labels simultaneously from a single multi-atlas segmentation before reconstructing volume consistent surfaces; (2) Fuzzy tissue memberships are combined with multi-atlas segmentations to address partial volume effects; (3) MaCRUISE reconstructs topologically consistent cortical surfaces by using the sulci locations from multi-atlas segmentation. Two data sets, one consisting of five subjects with expertly traced landmarks and the other consisting of 100 volumes from elderly subjects are used for validation. Compared with CRUISE, MaCRUISE achieves self-consistent whole brain segmentation and cortical reconstruction without compromising on surface accuracy. MaCRUISE is comparably accurate to FreeSurfer while achieving greater robustness across an elderly population.

Longitudinal analysis of magnetic resonance images of the human brain provides knowledge of brain changes during both normal aging as well as the progression of many diseases. Previous longitudinal segmentation methods have either ignored temporal information or have incorporated temporal consistency constraints within the algorithm. In this work, we assume that some anatomical brain changes can be explained by temporal transitions in image intensities. Once the images are aligned in the same space, the intensities of each scan at the same voxel constitute a temporal (or 4D) intensity trend at that voxel. Temporal intensity variations due to noise or other artifacts are corrected by a 4D intensity-based filter that smooths the intensity values where appropriate, while preserving real anatomical changes such as atrophy. Here smoothing refers to removal of sudden changes or discontinuities in intensities. Images processed with the 4D filter can be used as a pre-processing step to any segmentation method. We show that such a longitudinal pre-processing step produces robust and consistent longitudinal segmentation results, even when applying 3D segmentation algorithms. We compare with state-of-the-art 4D segmentation algorithms. Specifically, we experimented on three longitudinal datasets containing 4-12 time-points, and showed that the 4D temporal filter is more robust and has more power in distinguishing between healthy subjects and those with dementia, mild cognitive impairment, as well as different phenotypes of multiple sclerosis.

Background: Increasing evidence suggests that adipokines affect immune responses and chronic urticaria (CU) is associated with an altered immune response related to chronic systemic inflammation. Our objectives were to investigate whether adipokines are involved in CU pathogenesis and to outline relationships between adipokines and urticaria severity and quality of life., Methods: Serum adiponectin, leptin, lipocalin-2 (LCN2), interleukin (IL)-10, IL-6, and tumor necrosis factor (TNF)-α concentrations were measured by enzyme-linked immunosorbent assays in 191 CU patients and 89 healthy controls. The effect of LCN2 on N-formyl-methionine-leucine-phenylalanine (fMLP)-induced neutrophil chemotaxis was assessed using migration assays. CU severity was assessed based on the urticaria activity score (UAS). To explore relationships between adipokines and UAS and the chronic urticaria-specific quality of life (CU-QoL) questionnaire, a structural equation model was used., Results: Mean levels of serum LCN2, TNF-α, IL-6, and IL-10 were significantly higher in CU patients than in controls. Adiponectin levels were significantly lower in patients with CU than in controls. While serum IL-6 levels were significantly higher in refractory CU patients, compared to responsive CU individuals, LCN2 levels were significantly lower. LCN2 inhibited fMLP-induced neutrophil migration. LCN2 showed a direct relationship with UAS (β = -0.274, p < 0.001), and UAS was found to contribute to CU-QoL (β = 0.417, p < 0.001)., Conclusions: Our results highlighted an imbalance in pro- and anti-inflammatory adipokines in CU patients. We suggest that LCN2 could be a differential marker for disease activity and the clinical responses to antihistamine treatment in CU patients. Modulation of systemic inflammation may be a therapeutic strategy for treating severe, refractory CU., (© 2016 S. Karger AG, Basel.)

