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Abstract #1618

Cortical Bone Segmentation for Accurate Canine Body Composition Quantification Using 3 Tesla Fat-Water MRI

Aliya Gifford1, 2, Joel Kullberg3, Johan Berglund3, Filip Malmberg4, Katie C. Coate5, Philip E. Williams5, Alan D. Cherrington5, Malcolm J. Avison6, 7, E. Brian Welch6, 8

1Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States; 2Chemical and Physical Biology Program, Vanderbilt University School of Medicine, Nashville, TN, United States; 3Department of Radiology, Uppsala University, Uppsala, Sweden; 4Center for Image Analysis, Uppsala University, Uppsala, Sweden; 5Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, United States; 6Vanderbilt University Institute of Imaging Science, Vanderbilt University School of Medicine, Nashville, TN, United States; 7Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN, United States; 8Department of Radiology & Radiological Sciences, Vanderbilt University School of Medicine, Nashville, TN, United States

This work uses fat-water MRI for whole-body composition quantification in dogs to identify cortical bone (CB) voxels as separate tissue depots in the estimation of total body mass. Volumes of total body adipose tissue, lean tissue, and cortical bone were quantified using a semi-automated approach in six dogs. Tissue volumes were calculated as the volume of all voxels in the segmented class, and converted into masses using published tissue densities. The coefficient of variation between test-retest scans for CB was 3.08%. Inclusion of CB improved concordance between MRI-estimated static mass and mass change over time compared with scale mass measurements.

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Keywords

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