Comparison of Acute and Chronic Traumatic Brain Injury Using Semi-Automatic Multimodal Segmentation of MR Volumes

Abstract

Although neuroimaging is essential for prompt and proper management of traumatic brain injury (TBI), there is a regrettable and acute lack of robust methods for the visualization and assessment of TBI pathophysiology, especially for of the purpose of improving clinical outcome metrics. Until now, the application of automatic segmentation algorithms to TBI in a clinical setting has remained an elusive goal because existing methods have, for the most part, been insufficiently robust to faithfully capture TBI-related changes in brain anatomy. This article introduces and illustrates the combined use of multimodal TBI segmentation and time point comparison using 3D Slicer, a widely-used software environment whose TBI data processing solutions are openly available. For three representative TBI cases, semi-automatic tissue classification and 3D model generation are performed to perform intra-patient time point comparison of TBI using multimodal volumetrics and clinical atrophy measures. Identification and quantitative assessment of extra-and intra-cortical bleeding, lesions, edema, and diffuse axonal injury are demonstrated. The proposed tools allow cross-correlation of multimodal metrics from structural imaging (e.g., structural volume, atrophy measurements) with clinical outcome variables and other potential factors predictive of recovery. In addition, the workflows described are suitable for TBI clinical practice and patient monitoring, particularly for assessing damage extent and for the measurement of neuroanatomical change over time. With knowledge of general location, extent, and degree of change, such metrics can be associated with clinical measures and subsequently used to suggest viable treatment options.

Robust whole-brain segmentation: Application to traumatic brain injury

“…For example, in Bendlin et al (2008) brain volume loss following TBI has been identified using tissue segmentation techniques on structural MR images (MRIs) and diffusion tensor imaging (DTI). In Irimia et al (2011) an intra-patient time point comparison has been performed on three representative TBI patients using semi-automatic methods for tissue and lesion classification and 3D model generation. Ramlackhansingh et al (2011) used structural MRI and positron emission tomography (PET) to demonstrate inflammatory processes that remain active for months or years following brain trauma.…”

Patient-tailored multimodal neuroimaging, visualization and quantification of human intra-cerebral hemorrhage

“…The quantitative measures which are extracted include metrics of cortical atrophy such as the bifrontal index, the bicaudate index, Evan's index, the ventricular index, and Huckman's index 3 . The bifrontal index is the ratio of the maximum width of the anterior horns of the lateral ventricles (HLV) to the inner skull diameter at HLV level.…”

Integration of Multimodal Neuroimaging and Electroencephalography for the Study of Acute Epileptiform Activity After Traumatic Brain Injury

“…1). The details of the procedure for pathology identification are detailed elsewhere [4]. FreeSurfer (freesurfer.net) is utilized to segment healthy-appearing white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) from T 1 -weighted volumes, as well as to perform regional parcellation [5,6].…”

“…TBI-related lesions are segmented from GRE/SWI/FLAIR volumes as outlined elsewhere [10,11], the scalp is segmented from T 1 -weighted MRI, and hard bone is segmented from CT volumes. Eyes, muscle, cartilage, mucus, nerves, teeth, and ventriculostomy shunts are labeled based on T 1 /T 2 MRI.…”