Background: Autophagy and genetic predisposition have been suggested to potentially play roles in the development of asthma. However, little is known about the role of autophagy in the pathogenesis of severe asthma., Objective: We compared autophagy in the sputum granulocytes, peripheral blood cells (PBCs) and peripheral blood eosinophils (PBEs) between patients with severe asthma and those with non-severe asthma and investigated the functional effects of autophagy., Methods: We enrolled 36 patients with severe asthma, 14 with non-severe asthma and 23 normal healthy controls in this study. Sputum granulocytes, PBCs and PBEs were isolated from each subject. Autophagy was evaluated based on the expression of microtubule-associated protein light chain 3 (LC3) by Western blot, confocal microscopy, transmission electron microscopy and flow cytometry. IL-8 levels were measured by ELISA. To induce autophagy, HL-60 cells, human primary small airway epithelial cells (SAECs) and A549 cells were treated with IL-5, IL-1β and TNF-α. To inhibit autophagy, PI3K inhibitors (LY29400 and 3-methyladenine [3-MA]) and hydroxychloroquine (HCQ) were used. Knockdown of ATG5 and Beclin-1 was performed in A549 cells, and the therapeutic effects of dexamethasone were evaluated., Results: Higher autophagy levels were noted in sputum granulocytes, PBCs and PBEs from patients with severe asthma than from patients with non-severe asthma and healthy controls (P < 0.05 for all). IL-5 increased autophagy levels in both PBCs and PBEs (P < 0.05). 3-MA attenuated the increased expression of LC3-II and eosinophil cationic protein in HL-60 cells induced by IL-5 (P = 0.034 for both). Dexamethasone did not affect autophagy levels in PBEs. IL-1β increased LC3-II expression and IL-8 production (P < 0.01) in SAECs, and this was attenuated by LY294002, 3-MA, HCQ and knockdown of ATG5 and Beclin-1 (in A549 cells) (P < 0.01)., Conclusions and Clinical Relevance: Autophagy could play a role in the pathogenesis of severe asthma. Autophagy modulation may be a novel therapeutic target for conventional therapy-resistant severe asthma., (© 2015 John Wiley & Sons Ltd.)

Significance: Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging)., Aim: This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs., Approach: Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended., Conclusion: Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems., (© 2024 The Authors.)

Regional analysis of volumes examined in normalized space (RAVENS) are transformation images used in the study of brain morphometry. In this paper, RAVENS images are analyzed using a longitudinal variant of voxel-based morphometry (VBM) and longitudinal functional principal component analysis (LFPCA) for high-dimensional images. We demonstrate that the latter overcomes the limitations of standard longitudinal VBM analyses, which does not separate registration errors from other longitudinal changes and baseline patterns. This is especially important in contexts where longitudinal changes are only a small fraction of the overall observed variability, which is typical in normal aging and many chronic diseases. Our simulation study shows that LFPCA effectively separates registration error from baseline and longitudinal signals of interest by decomposing RAVENS images measured at multiple visits into three components: a subject-specific imaging random intercept that quantifies the cross-sectional variability, a subject-specific imaging slope that quantifies the irreversible changes over multiple visits, and a subject-visit specific imaging deviation. We describe strategies to identify baseline/longitudinal variation and registration errors combined with covariates of interest. Our analysis suggests that specific regional brain atrophy and ventricular enlargement are associated with multiple sclerosis (MS) disease progression.

Segmenting T2-hyperintense white matter lesions from longitudinal MR images is essential in understanding progression of multiple sclerosis. Most lesion segmentation techniques find lesions independently at each time point, even though there are different noise and image contrast variations at each point in the time series. In this paper, we present a patch based 4D lesion segmentation method that takes advantage of the temporal component of longitudinal data. For each subject with multiple time-points, 4D patches are constructed from the T 1 -w and FLAIR scans of all time-points. For every 4D patch from a subject, a few relevant matching 4D patches are found from a reference, such that their convex combination reconstructs the subject's 4D patch. Then corresponding manual segmentation patches of the reference are combined in a similar manner to generate a 4D membership of lesions of the subject patch. We compare our 4D patch-based segmentation with independent 3D voxel-based and patch-based lesion segmentation algorithms. Based on ground truth segmentations from 30 data sets, we show that the mean Dice coefficients between manual and automated segmentations improve after using the 4D approach compared to two state-of-the-art 3D segmentation algorithms.

