Optimizing Automated Brain Extraction for Moderate to Severe Traumatic Brain Injury Patients: The Role of Intensity Normalization and Bias-Field Correction

Patrick Carbone; Celina Alba; Alexis Bennett; Kseniia Kriukova; Dominique Duncan

doi:10.3390/a17070281

Algorithms (Jun 2024)

Optimizing Automated Brain Extraction for Moderate to Severe Traumatic Brain Injury Patients: The Role of Intensity Normalization and Bias-Field Correction

Patrick Carbone,
Celina Alba,
Alexis Bennett,
Kseniia Kriukova,
Dominique Duncan

Affiliations

Patrick Carbone: USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
Celina Alba: USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
Alexis Bennett: USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
Kseniia Kriukova: USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
Dominique Duncan: USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA

DOI: https://doi.org/10.3390/a17070281
Journal volume & issue: Vol. 17, no. 7
p. 281

Abstract

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Accurate brain extraction is crucial for the validity of MRI analyses, particularly in the context of traumatic brain injury (TBI), where conventional automated methods frequently fall short. This study investigates the interplay between intensity normalization, bias-field correction (also called intensity inhomogeneity correction), and automated brain extraction in MRIs of individuals with TBI. We analyzed 125 T1-weighted Magnetization-Prepared Rapid Gradient-Echo (T1-MPRAGE) and 72 T2-weighted Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRI sequences from a cohort of 143 patients with moderate to severe TBI. Our study combined 14 different intensity processing procedures, each using a configuration of N3 inhomogeneity correction, Z-score normalization, KDE-based normalization, or WhiteStripe intensity normalization, with 10 different configurations of the Brain Extraction Tool (BET) and the Optimized Brain Extraction Tool (optiBET). Our results demonstrate that optiBET with N3 inhomogeneity correction produces the most accurate brain extractions, specifically with one iteration of N3 for T1-MPRAGE and four iterations for T2-FLAIR, and pipelines incorporating N3 inhomogeneity correction significantly improved the accuracy of BET as well. Conversely, intensity normalization demonstrated a complex relationship with brain extraction, with effects varying by the normalization algorithm and BET parameter configuration combination. This study elucidates the interactions between intensity processing and the accuracy of brain extraction. Understanding these relationships is essential to the effective and efficient preprocessing of TBI MRI data, laying the groundwork for the development of robust preprocessing pipelines optimized for multi-site TBI MRI data.

Published in Algorithms

ISSN: 1999-4893 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Technology: Technology (General): Industrial engineering. Management engineering; Science: Mathematics: Instruments and machines: Electronic computers. Computer science
Website: https://www.mdpi.com/journal/algorithms

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