Abstract

Structural segmentation of T1-weighted (T1w) MRI has shown morphometric differences, both compared to controls and longitudinally, following a traumatic brain injury (TBI). While many patients with TBI present with abnormalities on structural MRI images, most neuroimaging software packages have not been systematically evaluated for accuracy in the presence of these pathology-related MRI abnormalities. The current study aimed to assess whether acute MRI lesions (MRI acquired 7–71 days post-injury) cause error in the estimates of brain volume produced by the semi-automated segmentation tool, Freesurfer. More specifically, to investigate whether this error was global, the presence of lesion-induced error in the contralesional hemisphere, where no abnormal signal was present, was measured. A dataset of 176 simulated lesion cases was generated using actual lesions from 16 pediatric TBI (pTBI) cases recruited from the emergency department and 11 typically-developing controls. Simulated lesion cases were compared to the “ground truth” of the non-lesion control-case T1w images. Using linear mixed-effects models, results showed that hemispheric measures of cortex volume were significantly lower in the contralesional-hemisphere compared to the ground truth. Interestingly, however, cortex volume (and cerebral white matter volume) were not significantly different in the lesioned hemisphere. However, percent volume difference (PVD) between the simulated lesion and ground truth showed that the magnitude of difference of cortex volume in the contralesional-hemisphere (mean PVD = 0.37%) was significantly smaller than that in the lesioned hemisphere (mean PVD = 0.47%), suggesting a small, but systematic lesion-induced error. Lesion characteristics that could explain variance in the PVD for each hemisphere were investigated. Taken together, these results suggest that the lesion-induced error caused by simulated lesions was not focal, but globally distributed. Previous post-processing approaches to adjust for lesions in structural analyses address the focal region where the lesion was located however, our results suggest that focal correction approaches are insufficient for the global error in morphometric measures of the injured brain.

Highlights

  • Automated analysis to derive quantitative measures of brain structure offers significant benefit to large scale research endeavors that have clinical translation potential

  • Frank parenchymal lesions as a result of pediatric TBI (pTBI) pathology result in surface reconstruction errors due to abnormal magnetic resonance images (MRI) features, such as distortions to the voxel-intensity (Merkley et al, 2008; Irimia et al, 2012; Goh et al, 2014)

  • The current study investigated the accuracy of surface-based, morphometric measurement from T1w images containing traumatic brain injury (TBI)-lesions, using a pediatric cohort of simulated lesions and their base control images as a reference

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Summary

Introduction

Automated analysis to derive quantitative measures of brain structure offers significant benefit to large scale research endeavors that have clinical translation potential. Recent traumatic brain injury (TBI) research has utilized segmentation and analysis of T1-weighted (T1w) structural magnetic resonance images (MRI) to quantify the postinjury morphometric changes [Dennis et al, 2017; Ryan et al, 2017; Urban et al, 2017, see King et al (2019) for a review]. The accuracy of automated methods in the context of gross lesions/pathology, may be reduced by errors introduced during the processing of such MRI This makes it difficult to ascertain whether differences between control and patient morphology are due to an injury-related pathology or due to systematic error which is specific to the patient cases with gross lesions (King et al, 2019)

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