The genetic and phenotypic heterogeneity of autism spectrum disorders (ASD) presents a substantial challenge for diagnosis, classification, research, and treatment. Investigations into the underlying molecular etiology of ASD have often yielded mixed and at times opposing findings. Defining the molecular and biochemical underpinnings of heterogeneity in ASD is crucial to our understanding of the pathophysiological development of the disorder, and has the potential to assist in diagnosis and the rational design of clinical trials. In this review, we propose that genetically diverse forms of ASD may be usefully parsed into entities resulting from converse patterns of growth regulation at the molecular level, which lead to the correlates of general synaptic and neural overgrowth or undergrowth. Abnormal brain growth during development is a characteristic feature that has been observed both in children with autism and in mouse models of autism. We review evidence from syndromic and non-syndromic ASD to suggest that entities currently classified as autism may fundamentally differ by underlying pro- or anti-growth abnormalities in key biochemical pathways, giving rise to either excessive or reduced synaptic connectivity in affected brain regions. We posit that this classification strategy has the potential not only to aid research efforts, but also to ultimately facilitate early diagnosis and direct appropriate therapeutic interventions.

Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject-specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole-brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available state-of-the art approaches.

Automatic processing of magnetic resonance images is a vital part of neuroscience research. Yet even the best and most widely used medical image processing methods will not produce consistent results when their input images are acquired with different pulse sequences. Although intensity standardization and image synthesis methods have been introduced to address this problem, their performance remains dependent on knowledge and consistency of the pulse sequences used to acquire the images. In this paper, an image synthesis approach that first estimates the pulse sequence parameters of the subject image is presented. The estimated parameters are then used with a collection of atlas or training images to generate a new atlas image having the same contrast as the subject image. This additional image provides an ideal source from which to synthesize any other target pulse sequence image contained in the atlas. In particular, a nonlinear regression intensity mapping is trained from the new atlas image to the target atlas image and then applied to the subject image to yield the particular target pulse sequence within the atlas. Both intensity standardization and synthesis of missing tissue contrasts can be achieved using this framework. The approach was evaluated on both simulated and real data, and shown to be superior in both intensity standardization and synthesis to other established methods., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Background: To report that artifactual microhemorrhages are introduced by the two-dimensional (2D) homodyne filtering method of generating susceptibility weighted images (SWI) when open-ended fringelines (OEF) are present in phase data., Methods: SWI data from 28 traumatic brain injury (TBI) patients was obtained on a 3 tesla clinical Siemens scanner using both the product 3D gradient echo sequence (GRE) with generalized autocalibrating partially parallel acquisition acceleration and an in-house developed segmented echo planar imaging (sEPI) sequence without GRAPPA acceleration. SWI processing included (i) 2D homodyne method implemented on the scanner console and (ii) a 3D Fourier-based phase unwrapping followed by 3D high pass filtering. Original and enhanced magnitude and phase images were carefully reviewed for sites of type III OEFs and microhemorrhages by a neuroradiologist on a PACS workstation., Results: Nineteen of 28 (68%) phase datasets acquired using GRAPPA-accelerated GRE acquisition demonstrated type III OEFs. In SWI images, artifactual microhemorrhages were found on 17 of 19 (89%) cases generated using 2D homodyne processing. Application of a 3D Fourier-based unwrapping method prior HP filtering minimized the appearance of the phase singularities in the enhanced phase, and did not generate microhemorrhage-like artifacts in magnitude images., Conclusion: The 2D homodyne filtering method may introduce artifacts mimicking intracranial microhemorrhages in SWI images when type III OEFs are present in phase images. Such artifacts could lead to overestimation of pathology, e.g., TBI. This work demonstrates that 3D phase unwrapping methods minimize this artifact. However, methods to properly combine phase across coils are needed to eliminate this artifact., (© 2014 Wiley Periodicals, Inc.)

