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

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

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

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

A Review on Detection of Traumatic brain Injury using Visual-Contextual model in MRI Images

Recently, there are various computational methods to analyze the traumatic brain injury (TBI) from magnetic resonance imaging (MRI).The detection of brain injury is very difficult task in the medical science. There are various soft techniques for the detection of the patch of brain injury on the basis of MRI image contents. This paper gives brief analysis about the different methods to determine the normal and abnormal tissues of the brain

Detection of Brain Injury Using Different Soft Computing Techniques: A Survey

The detection of brain injury is one of the important and difficult task in the field of medicine. If the brain injuries are not detected in time, then it can cause serious problems in patients and sometimes can even lead to death. Traumatic brain injury (TBI) is one of the major causes of mortality and poor quality of life among the survivors. Various imaging techniques are available for taking the images of the brain so that the injuries can be detected. Magnetic resonance imaging (MRI) is one of the common medical imaging technique used for the delineation of soft tissues such as that of the brain. This paper analyses few of the methods and their performances that have been proposed for the detection of the brain injury. In these methods different soft computing techniques such as artificial neural networks (ANN), k nearest neighbor (k-NN), support vector machine (SVM), Parzan window, etc. were used for the classification of abnormal and normal brain images. Before classification feature extraction and reduction were done using the methods such as DWT, GLCM, PCA, etc. DOI: 10.17762/ijritcc2321-8169.15030

Individualised profiling of white matter organisation in moderate-to-severe traumatic brain injury patients

Background and purpose Approximately 65% of moderate-to-severe traumatic brain injury (m-sTBI) patients present with poor long-term behavioural outcomes, which can significantly impair activities of daily living. Numerous diffusion-weighted MRI studies have linked these poor outcomes to decreased white matter integrity of several commissural tracts, association fibres and projection fibres in the brain. However, most studies have focused on group-based analyses, which are unable to deal with the substantial between-patient heterogeneity in m-sTBI. As a result, there is increasing interest and need in conducting individualised neuroimaging analyses. Materials and methods Here, we generated a detailed subject-specific characterisation of microstructural organisation of white matter tracts in 5 chronic patients with m-sTBI (29 – 49y, 2 females), presented as a proof-of-concept. We developed an imaging analysis framework using fixel-based analysis and TractLearn to determine whether the values of fibre density of white matter tracts at the individual patient level deviate from the healthy control group (n = 12, 8F, Mage = 35.7y, age range 25 – 64y). Results Our individualised analysis revealed unique white matter profiles, confirming the heterogenous nature of m-sTBI and the need of individualised profiles to properly characterise the extent of injury. Future studies incorporating clinical data, as well as utilising larger reference samples and examining the test–retest reliability of the fixel-wise metrics are warranted. Conclusions Individualised profiles may assist clinicians in tracking recovery and planning personalised training programs for chronic m-sTBI patients, which is necessary to achieve optimal behavioural outcomes and improved quality of life

How Valuable are Clinical Neuropsychological Assessments? A Meta-analysis of Neuropsychological Tests with Comparison to Common Medical Tests and Treatments

There has been a general decrease in neuropsychological assessments at a time when medical diagnostic technology and treatments have expanded, leading to a faulty assumption that medical tests and healthcare treatments provide more reliable or valid data than psychological assessments. A landmark report from the American Psychological Association’s (APA) Psychological Assessment Work Group (PAWG) found that validity coefficients for many psychological tests were indistinguishable from those of medical tests (Meyer et al., 2001). An updated systematic review of the advancement in neuropsychological testing is essential to the continued advancement of the value of neuropsychological assessment in healthcare. This meta-analysis sought to (1) summarize effect sizes of neuroimaging to diagnose dementia, medications to treat chronic diseases, and neuropsychological tests to diagnose dementia and TBI, (2) determine the differences (if any) in effect sizes between medical domains, and (3) determine the differences (if any) in effect sizes between medical domains and neuropsychological tests. EBSCO networks were searched for original research examining the efficacy of neuroimaging for Alzheimer’s Disease (AD), xi neuropsychological tests for AD and traumatic brain injury (TBI), and medication to treat memory impairment and cardiovascular events between clinical and control samples. Studies were coded using a complex multi-comparison, outcome, and subgroup schema. Data were analyzed under random-effects modeling. Of 6,668 studies identified, 78 were retained for primary and ancillary meta-analyses (715 effect sizes extracted; 35,810 clinical and 42,964 control participants represented). Primary results indicated a significant difference between domains, such that neuroimaging (g = -1.603) and neuropsychological tests (g = -1.591) both yielded greater effect sizes than medication studies (g = -0.009]. Secondary results indicated the AD neuropsychological test effect size [g = -2.213) was significantly different than the TBI neuropsychological test efficacy [g = -0.649; Q(1) = 42.821, p = 0.000]. Additionally, results indicated nonsignificant effect sizes for both memory impairment medications (g = -.052) and aspirin for cardiovascular events (g = .017). CONCLUSIONS: The diagnostic efficacy of neuroimaging and neuropsychological tests were both substantial and non-significantly different from one another. These findings provide clinicians and consumers with convincing evidence that neuropsychological tests are a reliable diagnostic tool for people with acquired and neurodegenerative brain disorders

How Valuable are Clinical Neuropsychological Assessments? A Meta-analysis of Neuropsychological Tests with Comparison to Common Medical Tests and Treatments

There has been a general decrease in neuropsychological assessments at a time when medical diagnostic technology and treatments have expanded, leading to a faulty assumption that medical tests and healthcare treatments provide more reliable or valid data than psychological assessments. A landmark report from the American Psychological Association’s (APA) Psychological Assessment Work Group (PAWG) found that validity coefficients for many psychological tests were indistinguishable from those of medical tests (Meyer et al., 2001). An updated systematic review of the advancement in neuropsychological testing is essential to the continued advancement of the value of neuropsychological assessment in healthcare. This meta-analysis sought to (1) summarize effect sizes of neuroimaging to diagnose dementia, medications to treat chronic diseases, and neuropsychological tests to diagnose dementia and TBI, (2) determine the differences (if any) in effect sizes between medical domains, and (3) determine the differences (if any) in effect sizes between medical domains and neuropsychological tests. EBSCO networks were searched for original research examining the efficacy of neuroimaging for Alzheimer’s Disease (AD),neuropsychological tests for AD and traumatic brain injury (TBI), and medication to treat memory impairment and cardiovascular events between clinical and control samples. Studies were coded using a complex multi-comparison, outcome, and subgroup schema. Data were analyzed under random-effects modeling. Of 6,668 studies identified, 78 were retained for primary and ancillary meta-analyses (715 effect sizes extracted; 35,810 clinical and 42,964 control participants represented). Primary results indicated a significant difference between domains, such that neuroimaging (g = -1.603) and neuropsychological tests (g = -1.591) both yielded greater effect sizes than medication studies (g = -0.009]. Secondary results indicated the AD neuropsychological test effect size [g = -2.213) was significantly different than the TBI neuropsychological test efficacy [g = -0.649; Q(1) = 42.821, p = 0.000]. Additionally, results indicated nonsignificant effect sizes for both memory impairment medications (g = -.052) and aspirin for cardiovascular events (g = .017). CONCLUSIONS: The diagnostic efficacy of neuroimaging and neuropsychological tests were both substantial and non-significantly different from one another. These findings provide clinicians and consumers with convincing evidence that neuropsychological tests are a reliable diagnostic tool for people with acquired and neurodegenerative brain disorders