553 research outputs found

    Dynamic low-level context for the detection of mild traumatic brain injury.

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    Mild traumatic brain injury (mTBI) appears as low contrast lesions in magnetic resonance (MR) imaging. Standard automated detection approaches cannot detect the subtle changes caused by the lesions. The use of context has become integral for the detection of low contrast objects in images. Context is any information that can be used for object detection but is not directly due to the physical appearance of an object in an image. In this paper, new low-level static and dynamic context features are proposed and integrated into a discriminative voxel-level classifier to improve the detection of mTBI lesions. Visual features, including multiple texture measures, are used to give an initial estimate of a lesion. From the initial estimate novel proximity and directional distance, contextual features are calculated and used as features for another classifier. This feature takes advantage of spatial information given by the initial lesion estimate using only the visual features. Dynamic context is captured by the proposed posterior marginal edge distance context feature, which measures the distance from a hard estimate of the lesion at a previous time point. The approach is validated on a temporal mTBI rat model dataset and shown to have improved dice score and convergence compared to other state-of-the-art approaches. Analysis of feature importance and versatility of the approach on other datasets are also provided

    Visual and Contextual Modeling for the Detection of Repeated Mild Traumatic Brain Injury.

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    Currently, there is a lack of computational methods for the evaluation of mild traumatic brain injury (mTBI) from magnetic resonance imaging (MRI). Further, the development of automated analyses has been hindered by the subtle nature of mTBI abnormalities, which appear as low contrast MR regions. This paper proposes an approach that is able to detect mTBI lesions by combining both the high-level context and low-level visual information. The contextual model estimates the progression of the disease using subject information, such as the time since injury and the knowledge about the location of mTBI. The visual model utilizes texture features in MRI along with a probabilistic support vector machine to maximize the discrimination in unimodal MR images. These two models are fused to obtain a final estimate of the locations of the mTBI lesion. The models are tested using a novel rodent model of repeated mTBI dataset. The experimental results demonstrate that the fusion of both contextual and visual textural features outperforms other state-of-the-art approaches. Clinically, our approach has the potential to benefit both clinicians by speeding diagnosis and patients by improving clinical care

    Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images.

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    We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects

    Hyperbaric oxygen therapy for traumatic brain injury

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    Traumatic brain injury (TBI) is a major public health issue. The complexity of TBI has precluded the use of effective therapies. Hyperbaric oxygen therapy (HBOT) has been shown to be neuroprotective in multiple neurological disorders, but its efficacy in the management of TBI remains controversial. This review focuses on HBOT applications within the context of experimental and clinical TBI. We also discuss its potential neuroprotective mechanisms. Early or delayed multiple sessions of low atmospheric pressure HBOT can reduce intracranial pressure, improve mortality, as well as promote neurobehavioral recovery. The complimentary, synergistic actions of HBOT include improved tissue oxygenation and cellular metabolism, anti-apoptotic, and anti-inflammatory mechanisms. Thus HBOT may serve as a promising neuroprotective strategy that when combined with other therapeutic targets for TBI patients which could improve long-term outcomes

    Computational analysis reveals increased blood deposition following repeated mild traumatic brain injury.

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    Mild traumatic brain injury (mTBI) has become an increasing public health concern as subsequent injuries can exacerbate existing neuropathology and result in neurological deficits. This study investigated the temporal development of cortical lesions using magnetic resonance imaging (MRI) to assess two mTBIs delivered to opposite cortical hemispheres. The controlled cortical impact model was used to produce an initial mTBI on the right cortex followed by a second injury induced on the left cortex at 3 (rmTBI 3d) or 7 (rmTBI 7d) days later. Histogram analysis was combined with a novel semi-automated computational approach to perform a voxel-wise examination of extravascular blood and edema volumes within the lesion. Examination of lesion volume 1d post last injury revealed increased tissue abnormalities within rmTBI 7d animals compared to other groups, particularly at the site of the second impact. Histogram analysis of lesion T2 values suggested increased edematous tissue within the rmTBI 3d group and elevated blood deposition in the rm TBI 7d animals. Further quantification of lesion composition for blood and edema containing voxels supported our histogram findings, with increased edema at the site of second impact in rmTBI 3d animals and elevated blood deposition in the rmTBI 7d group at the site of the first injury. Histological measurements revealed spatial overlap of regions containing blood deposition and microglial activation within the cortices of all animals. In conclusion, our findings suggest that there is a window of tissue vulnerability where a second distant mTBI, induced 7d after an initial injury, exacerbates tissue abnormalities consistent with hemorrhagic progression

    Astrocytic Ephrin-B1 Regulates Synapse Remodeling Following Traumatic Brain Injury.