Independent component analysis (ICA) is a popular blind source separation technique used in many scientific disciplines. Current ICA approaches have focused on developing efficient algorithms under specific ICA models, such as instantaneous or convolutive mixing conditions, intrinsically assuming temporal independence or autocorrelation of the sources. In practice, the true model is not known and different ICA algorithms can produce very different results. Although it is critical to choose an ICA model, there has not been enough research done on evaluating mixing models and assumptions, and how the associated algorithms may perform under different scenarios. In this paper, we investigate the performance of multiple ICA algorithms under various mixing conditions. We also propose a convolutive ICA algorithm for echoic mixing cases. Our simulation studies show that the performance of ICA algorithms is highly dependent on mixing conditions and temporal independence of the sources. Most instantaneous ICA algorithms fail to separate autocorrelated sources, while convolutive ICA algorithms depend highly on the model specification and approximation accuracy of unmixing filters.

The simulation of magnetic resonance (MR) images plays an important role in the validation of image analysis algorithms such as image segmentation, due to lack of sufficient ground truth in real MR images. Previous work on MRI simulation has focused on explicitly modeling the MR image formation process. However, because of the overwhelming complexity of MR acquisition these simulations must involve simplifications and approximations that can result in visually unrealistic simulated images. In this work, we describe an example-based simulation framework, which uses an "atlas" consisting of an MR image and its anatomical models derived from the hard segmentation. The relationships between the MR image intensities and its anatomical models are learned using a patch-based regression that implicitly models the physics of the MR image formation. Given the anatomical models of a new brain, a new MR image can be simulated using the learned regression. This approach has been extended to also simulate intensity inhomogeneity artifacts based on the statistical model of training data. Results show that the example based MRI simulation method is capable of simulating different image contrasts and is robust to different choices of atlas. The simulated images resemble real MR images more than simulations produced by a physics-based model.

Multiple Sclerosis (MS) is a disease of the central nervous system in which the protective myelin sheath of the neurons is damaged. MS leads to the formation of lesions, predominantly in the white matter of the brain and the spinal cord. The number and volume of lesions visible in magnetic resonance (MR) imaging (MRI) are important criteria for diagnosing and tracking the progression of MS. Locating and delineating lesions manually requires the tedious and expensive efforts of highly trained raters. In this paper, we propose an automated algorithm to segment lesions in MR images using multi-output decision trees. We evaluated our algorithm on the publicly available MICCAI 2008 MS Lesion Segmentation Challenge training dataset of 20 subjects, and showed improved results in comparison to state-of-the-art methods. We also evaluated our algorithm on an in-house dataset of 49 subjects with a true positive rate of 0.41 and a positive predictive value 0.36.

Background: S100A8 and S100A9 can be produced by lipopolysaccharide-stimulated granulocytes and provoke an innate immune-mediated airway inflammation. Involvement of S100A8 and S100A9 has been implicated in asthma. To further understand the role of S100A8 and S100A9 during innate immune responses in baker's asthma, we investigated the associations of serum S100A8 and S100A9 with exposure to bakery allergens and polymorphisms of the Toll-like receptor 4 (TLR4) gene., Methods: Totally, 381 bakery workers and 100 unexposed healthy controls were recruited. Skin prick tests for bakery allergens were performed. Serum levels of S100A8, S100A9, myeloperoxidase (MPO), tumor necrosis factor (TNF)-α, and interleukin (IL)-8 were measured using ELISA. Predictive values of serum S100A8 and S100A9 in bakery workers were evaluated by receiver-operating characteristic (ROC) curves. Polymorphisms of TLR4 -2027Ax2192;G and -1608Tx2192;C were genotyped., Results: Higher serum levels of S100A8 and S100A9 were noted in bakery workers compared to the normal controls (p < 0.001); however, no significant differences were noted according to work-related symptoms. The area under the ROC curve of serum S100A8 was 0.886 for occupational exposure (p < 0.001). The TLR4 -1608CC genotype was significantly associated with a higher serum S100A8 level (p = 0.025). Serum S100A8 and S100A9 levels were correlated with serum levels of MPO (r = 0.396 and 0.189, respectively), TNF-α (r = 0.536 and 0.280, respectively), and IL-8 (r = 0.540 and 0.205, respectively; p < 0.001 for all)., Conclusion: S100A8 and S100A9 are involved in innate immune responses under the regulation of TLR4 polymorphisms in baker's asthma pathogenesis. Serum S100A8 could be a potential biomarker for predicting occupational exposure to wheat flour in bakery workers., (© 2016 S. Karger AG, Basel.)

