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Abstract #4826

BIASS: Benchmarking the Impact of Anatomical Segmentation in Spectroscopy

Jessica Archibald1, Kay Chioma Igwe2, Antonia Kaiser3, Jaimie J Lee4, John L.K. Kramer4, Alexandra Zimmerman5, Aaron Gudmundson6,7, Helge J Zöllner6,7, Candace C. Fleischer8,9, Georg Oeltzschner6,7, Jamie Near10, and Mark Mikkelsen11
1Department of Radiology, Weill Cornell Medicine, New York, NY, United States, 2Department of Biomedical Engineering, Columbia University School of Engineering and Applied Science, New York, NY, United States, 3CIBM Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 4Department of Anesthesiology, Pharmacology and Therapeutics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada, 5BrainSpec, Inc, Boston, MA, United States, 6F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, 7The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 8Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, United States, 9Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States, 10Sunnybrook Research Institute and University of Toronto, Toronto, ON, Canada, 11Radiology, Weill Cornell Medicine, New York, NY, United States

Synopsis

Keywords: Segmentation, Spectroscopy

Motivation: Water-scaled metabolite estimates in brain magnetic resonance spectroscopy (MRS) usually require corrections accounting for differences in water content and water and metabolite relaxation behavior gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF).

Goal(s): This study aims to determine how strongly commonly used segmentation tools differ in determining partial volumes from a typical MRS single-voxel volume.

Approach: By analyzing GM, WM, and CSF fractions obtained from segmentation tools (e.g., FSL, ANTs, and SPM) and comparing them to manual segmentation as "ground truth".

Results: Significant variability in tissue fraction estimates across segmentation methods for different brain regions, with CSF showing the highest inconsistencies.

Impact: This study demonstrates that segmentation tools obtain different tissue volume fraction estimates in a typical MRS voxel. These findings help researchers and clinicians understand the variability that the choice of segmentation algorithm contributes to the uncertainty of water-scaled concentration estimates.

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Abstract

Keywords