@inproceedings{e49b339f9c5a4e44837205bd2d8a458c,
title = "ANTs, BET, or...neither? An exploration of brain masking and machine learning tools applied to magnetic resonance elastography",
abstract = "Magnetic resonance elastography is a quantitative MRI modality that can aid in diagnosis of disease by detecting altered tissue mechanical properties. While brain masking tools exist for common MRI sequences, such as T1-weighted and T2-weighted imaging, there is no reliable masking tool for MRE. In this research, our innovation involves applying machine learning methods to a problem where no existing tools exist within the MRE research space. The demonstrated machine learning model shows potential for improvement in masking out distorted regions in brain elastography when compared to current non-machine learning masking methods not meant for MRE. This tool will enable automated and reproducible MRE results for neuroimaging applications.",
keywords = "Advanced Normalization Tools (ANTs), Brain Extraction Tool (BET), Machine learning, Magnetic resonance elastography (MRE), TorchIO",
author = "Squire, {John D.} and Anderson, {Aaron T.} and Johnson, {Curtis L.} and Brad Sutton",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 ; Conference date: 15-07-2024 Through 19-07-2024",
year = "2024",
doi = "10.1109/EMBC53108.2024.10781675",
language = "English (US)",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings",
address = "United States",
}