Magnetic resonance imaging (MRI) is the dominant modality for neuroimaging in clinical and research domains. The tremendous versatility of MRI as a modality can lead to large variability in terms of image contrast, resolution, noise, and artifacts. Variability can also manifest itself as missing or corrupt imaging data. Image synthesis has been recently proposed to homogenize and/or enhance the quality of existing imaging data in order to make them more suitable as consistent inputs for processing. We frame the image synthesis problem as an inference problem on a 3-D continuous-valued conditional random field (CRF). We model the conditional distribution as a Gaussian by defining quadratic association and interaction potentials encoded in leaves of a regression tree. The parameters of these quadratic potentials are learned by maximizing the pseudo-likelihood of the training data. Final synthesis is done by inference on this model. We applied this method to synthesize T2-weighted images from T1-weighted images, showing improved synthesis quality as compared to current image synthesis approaches. We also synthesized Fluid Attenuated Inversion Recovery (FLAIR) images, showing similar segmentations to those obtained from real FLAIRs. Additionally, we generated super-resolution FLAIRs showing improved segmentation.

Purpose: To evaluate different susceptibility-weighted imaging (SWI) phase processing methods and parameter selection, thereby improving understanding of potential artifacts, as well as facilitating choice of methodology in clinical settings., Materials and Methods: Two major phase processing methods, homodyne-filtering and phase unwrapping-high pass (HP) filtering, were investigated with various phase unwrapping approaches, filter sizes, and filter types. Magnitude and phase images were acquired from a healthy subject and brain injury patients on a 3T clinical Siemens MRI system. The results were evaluated based on image contrast-to-noise ratio and presence of processing artifacts., Results: When using a relatively small filter size (32 pixels for the matrix size 512 × 512 pixels), all homodyne-filtering methods were subject to phase errors leading to 2% to 3% masked brain area in lower and middle axial slices. All phase unwrapping-filtering/smoothing approaches demonstrated fewer phase errors and artifacts compared to the homodyne-filtering approaches. For performing phase unwrapping, Fourier-based methods, although less accurate, were 2-4 orders of magnitude faster than the PRELUDE, Goldstein, and Quality-guide methods., Conclusion: Although homodyne-filtering approaches are faster and more straightforward, phase unwrapping followed by HP filtering approaches perform more accurately in a wider variety of acquisition scenarios., (© 2013 Wiley Periodicals, Inc.)

Unlabelled: Integrated PET/MR systems are becoming increasingly popular in clinical and research applications. Quantitative PET reconstruction requires correction for γ-photon attenuations using an attenuation coefficient map (μ map) that is a measure of the electron density. One challenge of PET/MR, in contrast to PET/CT, lies in the accurate computation of μ maps. Unlike CT, MR imaging measures physical properties not directly related to electron density. Previous approaches have computed the attenuation coefficients using a segmentation of MR images or using deformable registration of atlas CT images to the space of the subject MR images., Methods: In this work, we propose a patch-based method to generate whole-head μ maps from ultrashort echo-time (UTE) MR imaging sequences. UTE images are preferred to other MR sequences because of the increased signal from bone. To generate a synthetic CT image, we use patches from a reference dataset, which consists of dual-echo UTE images and a coregistered CT scan from the same subject. Matching of patches between the reference and target images allows corresponding patches from the reference CT scan to be combined via a Bayesian framework. No registration or segmentation is required., Results: For evaluation, UTE, CT, and PET data acquired from 5 patients under an institutional review board-approved protocol were used. Another patient (with UTE and CT data only) was selected to be the reference to generate synthetic CT images for these 5 patients. PET reconstructions were attenuation-corrected using the original CT, our synthetic CT, Siemens Dixon-based μ maps, Siemens UTE-based μ maps, and deformable registration-based CT. Our synthetic CT-based PET reconstruction showed higher correlation (average ρ = 0.996, R(2) = 0.991) to the original CT-based PET, as compared with the segmentation- and registration-based methods. Synthetic CT-based reconstruction had minimal bias (regression slope, 0.990), as compared with the segmentation-based methods (regression slope, 0.905). A peak signal-to-noise ratio of 35.98 dB in the reconstructed PET activity was observed, compared with 29.767, 29.34, and 27.43 dB for the Siemens Dixon-, UTE-, and registration-based μ maps., Conclusion: A patch-matching approach to synthesize CT images from dual-echo UTE images leads to significantly improved accuracy of PET reconstruction as compared with actual CT scans. The PET reconstruction is improved over segmentation- (Dixon and Siemens UTE) and registration-based methods, even in subjects with pathologic findings., (© 2014 by the Society of Nuclear Medicine and Molecular Imaging, Inc.)

