Traumatic brain injuries are a leading cause of death and disability, affecting approximately 27 million individuals worldwide every year (Huang et al., 2024). Neuroimaging is critical to support the accurate diagnosis and clinical management of traumatic brain injuries (Douglas et al., 2015). For example, the failure to use neuroimaging to support the diagnosis of a traumatic brain injury can inadvertently prevent clinicians from ruling out differential diagnoses such as epidural haematomas, which require prompt neuro-surgical intervention.

The most common type of neuroimaging used to assess traumatic brain injuries are computed tomography (CT) and magnetic resonance imaging (MRI). CT scans can detect fractures and gross injuries requiring surgical intervention, such as subdural hematomas (Shenton et al., 2012). By contrast, MRI scans support the visualisation of grey and white matter structures in the brain, supporting the detection of subacute haemorrhages as well as macroscopic white matter injuries (Shenton et al., 2012). MRI scans using sequences designed to accentuate magnetic susceptibility signal loss (e.g. Susceptibility Weighted Imaging, SWI) is particularly sensitive to focal brain haemorrhage in trauma.

Whilst MRI scans provide a better resolution compared with CT scans, both neuroimaging methods lack the sensitivity to detect structural changes in the brain following mild traumatic brain injuries because they are unable to detect mild diffuse axonal injuries or traumatic axonal injuries (Shenton et al., 2012). In a cohort study including 991 individuals who presented to emergency services with a suspected traumatic brain injury, Madhok and colleagues (2022) found that 56% of individuals who had negative CT scans continued to show evidence of impairment in a 6-month follow-up. These findings highlight the need for neuroimaging methods with a greater level of sensitivity to detect neural markers of traumatic brain injury, as well as methods that can reliably predict the prognosis of the patient following a traumatic brain injury.

Diffusion tensor imaging, a sequence that can be used within an MRI scan, is sensitive in detecting microscopic changes in white matter integrity (Le Bihan et al., 2001). Diffusion tensor imaging measures the movement of water molecules to highlight any disruption to the ordered microstructural anatomy of white matter tracts in the brain (Le Bihan et al., 2001). Diffusion tensor imaging can help to differentiate individuals who have experienced traumatic brain injuries from healthy volunteers, irrespective of the severity of the injury (Hulkover et al., 2013). These findings suggest that diffusion tensor imaging may be a useful tool to investigate mild traumatic brain injuries.

Traumatic brain injuries are associated with alterations in white matter tract integrity, as indexed by metrics of mean diffusivity and fractional anisotropy (Douglas et al., 2018). However, the brain regions affected are highly variable since the location and severity of brain injuries vary considerably between patients (Douglas et al., 2018). Changes in composite fractional anisotropy, a metric of white matter integrity, have been associated with impaired cognitive functioning as well as global clinical outcomes post-injury (Levin et al., 2008). These findings highlight that diffusion tensor imaging may be a valuable tool for predicting the prognosis of the individuals who have experienced traumatic brain injuries. Since this method can distinguish individuals with traumatic brain injuries irrespective of the severity of the injury (Hulkover et al., 2013), this method may be useful to support clinicians in determining the diagnosis and the likely prognosis of individuals who have experienced mild traumatic brain injuries.

However, there are limitations to the clinical application of DTI due to the variability in design of published studies and due to a lack of standardisation in image acquisition parameters and image post processing techniques. While the lack of standardisation negatively impacts the direct comparison between different studies, it is possible to control for these confounds when pooling data that was acquired on different scanners or when pooling data that was analysed using different methods (van Velkinburgh et al., 2023).

The permissibility of DTI evidence in court proceedings has been widely debated (van Velkinburgh et al., 2023). It should be noted that DTI evidence alone is not sufficient to support the diagnosis of a traumatic brain injury (van Velkinburgh et al., 2023). Instead, certain patterns of white matter damage could inform the probability of whether an injury is present. In a review of all documented trial court decisions to admit or deny the motion to admit DTI evidence, 69 of 72 cases admitted DTI evidence (van Velkinburgh et al., 2023).

The Food and Drug administration (2023) recently approved the use of Advanced Neuro Diagnostic Imaging (ANDI), a software that can measure the microstructural properties of white matter for use in clinical settings. The software, developed by IMEKA, is intended to support the image processing of diffusion-weighted MRI sequences into 3D maps of white matter tracts in clinical settings. The software is currently only available within the United States of America. However, it should be noted that no clinical studies were conducted to support the approval of the product. Furthermore, correspondence from FDA (Food and Drug Administration, 2023) states that this tool cannot be used as a diagnostic aid; and that the software cannot be used to support the prediction, diagnosis or interpretation of brain health.

Further research is needed to develop clinical tools that can reliably support clinicians in diagnosing mild traumatic brain injuries using validated diagnostic tools. Whilst diffusion imaging may lend weight to a TBI diagnosis, it is unlikely to considered as a stand-alone neuroimaging biomarker for TBI.

References

Douglas D.B., Iv M., Douglas P.K., Anderson A., Vos S.B., Bammer R., Zeineh M., Wintermark M. (2015). Diffusion Tensor Imaging of TBI: Potentials and Challenges. Top Magn Reson Imaging; 24(5):241-51. 

Food and Drug Administration (2023). “510K Summary”. URL: https://www.accessdata.fda.gov/cdrh_docs/pdf23/K230913.pdf. Accessed: 10 February 2025.

Huang, X. F., Ma, S. F., Jiang, X. H., Song, R. J., Li, M., Zhang, J., … & Li, H. (2024). Causes and global, regional, and national burdens of traumatic brain injury from 1990 to 2019. Chinese journal of traumatology. 27(6): 311-322.

Hulkower, M.B., Poliak, D.B., Rosenbaum, S.B., Zimmerman, M.E., Lipton, M.L. (2013). A decade of DTI in traumatic brain injury: 10 years and 100 articles later. AJNR Am J Neuroradiol; 34(11):2064-74. 

Le Bihan, D., Mangin, J. F., Poupon, C., Clark, C. A., Pappata, S., Molko, N., & Chabriat, H. (2001). Diffusion tensor imaging: concepts and applications. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, 13(4): 534-546.

Madhok, D.Y., Rodriguez, R.M., Barber, J., et al. (2022). Outcomes in patients with mild traumatic brain injury without acute intracranial traumatic injury. JAMA Network Open; 5(8), e2223245.

Shenton M.E., Hamoda H.M., Schneiderman J.S., Bouix S., Pasternak O., Rathi Y., Vu M.A., Purohit M.P., Helmer K., Koerte I., Lin A.P., Westin C.F., Kikinis R., Kubicki M., Stern R.A., Zafonte R. (2012). A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury. Brain Imaging Beha; 6(2):137-92. 

Van Velkinburgh JC, Herbst MD, Casper SM. (2023). Diffusion tensor imaging in the courtroom: Distinction between scientific specificity and legally admissible evidence. World J Clin Cases. 6;11(19):4477-4497.