Skip to main content
eScholarship
Open Access Publications from the University of California

Scholarly Works (4 results)

  • Thesis
  • Peer Reviewed
UC Riverside Electronic Theses and Dissertations (2014)

Awareness of mild traumatic brain injury and its potential long term effects has increased dramatically in recent years. 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. In manual estimation a train operator must spend a copious amount of time locating and delineating the lesions. We have developed three innovations to aid in the analysis of mTBI by automatically detecting lesion in MR images. (1) A high level Bayesian network based contextual model, which is able to estimate the progression of lesion over time given basic information about the subject. The generative contextual model is fused with a discriminative visual model that estimates the lesion using 3D features extracted from T2maps. (2) Low level dynamic and static contextual features are developed that take advantage of sequential imaging to improve detection as an iterative post processing step. After the initial estimate by a discriminative classifier, the context features are estimated from the posterior marginal to describe spatio-temporal information about the lesion to the classifier. (3) Multi-Modal features are developed to further improve the visual model. A temporal generative tissue model is developed to model tissue type probabilities within the brain over the course of the injury in the T2map modality. Maximum a posterior edge features are developed to take advantage of the susceptibility weighted imaging (SWI) channel. All of the proposed methods have been evaluated on a temporal mTBI dataset with manually evaluated ground truth and have been shown to outperform the current state-of-the-art approaches.

    Cover page: Computational Methods for Mild Traumatic Brain Injury
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2015)
    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.
      Cover page: Dynamic Low-Level Context for the Detection of Mild Traumatic Brain Injury
      • Article
      • Peer Reviewed
      UC Irvine Previously Published Works (2014)
      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.
        Cover page: Visual and Contextual Modeling for the Detection of Repeated Mild Traumatic Brain Injury
        • Article
        • Peer Reviewed
        UC Irvine Previously Published Works (2012)
        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.
          Cover page: Computational analysis reveals increased blood deposition following repeated mild traumatic brain injury
          Top