In vivo measurements of human brain deformation during mild acceleration are needed to help validate computational models of traumatic brain injury and to understand the factors that govern the mechanical response of the brain. Tagged magnetic resonance imaging is a powerful, noninvasive technique to track tissue motion in vivo which has been used to quantify brain deformation in live human subjects. However, these prior studies required from 72 to 144 head rotations to generate deformation data for a single image slice, precluding its use to investigate the entire brain in a single subject. Here, a novel method is introduced that significantly reduces temporal variability in the acquisition and improves the accuracy of displacement estimates. Optimization of the acquisition parameters in a gelatin phantom and three human subjects leads to a reduction in the number of rotations from 72 to 144 to as few as 8 for a single image slice. The ability to estimate accurate, well-resolved, fields of displacement and strain in far fewer repetitions will enable comprehensive studies of acceleration-induced deformation throughout the human brain in vivo., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Background: Susceptibility-based MRI offers a unique opportunity to study neurological diseases such as multiple sclerosis (MS). In this work, we assessed a three-dimensional segmented echo-planar-imaging (3D-EPI) sequence to rapidly acquire high-resolution T2 -weighted and phase contrast images of the whole brain. We also assessed if these images could depict important features of MS at clinical field strength, and we tested the effect of a gadolinium-based contrast agent (GBCA) on these images., Materials and Methods: The 3D-EPI acquisition was performed on four healthy volunteers and 15 MS cases on a 3T scanner. The 3D sagittal images of the whole brain were acquired with a voxel size of 0.55 × 0.55 × 0.55 mm(3) in less than 4 minutes. For the MS cases, the 3D-EPI acquisition was performed before, during, and after intravenous GBCA injection., Results: Both T2-weighted and phase-contrast images from the 3D-EPI acquisition were sensitive to the presence of lesions, parenchymal veins, and tissue iron. Conspicuity of the veins was enhanced when images were obtained during injection of GBCA., Conclusions: We propose this rapid imaging sequence for investigating, in a clinical setting, the spatiotemporal relationship between small parenchymal veins, iron deposition, and lesions in MS patient brains., (© The Author(s) 2014.)

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer's disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers.

Mild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) may share common symptom and neuropsychological profiles in military service members (SMs) following deployment; while a connection between the two conditions is plausible, the relationship between them has been difficult to discern. The intent of this report is to enhance our understanding of the relationship between findings on structural and functional brain imaging and symptoms of PTSD. Within a cohort of SMs who did not meet criteria for PTSD but were willing to complete a comprehensive assessment within 2 months of their return from combat deployment, we conducted a nested case-control analysis comparing those with combat-related mTBI to age/gender-matched controls with diffusion tensor imaging, resting state functional magnetic resonance imaging and a range of psychological measures. We report degraded white matter integrity in those with a history of combat mTBI, and a positive correlation between the white matter microstructure and default mode network (DMN) connectivity. Higher clinician-administered and self-reported subthreshold PTSD symptoms were reported in those with combat mTBI. Our findings offer a potential mechanism through which mTBI may alter brain function, and in turn, contribute to PTSD symptoms., (Published by Elsevier Ireland Ltd.)

