Structural and functional neuroimaging in mild-to-moderate head injury. Lancet Neurol 6:699-710

Department of Neurology, University Medical Center Groningen, Netherlands.
The Lancet Neurology (Impact Factor: 21.9). 09/2007; 6(8):699-710. DOI: 10.1016/S1474-4422(07)70191-6
Source: PubMed


Head injury is a major cause of disability and death in adults. Significant developments in imaging techniques have contributed to the knowledge of the pathophysiology of head injury. Although extensive research is available on severe head injury, less is known about mild-to-moderate head injury despite the fact that most patients sustain this type of injury. In this review, we focus on structural and functional imaging techniques in patients with mild-to-moderate head injury. We discuss CT and MRI, including different MRI sequences, single photon emission computed tomography, perfusion-weighted MRI, perfusion CT, PET, magnetic resonance spectroscopy, functional MRI and magnetic encephalography. We outline the advantages and limitations of these various techniques in the contexts of the initial assessment and identification of brain abnormalities and the prediction of outcome.

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    • "The primary focus of imaging is only to assess for the presence of a hematoma [1]. Various clinical studies have used manual quantitative analysis to evaluate TBI in MRI [2] [3] [4]. These studies identified the size and location of the lesions and linked them to long term neurological effects. "
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    ABSTRACT: 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.
    IEEE transactions on bio-medical engineering 07/2014; 62(1). DOI:10.1109/TBME.2014.2342653 · 2.35 Impact Factor
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    • "Firstly, movement in the MR scanner can be an important limitation because TBI patients are often agitated or confused in the acute phase of TBI. This can interfere with image acquisition and with the investigation of symptoms (Metting et al., 2007), and can also result in systematic errors related to connectivity calculations (Van Dijk et al., 2012). For this reason, recently introduced methods which address this limitation (such as PROspective MOtion Correction, PROMO, Brown et al., 2010) are likely to find wide implementation in the TBI neuroimaging field, and the application of both existing and novel motion correction algorithms may also greatly benefit the field of TBI neuroimaging in general. "
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    ABSTRACT: Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.
    Clinical neuroimaging 08/2012; 1(1):1-17. DOI:10.1016/j.nicl.2012.08.002 · 2.53 Impact Factor
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    • "Executive function is affected by TBI, independently of the presence/absence of cortical lesions (Metting et al., 2007; Sanchez-Carrion et al., 2008). It has been suggested that it is diffuse axonal injury – commonly observed after TBI – that disrupts integrative networks and that is the main cause of executive functions impairment following TBI (Ghajar and Ivry, 2008; Niogi et al., 2008). "
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