HealthManagement, Volume 15 - Issue 2, 2015

Big Data Initiatives to Support Next Generation Neuroimaging of TBI

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Big Data Initiatives to Support Next Generation Neuroimaging of Traumatic Brain Injury (TBI)

American College of Radiology Head Injury Institute

Traumatic Brain Injury (TBI) is a major societal issue that has recently captured a progressively greater degree of public attention. TBI occurs across a spectrum of mild to severe disease with outcomes that range from transient symptoms to lifelong disability or death. The initial clinical presentation of TBI does not necessarily clearly predict outcome in all cases. As such there is an increased sense of urgency in defining the role of imaging in the management of this disease process. Realising this trend, in May 2013 the American College of Radiology (ACR) launched the Head Injury Institute (HII) to help drive progress and demonstrate leadership in head injury imaging. The HII works with TBI researchers and caregivers, leveraging and combining both science and practice information to create a shareable message for practising radiologists. The objective is to improve patient care as a broad and practical translational effort. To achieve these goals, the HII focuses on three domains:
  1. Standardisation: this includes standardising terminology and descriptions applied to TBI imaging, in addition to standardised clinical and scientific imaging protocols (Maas 2011);
  2. Knowlege sharing: this includes creating and managing information exchanges involving not only scientists but also clinicians, and
  3. Research to practice: facilitating and organising efforts to move research initiatives into the practice setting.
A key focus of the ACR HII is to address barriers that limit the role of imaging in patients with mild traumatic brain injury (mTBI). The vast majority of patients with head injury experience the mild form of this condition (mTBI). Though many mTBIs may be transient in nature with complete resolution of symptoms, this condition may also be associated with lifelong morbidity that includes difficulties maintaining employment and interpersonal relationships, particularly in sports or military populations with an increased environmental risk for subsequent exposures. MTBI in its uncomplicated form is defined by the lack of any observable imaging abnormalities using current clinical techniques. Although neuroimaging is of great utility in identifying moderate to severe injuries that may require acute neurosurgical or intensive care management (Kim 2011), imaging has a very limited role in helping elucidate subtle diagnostic features that may help to predict prognosis on the milder spectrum of this disease process.


Recent population-based research studies have demonstrated that advanced neuroimaging techniques may be capable of identifying the structural and functional changes that are known to occur in mTBI (Eierud 2014). These advanced techniques may include functional connectivity magnetic resonance imaging (MRI) (McDonald 2012), spectroscopy (Yeo 2011), white matter-sensitive techniques such as diffusion tensor imaging (DTI) or diffusion spectrum imaging (DSI) (Shenton 2012; grossman 2010), cortical volumetric techniques (Wang 2014), and /or positron emission tomography (PET) molecular imaging using TBI-specific ligands (Ramlackhansingh 2011).
These advanced neuroimaging techniques are presently used to detect statistically meaningful differences in scientific exploration, but are of limited use in helping to inform the real-time clinical management of patients with TBI. Although many of these techniques can be acquired using current clinical scanners, the abnormal signal reflective of mTBI is often diffuse and difficult, if not impossible, for a neuroradiologist to qualitatively describe. Discernible changes from normal on imaging studies often can only be revealed using quantitative, computer assisted techniques. However, validated quantitative diagnostic tools to interpret advanced neuroimaging techniques and achieve a diagnosis of mTBI within a single patient do not exist. In many cases, the lack of ‘normal’ comparison values that are readily measurable on clinical scanners further impedes the implementation of advanced imaging tools.
The ACR HII recognises that the future of neuroimaging for mTBI will likely require some combination of computer-aided diagnosis (CAD) and qualitative interpretation by a practising radiologist. Therefore the HII is actively involved with helping to bridge the gap between the prevailing clinical neuroimaging of TBI and the
disruptive potential of advanced neuroimaging for the management of this disease condition. Through multiple ACR-supported conferences that have assembled many of the thought leaders and policymakers in the neuroimaging of TBI, a pathway forward has been defined which includes:
(i) Collection and aggregation of large-scale data from TBI clinical trials and from age-stratified normal controls employing standardised techniques and standardisation between imaging equipment;
(ii) Identification of clear imaging features that are diagnostic for TBI and demonstrate value in assessing injury severity and outcomes, and
(iii) Development of practical imagebased tools and the requisite statistical framework to determine a diagnosis and prognosis for individual patients with TBI. This final goal may include the use of such contemporary tools as machine-learning algorithms that can learn from large volumes of patient data where such knowledge is encapsulated in statistical classification/ regression models, which can then be used for TBI prediction in individual patients (see Figure 1).