Cortical atrophy has been reported in a number of diseases, such as multiple sclerosis and Alzheimer's disease, that are also associated with white matter (WM) lesions. However, most cortical reconstruction techniques do not account for these pathologies, thereby requiring additional processing to correct for the effect of WM lesions. In this work, we introduce CRUISE(+), an automated process for cortical reconstruction from magnetic resonance brain images with WM lesions. The process extends previously well validated methods to allow for multichannel input images and to accommodate for the presence of WM lesions. We provide new validation data and tools for measuring the accuracy of cortical reconstruction methods on healthy brains as well as brains with multiple sclerosis lesions. Using this data, we validate the accuracy of CRUISE(+) and compare it to another state-of-the-art cortical reconstruction tool. Our results demonstrate that CRUISE(+) has superior performance in the cortical regions near WM lesions, and similar performance in other regions., (Copyright © 2013 Wiley Periodicals, Inc.)

Fluid Attenuated Inversion Recovery (FLAIR) is a commonly acquired pulse sequence for multiple sclerosis (MS) patients. MS white matter lesions appear hyperintense in FLAIR images and have excellent contrast with the surrounding tissue. Hence, FLAIR images are commonly used in automated lesion segmentation algorithms to easily and quickly delineate the lesions. This expedites the lesion load computation and correlation with disease progression. Unfortunately for numerous reasons the acquired FLAIR images can be of a poor quality and suffer from various artifacts. In the most extreme cases the data is absent, which poses a problem when consistently processing a large data set. We propose to fill in this gap by reconstructing a FLAIR image given the corresponding T 1 -weighted, T 2 -weighted, and P D -weighted images of the same subject using random forest regression. We show that the images we produce are similar to true high quality FLAIR images and also provide a good surrogate for tissue segmentation.

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

Spinal cord segmentation is an important step in the analysis of neurological diseases such as multiple sclerosis. Several studies have shown correlations between disease progression and metrics relating to spinal cord atrophy and shape changes. Current practices primarily involve segmenting the spinal cord manually or semi-automatically, which can be inconsistent and time-consuming for large datasets. An automatic method that segments the spinal cord and cerebrospinal fluid from magnetic resonance images is presented. The method uses a deformable atlas and topology constraints to produce results that are robust to noise and artifacts. The method is designed to be easily extended to new data with different modalities, resolutions, and fields of view. Validation was performed on two distinct datasets. The first consists of magnetization transfer-prepared T2*-weighted gradient-echo MRI centered only on the cervical vertebrae (C1-C5). The second consists of T1-weighted MRI that covers both the cervical and portions of the thoracic vertebrae (C1-T4). Results were found to be highly accurate in comparison to manual segmentations. A pilot study was carried out to demonstrate the potential utility of this new method for research and clinical studies of multiple sclerosis., (© 2013 Elsevier Inc. All rights reserved.)

The transcription factor SOX2 has been extensively studied for its role in embryonic stem cell self-renewal and pluripotency. More recent data suggest oncogenic functions of SOX2 and demonstrate expression in several carcinoma types, predominantly of squamous cell origin. The gene SOX2 is located at chromosome 3q26, a region that is frequently amplified in ovarian high-grade serous carcinoma. In this study, we correlate SOX2 protein expression and gene-amplification status in ovarian carcinomas with histopathologic criteria and disease outcome. A total of 215 cases of ovarian carcinomas (154 serous, 39 endometrioid, 11 clear cell, 5 mucinous, and 6 transitional cell carcinomas) were analyzed by immunohistochemistry in a tissue microarray for nuclear expression of SOX2. A total of 60.5% of all carcinomas showed SOX2 expression with no significant difference between the major histologic types. Interestingly, SOX2 expression was predominantly a feature of high-grade tumors (G1: 36.4%, G2: 55.6%, G3: 64.0%, P=0.040). In 21.2% of these cases, fluorescence in situ hybridization analysis detected low-level SOX2 gene amplification which was not significantly associated with SOX2 protein expression. However, survival analysis of Stage II to IV high-grade serous carcinomas revealed a favorable effect of SOX2 expression (median disease-free survival 27 vs. 21 mo, P=0.041; median overall survival 39 vs. 25 mo, P=0.062). Further studies are needed to explore whether expression of SOX2 has a specific role in prognosis and therapy response.