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    Traumatic brain injury (TBI) can result in tissue alterations distant from the site of the initial injury, which can trigger pathological changes within hippocampal circuits and are thought to contribute to long-term cognitive and neuropsychological impairments. However, our understanding of secondary injury mechanisms is limited. Astrocytes play an important role in brain repair after injury and astrocyte-mediated mechanisms that are implicated in synapse development are likely important in injury-induced synapse remodeling. Our studies suggest a new role of ephrin-B1, which is known to regulate synapse development in neurons, in astrocyte-mediated synapse remodeling following TBI. Indeed, we observed a transient upregulation of ephrin-B1 immunoreactivity in hippocampal astrocytes following moderate controlled cortical impact model of TBI. The upregulation of ephrin-B1 levels in hippocampal astrocytes coincided with a decline in the number of vGlut1-positive glutamatergic input to CA1 neurons at 3 days post injury even in the absence of hippocampal neuron loss. In contrast, tamoxifen-induced ablation of ephrin-B1 from adult astrocytes in ephrin-B1(loxP/y)ERT2-Cre(GFAP) mice accelerated the recovery of vGlut1-positive glutamatergic input to CA1 neurons after TBI. Finally, our studies suggest that astrocytic ephrin-B1 may play an active role in injury-induced synapse remodeling through the activation of STAT3-mediated signaling in astrocytes. TBI-induced upregulation of STAT3 phosphorylation within the hippocampus was suppressed by astrocyte-specific ablation of ephrin-B1 in vivo, whereas the activation of ephrin-B1 in astrocytes triggered an increase in STAT3 phosphorylation in vitro. Thus, regulation of ephrin-B1 signaling in astrocytes may provide new therapeutic opportunities to aid functional recovery after TBI

    Posterior archaeomagnetic dating: An example from the Early Medieval site Thunau am Kamp, Austria

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    International audienceThe Early Medieval valley settlement of Thunau am Kamp in Lower Austria has been under archaeological excavation for 10 years. The site was occupied during the 9th and 10th centuries AD according to potsherds, which seem to indicate two phases of activity: in the older phase ovens were placed in the corners of houses while during the younger phase they are found in the middle of the wall. The present study has been conducted in order to increase the archaeomagnetic database and fill the temporal gap around 900 AD. For this purpose 14 ovens have been sampled for their paleaomagnetic signals. Laboratory treatment generally confirmed that the baked clay has preserved stable directions. Apart from one exception, all the mean characteristic remanent magnetisation directions are concentrated on the Early Medieval part of the directional archaeomagnetic reference curve of Austria at about 900 AD. Using this curve archaeomagnetic dating provides ages between 800 and 1100 AD, which are in agreement with the archaeological dating. Together with the archaeological age estimates and stratigraphic information the new data have been included into the database of the Austrian curve and it has been recalculated using a new version of RenCurve. The new data confine the curve and its error band considerably in the time interval 800 to 1100 AD. This calibration process also provides probability density distributions for each included structure, which allows for posterior dating and refines temporal errors considerably. Because such dating includes archaeological information it is not an independent age estimate but is a combination of all available dating method

    A Model for the Development of Sustainable Innovations for the Early Phase of the Innovation Process

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    Current industrial development is faced by the global challenge to meet the continuously growing demand for capital and consumer goods in emerging countries while simultaneously ensuring a sustainable industrial growth in the social, environmental and economic dimension. By means of market dynamics of cooperation and competition in global value creation and knowledge networks, innovations geared towards sustainability can be essential drivers for realizing a sustainable development. The targeted development of new sustainable innovations is consequently a key activity in order to move towards sustainable industrial growth. This paper will describe a model for the development of sustainable innovations. The model focuses on idea generation in the early phase of the innovations process, addressing the fuzzy front end of innovation. In this context, specific goals and principles of sustainable development are integrated into a problem-solving approach. This integrated approach is subsequently used as a foundation for the targeted development of sustainable innovations in the frame of a workshop concept
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